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The important role of software engineering in industry

Tue, 09 Jun 2026 00:00:00 +1000

The last decade or so has seen a sharp increase in the demands placed on industrial control systems. This is due in part to the higher levels of automation needed to make products efficiently; but industry is also having to fulfil ever greater and more diverse customer requests.

These increases in demands have led to what seems an exponential rise in the complexity of programs used by industrial control systems — a trend that seems certain to continue well into the future. The overall value of a control system has moved away from its hardware, and is now heavily weighted towards its software, which contains the intellectual property (IP) of the overall system.

Software development therefore needs to be carefully controlled to prevent programs becoming unwieldly, difficult to maintain and highly time‑consuming to fault‑find. If not kept in check, program management will become ever more expensive and burdensome for industry.

Fortunately, more attention is being paid to the software engineering practices used by industry. This will become ever more important as companies seek to digitise their operations. It will also help avoid cost blow‑outs for software projects.

The value added by PLCopen

Industry has long recognised the trend towards ever-greater complexity and the ramifications it has for programming. PLCopen is one organisation specifically established to help in this process. Its stated aim is to enhance the quality of industrial software and thereby improve the overall efficiency of industry.

Being vendor neutral, PLCopen seeks to collaborate with as many vendors as possible, across a wide range of industries. It creates vendor working groups with the goal of creating standards for industry. The process is long, with many requests for comments (RFCs) received from vendors, but the final product shows the benefit of such collective input.

In the early days, PLCopen promoted the use of the IEC 61131‑3 PLC programming standard. Since then, it has more recently branched out into producing standards for other aspects of industrial control such as motion, safety, communications (mainly OPC UA) and data exchange. It also provides training and certification services for vendors to publicise their level of compliance to these standards.

The use of such standards has undoubtedly increased the quality of software used in industry; it has also helped programmers become more efficient in their automation projects.

The traditional model for program development

For many years, industrial programmers have had to purchase a development package for their controllers and install it onto their local computer. This computer was usually located close to the controller so that a physical connection could be made to it.

Creating programs in this way almost always restricted program development to a single user, working on a solitary computer. It also meant the programmer was solely responsible for procuring and maintaining the computer hardware, as well as generating backups of the program. They were responsible for installing updates (to both the operating system and development package) and for ensuring the integrity of the system by protecting against malware.

Much effort was required of this one programmer — in areas they were not necessarily expert in. Nevertheless, this model has worked reasonably well over many years and continues to be used today. But apart from the extra demands placed on the programmer, the one aspect that’s becoming increasingly important is the ability to support multiple programmers on the same project. Due to the growing complexity of programs and the sheer amount of code that needs to be written and tested, projects are increasingly drawing on expertise from different areas, necessitating collaboration from numerous programmers.

The future of software engineering for industry

It’s become clear that to successfully navigate the larger, more complex programs required of modern applications, the programming practices used by industry will need to be modernised.

To improve programming efficiency, industry should really adopt more of the methodology and techniques commonly used in mainstream IT. For example, Visual Studio is a popular development environment used by IT: it is very adept at handling large programs and supports some very useful facilities that make programming manageable and easy to fault‑find.

The first PLCs were designed to replace hard‑wired relay circuits, and thus only supported the ladder diagram programming language. While it is still the preferred language for solving Boolean logic type problems, its overuse will cause a phenomenon called ‘code bloat’, where PLC programs are far longer and more complicated than they need to be. However, the use of structured text language will prevent programs from becoming overly convoluted, particularly when an application requires heavy mathematical calculations.

Object‑oriented programming (OOP) is another example of a programming paradigm that’s been available for decades to the IT community but is still not supported by most industrial vendors. This is despite OOP’s inclusion into the third edition of the IEC 61131‑3 programming standard in 2013.

The way OOP organises programs around objects with data and code is radically different from the logic and functions approach of traditional industrial programming. Certain applications lend themselves very well to OOP and its use greatly simplifies programming. Moreover, without OOP, these applications would become far less efficient and more difficult to manage.

Cloud‑based software engineering — the next frontier

One of the more recent innovations in software engineering has been the adoption of cloud‑based services. The cloud, with its virtually unlimited storage capacity, very scalable infrastructure and large array of facilities, has become highly attractive to businesses. It’s also being used extensively by IT departments.

Automation suppliers have similarly been integrating the cloud into industry and using it in areas like IoT for ‘big data’ collection. Using existing internet infrastructure, the cloud can similarly add value to industrial programming environments and can add features that would otherwise be very difficult to implement.

So what exactly is cloud‑based engineering and how can it be applied? More importantly, how can it help us improve what we do?

What is cloud‑based engineering?

With cloud-based engineering, the whole development suite — together with all its tools — resides not on a local computer but in the cloud. The entire programming environment is called a ‘virtual machine’ because, while it exists in cyberspace, as far as the programmer is concerned it runs as if it’s installed on the developer’s local machine.

Every controller is given its own instance of a virtual machine image in the cloud. Programmers need only login to a portal via a standard web browser on a local device to access it; no special software needs to be loaded. The physical industrial controller on the factory floor is also tunnelled to the cloud via a secure transport channel and linked to the virtual machine to enable program deployment.

One advantage of using the cloud in this way is that it provides true platform independence. Programmers aren’t restricted to just desktop computers: they can use tablets, mobile phones or any other internet‑enabled device. The internet affords complete location independence, meaning programmers and controllers can be anywhere in the world. This alleviates the need for programmers to be physically present onsite.

The advantages of cloud‑based software engineering

Perhaps the most significant advantage of cloud‑based engineering is the lifting of the burden on the programmer having to provide the computing resources. With the cloud, it’s the vendor who’s responsible for supplying all the computing hardware and software, as well as its maintenance and the installation of updates. File handling is also performed by the cloud provider; programmers do not need to be concerned with backups or malware.

Another advantage is the elimination of having separate software versions for different firmware editions. In the cloud, every firmware revision is supported, and each will have their own virtual machine. In this way, programming software always matches the controller’s hardware and firmware revision.

Version control (ie, the handling of different revisions of the same program) can be handled in the cloud by seamlessly integrating established systems such as Git. Git is arguably the most used versioning system and its branching and merging facilities are ideal for collaborative work between multiple programmers.

Security concerns

Cloud‑based engineering undoubtedly improves the productivity of the programming team. It does this by streamlining their work and allowing them to concentrate on their core task of programming, rather than peripheral issues such as file management.

However, security remains a significant concern for some users, who need to be highly protective of their IP, as it’s what gives them their market advantage. For this reason, some web‑based services are hosted locally, rather than in the cloud, where the location of the data is often unknown. While some cloud providers counter this by specifically guaranteeing the final location of the data by country, a hybrid cloud system remains a viable alternative. This is where the cloud is used for general data and services, and a locally‑hosted cloud stores the sensitive elements.

Cloud engineering offerings

Cloud‑based computing is essentially a software-as-a-service (SaaS) offering. Despite the advantages of SaaS, take up in the industrial space has heretofore been slow — the main reason being the lack of support from industrial vendors. This placed the onus of creating virtual machines in the cloud and connections to the hardware onto the programmer. These tasks are difficult to implement without expert knowledge.

To help overcome this roadblock, hardware vendors are starting to rent out cloud‑based engineering services to their programmers. They have created web‑based portals for users to login to, dashboards into virtual machines, and connection handles for real controller hardware. The portal can also provide tutorial support and guidance documentation.

Setting up a new cloud instance only takes a few minutes, with many of the functions automated. In future, vendors will be able to extract operational data through the same channels and can add value to their offerings by including integrated analytics, predictive maintenance and much more.

While still embryonic, offerings for cloud‑based services are starting to appear, albeit slowly. And with the integration of AI into programming, it seems certain the cloud will be increasingly used in industrial software engineering.

*Harry Mulder is the Principal Automation Engineer at Beckhoff Automation. He has been involved in industrial automation for over 30 years and is fascinated by how new innovations keep affecting the direction of the industry. He really enjoys the practical element of his job, where he has a chance to get his hands dirty!

Image credit: iStock.com/Jacob Wackerhausen

AI won’t restart your plant: Why practical skills matter more than ever

Fri, 17 Apr 2026 00:00:00 +1000

The scaremongering has reached a crescendo; with the assertion that AI tools will replace knowledge-based professionals, including engineers. I don’t deny that AI is becoming increasingly sophisticated — including the ubiquitous ChatGPT — but the reality is more nuanced.

To save time, engineering personnel are using AI to construct snippets of PLC code, make design suggestions, summarise manuals, generate ideas for loop tuning, and describe process optimisation. But when SCADA screens alert process operators to a plant spinning out of control, nobody calls a chatbot. They call the troubleshooting expert.

AI tools are based on probability, suggesting the next word in a sentence, for instance. A bit like Google on steroids. AI can find information efficiently and provide advice based on a given prompt. Despite presenting these results with confidence, even conviction, AI is deeply flawed. Users need to be hyper-vigilant; everything it produces needs a beady-eyed expert.

Theory — the feeding ground for AI — is tested by reality. Consider these briefly sketched scenarios. A pressure transmitter is installed ‘as per spec’, but the impulse lines are partially blocked. The status is ‘healthy’, but in wet gas a badly ranged DP transmitter kills turndown — low-flow DP vanishes and the numbers lie. The temperature appears normal, but the associated control valve is either faulty or the product entering the plant has changed grade. This is the world of instrumentation and automation professionals: a place where measurement is never just a number, and control is never just code.

AI can be a useful adviser — a ‘chum on the side’. What it can’t do is smell hot insulation, hear pump cavitation, spot the subtle change to the vibration in an actuator, or feel the increasing heat on a terminal strip that’s about to become tomorrow’s incident report. It cannot walk the line, check an instrument air filter, or link that ‘mystery fault’ with a washdown cycle and a poorly sealed junction box. It cannot spot a poorly trained or over-tired operator, and it is not responsible when an oversight becomes a trip, a spill, or a near-miss. Humans are.

In our industry troubleshooting is the career moat. AI can recite the theory of pressure, flow and temperature measurement, but it cannot mimic experience and diagnose failure modes under pressure — calmly, methodically and with discipline.

Consider two common scenarios where hard-earned experience is essential: Firstly, the ‘perfect’ PID loop that still hunts. The tuning is textbook, but the loop oscillates because the valve is sticky or the actuator is undersized. Or the PLC that lies by omission, an intermittent trip that disappears when you watch it. The culprit is often noise on inputs, earthing/shielding errors, or a vibrating 24 V rail.

Budding instrumentation professionals can be trained using EIT’s online learning platform — while working. The teaching and learning sessions are live and interactive, covering job-aligned modules and presented by real, grizzled instrumentation veterans (not AI-bots or even humans with PhDs). There are troubleshooting exercises using realistic scenarios, and assessments that reward diagnosis and decision-making, not memorisation.

Australia’s competitiveness depends on our ability to proficiently troubleshoot in the industries which underpin our economy: energy, mining, processing, oil and gas, food and beverage manufacturing. AI will be there as a sounding-board, but people and their skills build the national capability.

*Dr Steve Mackay has worked in engineering throughout Australia, Europe, Africa and North America for over 40 years in the mining, oil and gas, and power industries. A registered professional engineer in electrical, mechanical and chemical engineering, he believes university engineering programs need to be strongly focused on industry. He leads three online fully accredited engineering colleges with over 6000 students from over 160 countries attending a range of engineering certificate, diploma, bachelor degree and master's degree programs.

Top image credit: iStock.com/PhonlamaiPhoto

Calibration explained: principles, processes and modern reporting

Wed, 15 Apr 2026 00:00:00 +1000

Calibration is essential in industrial automation, ensuring that measurement instruments provide accurate readings. Today, IIoT platforms can simplify documentation, provide central access to calibration data, and enable efficient calibration planning.

What is calibration?

Calibration can be simply described as the process of comparing the measured value from an instrument under calibration with a reference standard of known and high accuracy. In essence, it establishes whether the instrument provides measurements within acceptable limits.

The International Bureau of Weights and Measures (BIPM) defines calibration as an “operation that, under specified conditions, in a first step, establishes a relation between the quantity values with measurement uncertainties provided by measurement standards and corresponding indications with associated measurement uncertainties (of the calibrated instrument or secondary standard) and, in a second step, uses this information to establish a relation for obtaining a measurement result from an indication”.

Performing calibration requires specific tools and instruments, which vary depending on the type of calibration. Common examples include calibrators with valid calibration certificates, standard devices, and calibration rigs.

Why is calibration important?

Calibration is essential to ensure accurate measurement. Measuring devices are installed in diverse industrial environments where they are exposed to challenges such as abrasion, vibration, sudden temperature changes, harsh conditions and mechanical shocks. These factors can affect device performance, making calibration necessary to verify accuracy and, if required, adjust the instrument to meet application specifications.

Accurate calibration positively impacts production processes by ensuring reliable measurements. It also reduces variation within technical specifications, supports preventive maintenance and guarantees measurement traceability.

Additionally, modern smart instruments can provide continuous health status information, offering a clearer picture of device condition and measurement reliability.

What do you need to know about the calibration certificate?

During calibration, all measurements must be recorded, either manually or through an automated system. Upon completion, a final document — known as the calibration certificate — is generated, containing all technical details of the procedure.

Typically, the certificate includes comparisons between the calibrated device and the traceable reference standard. It must also provide technical specifications of both instruments, procedural data, calibration uncertainty, calibration number and the signatures of authorised personnel.

Which instruments require calibration?

All measuring devices can be calibrated to ensure proper functionality and the level of accuracy required for their application. While the concept of calibration remains consistent, the procedure varies depending on the type of field instrument.

For example, calibrating a pressure transmitter may involve using a calibrated deadweight tester as a reference to generate pressure. Alternatively, another pressure device with higher accuracy than the instrument under calibration can be used.

All calibration standards must include a valid calibration certificate confirming compliance with applicable standards in the relevant region.

How does calibration work?

Calibration involves comparing the device under test with a reference standard, typically at multiple points across the measuring range — commonly 0%, 25%, 50%, 75% and 100% of range. Additional test points can be included if required, although this may increase time and costs.

The reference standard used depends on the type of device:

Flow transmitters: Calibration may involve a master device, comparison with a weight scale or mobile prover calibration.
Pressure transmitters: A higher-accuracy standard device, digital calibrator, or deadweight tester is typically used.
Temperature transmitters: A calibrated reference such as an electronic temperature sensor simulator is applied.

Calibration procedures are guided by a Standard Operating Procedure (SOP) that outlines each step. The interval between calibrations is not universally defined but can be determined based on factors such as:

device type and application
manufacturer recommendations
trend analysis from previous calibrations
instrument historical data
comparison with similar devices in the plant
required measurement accuracy.

The difference between calibration and adjustment

Calibration is generally understood as the process of comparing a device with a reference standard of higher and known accuracy. Adjustment, if required, follows calibration to correct deviations identified during the comparison.

During calibration, the procedure involves verifying the measuring range against the reference standard. If an error greater than the acceptable limit is detected, the instrument must be adjusted.

For example, adjusting a pressure transmitter typically involves trimming the zero and then the span value. These parameters can be modified through mechanical or software settings, depending on the device’s age and manufacturer specifications. After adjustment, the measuring range must be rechecked against the standard to confirm that accuracy meets the required limits.

What’s the benefit of the onsite calibration?

Onsite calibration is a common practice in industrial environments, particularly during planned production shutdowns when multiple instruments require calibration. In such cases, external service providers are often engaged to calibrate pressure, temperature and flow instruments.

Field calibration, including flow calibration, is increasingly prevalent. Many companies now employ mobile calibration rigs to perform these services directly onsite.

The benefits of onsite calibration include eliminating the need for instrument transportation, enabling immediate adjustments and repairs, and facilitating quick instrument replacement — all performed by qualified experts. This approach reduces downtime and ensures compliance with calibration standards.

How often should you calibrate?

The frequency of calibration depends on several factors, as there is no universal standard. Best practices suggest considering the following points when defining calibration intervals:

The criticality of the measurement to the process.
Quality system requirements at the plant.
Regulatory compliance.
Manufacturer recommendations.
Impact of failure due to lack of accuracy.
Other technical requirements.

These factors help establish an appropriate calibration schedule, which can be adjusted as needed. Modern IIoT solutions further simplify calibration planning and execution by providing easy access to device data and scheduling tools.

What is calibration uncertainty?

Calibration uncertainty refers to the degree of doubt associated with the calibration process and is influenced by factors such as installation conditions, reference traceability and environmental variables. If calibration uncertainty exceeds the tolerance of the instrument being calibrated, the validity of the calibration must be questioned.

For example, using a clamp-on flowmeter to calibrate an in-line device may result in calibration uncertainty higher than the installed meter’s tolerance, making the process ineffective.

What should you know about pass and fail calibration?

A device under test can either pass or fail calibration based on its tolerance limits, which are defined by the manufacturer or specified in the initial calibration certificate. During calibration, if the measured error exceeds the tolerance limit, the calibration is considered to have failed. In such cases, the device should be adjusted and recalibrated. If the difference between the calibrated device and the reference standard falls within the tolerance limit after adjustment, the device passes.

How to manage calibration reports with IIoT

Proper storage and accessibility of calibration documents are essential. Modern IIoT services enable centralised cloud-based management of calibration reports, technical data and related documentation under each device tag. This approach ensures that all team members can access, share and update information efficiently, saving time during field verification or when retrieving historical calibration records.

When integrated with edge devices, IIoT platforms can automatically create digital twins of all instruments, making files accessible from smartphones, tablets and laptops. This simplifies collaboration and ensures that technical documentation and calibration reports are always available, improving efficiency and compliance.

Image credit: iStock.com/Smederevac

Ensuring reliable level measurement in tanks with internal obstructions

Tue, 14 Apr 2026 00:00:00 +1000

Accurate and reliable level measurement is fundamental to the safe and efficient operation of process plants. Level data underpins effective process control, optimised inventory management and precise custody transfer — all of which directly influence productivity and profitability. In addition, level measurement is central to critical safety applications such as overfill prevention. A broad range of level measurement technologies is available to end users, including differential pressure, capacitance and guided wave radar. Each technology has proven effective in specific conditions, but non-contacting radar transmitters based on frequency modulated continuous wave (FMCW) technology have emerged as a preferred choice for numerous applications.

Non-contacting radar level transmitters provide a direct, top-down measurement that is extremely accurate and reliable, and with no moving parts these devices have minimal maintenance requirements. Because the antenna does not come into contact with the process medium, issues such as coating, corrosion and mechanical wear are eliminated or greatly reduced. This makes the technology particularly well-suited to aggressive chemicals, sticky or viscous products, as well as hygienic applications where material contact must be avoided. Radar measurement requires no compensation for variations in density, dielectric constant or conductivity, and modern FMCW transmitters can maintain high accuracy in extreme pressures and temperatures. Because this versatile technology is suitable for measuring the level of liquids, sludges, slurries and bulk solids, it has been widely adopted across industries including oil and gas, chemical, refining, food and beverage, water and wastewater, and life sciences.

Principle of operation

Non-contacting radar level measurement is based on the transmission and reflection of microwave signals. FMCW devices transmit a continuous microwave signal, the frequency of which is constantly varied across a defined range. When the reflected signal (known as an echo) returns from the product surface, it is compared with the frequency of the signal being transmitted at that moment. The difference between the two is directly proportional to the distance to the surface.

Challenges posed by internal tank obstructions

The product surface is, however, not the only feature within a tank that reflects microwave signals. Tanks often contain a range of internal structures such as ladders, agitators, heating or cooling coils, baffles and nozzles, which can also reflect signals, producing false echoes that compete with the true surface echo. The transmitter must then distinguish between multiple possible echoes to identify which one accurately represents the product surface.

This task becomes especially challenging when measuring products with a low dielectric constant, such as certain hydrocarbons, liquefied gases or oils. Because these materials reflect radar signals weakly, the true surface echo may be less distinct than the echoes generated by obstructions. As a result, even minor interference from internal structures can cause the transmitter to misidentify a false echo as the correct one. The presence of turbulence, foam or vapours can further complicate the situation, as these conditions may weaken the surface echo or introduce additional sources of signal scattering. When combined, these factors can make accurate and reliable level measurement in obstructed tanks one of the most difficult applications for non-contacting radar technology.

The consequences of interpreting a false echo as valid

When a transmitter misinterprets a false echo as the true product surface, the result is an inaccurate level measurement. The consequences of such an error can be wide-ranging, and in many cases extremely serious. The most critical risk is overfilling the tank. If the transmitter reports the level as lower than it actually is, a tank may be filled beyond its capacity. This can lead to product spillage, which in the case of volatile or flammable substances poses immediate safety hazards to personnel, as well as the risk of fire or explosion. Even when the product itself is not hazardous, spills can cause environmental damage, and lead to regulatory non-compliance and significant financial costs associated with clean-up and product loss.

Conversely, a false echo may cause the transmitter to indicate a level higher than reality, leading to premature filling stops. Underfilled tanks reduce storage efficiency, disrupt production schedules, and can result in downstream process interruptions, product shortages or even dry running of pumps, which may cause equipment damage and unplanned downtime. Across industries that depend on just-in-time operations, such inefficiencies can translate directly into lost revenue and reduced competitiveness.

Figure 1: Equipment such as agitators, heating coils, pipes, ladders or baffles inside tanks can potentially interfere with microwaves and impact level measurement.

False echoes can also degrade process quality and efficiency. In batch operations that rely on precise volume control, inaccurate level measurements may lead to inconsistent product quality, rework or waste. Ultimately, the misinterpretation of a false echo compromises not only safety, but also operational efficiency, product quality and profitability. This makes effective discrimination between true and false echoes a critical requirement for reliable non-contacting radar level measurement.

Figure 2: Internal equipment can make it challenging for a non-contacting radar level transmitter to differentiate the true surface echo from false echoes coming from obstructions.

Strategies for mitigating false echoes

While tanks containing internal structures present clear challenges for non-contacting radar level transmitters, a number of strategies can help to reduce or eliminate the impact of false echoes. The most fundamental consideration is the placement of the radar device. If a tank has an existing nozzle that provides a completely unobstructed line of sight to the product surface, installing the transmitter there is the most straightforward and effective solution. Proper positioning minimises the likelihood of echoes being generated by internal structures and helps to ensure that the strongest signal received corresponds to the actual product surface.

In practice, however, it is uncommon for a nozzle to be located in an ideal position. Tank openings are often dictated by mechanical design, process requirements or structural constraints rather than by measurement considerations.

As a result, many installations cannot avoid at least some degree of obstruction within the radar beam path. In these cases, additional measures become necessary to achieve accurate and reliable measurements.

Deflector plates

In tanks with internal structures, some radar level transmitter vendors recommend the use of deflector plates to reduce the impact of false echoes. These plates are typically installed near obstructions and angled to redirect radar waves that would otherwise reflect directly back to the transmitter. By guiding unwanted reflections towards tank walls or other areas where they dissipate, deflector plates help the transmitter to more reliably identify the true material surface, resulting in more stable and consistent level measurements.

However, while deflector plates can improve measurement reliability in some applications, their installation presents several practical challenges. In tanks with limited access or complex internal structures, positioning the plates correctly can be difficult. Misaligned plates may inadvertently create additional reflections or partially block the radar beam, producing blind spots or signal loss. More importantly, no end user is likely to go through the expense and inconvenience of securing confined-space entry permits and deploying welders inside a vessel merely because an internal obstruction might cause a measurement issue. Such interventions represent significant operational disruption and cost, and are rarely justified unless a proven and recurring problem exists. Operational conditions also affect performance. In tanks containing sticky, viscous or dusty materials, build-up on deflector plates can change the angle of reflection or generate new false echoes, potentially compromising measurement accuracy.

Figure 3: Deflector plates are typically installed near obstructions and angled to redirect radar waves that would otherwise reflect directly back to the transmitter. However, their installation can be challenging.

False echo suppression

For many years, top-down level measurement technologies have used a common mapping technique to analyse received signals and suppress false echoes, ensuring that the device reliably detects the true material level. During initial commissioning, a reference map of the tank is created by capturing echoes when the tank is empty or at a known level. These stored signals represent potential false echoes from fixed obstructions and serve as a baseline for comparison during normal operation. As the transmitter operates, incoming echoes are continuously evaluated against this reference map. Signals corresponding to known obstructions are identified and effectively ignored, while changes in the echo profile indicate movement of the actual product surface. This enables accurate, continuous level measurement, even in tanks with complex internal geometries.

In practice, however, there are two fundamental limitations to conventional false echo suppression. Firstly, the need to empty a tank in order to map echoes is often impractical, as this can interrupt operations and add downtime that many facilities cannot easily justify. Secondly, false echo suppression relies on the transmitter establishing a fixed threshold to block unwanted echoes based on conditions observed at the time of set-up. However, echo amplitudes can fluctuate over time due to process changes, temperature variations, or other environmental influences. If a previously suppressed false echo later increases in strength and exceeds the stored threshold, it can reappear in the measurement signal — sometimes unexpectedly — leading to intermittent or misleading level readings. These factors highlight why, despite their usefulness, traditional false echo suppression methods are not foolproof.

Smart echo supervision

More recently the introduction of smart echo supervision has enabled organisations to achieve more accurate, reliable measurements in tanks with internal obstructions — and without the need for installing deflector plates or running complex false echo suppression algorithms.

At the heart of smart echo supervision is a dynamic evaluation of all viable echoes. The system continuously analyses the behaviour of each echo in real time, ranking them according to how closely they resemble the behaviour of a true material surface reflection over time. The echo that most consistently mirrors the dynamics of the surface is automatically tracked as the genuine surface echo, while stationary or irrelevant echoes — typically originating from tank obstructions — are automatically suppressed.

This adaptive approach allows the transmitter to maintain accurate readings even as the echo profile changes due to rising or falling levels, tank agitation, temperature variations, or the presence of vapour or foam. By continuously adapting to these environmental changes, smart echo supervision aims to ensure stable, repeatable performance without the need for frequent manual recalibration or intervention. On those occasions when manual adjustment is required, a user interface can enable operators to suppress unwanted echoes. This simplicity reduces commissioning time and minimises the potential for human error.

By combining intelligent echo evaluation and real-time adaptation, smart echo supervision makes it possible to confidently measure tank levels in even the most obstructed and complex vessels, while simplifying installation, commissioning and ongoing operation.

Conclusion

While today’s high-frequency non-contacting radar transmitters with narrow beam angles can reduce the risk of interference in obstructed tanks, true measurement reliability is only achieved when they are paired with advanced signal processing. Smart echo supervision is the latest technology that provides this capability, providing a solution that is specifically engineered to address the complexities of tank environments.

By continuously analysing and ranking all viable echoes, filtering out false signals and adapting dynamically to changing process conditions, the technology ensures that the genuine surface echo is consistently identified and tracked. The result is a more stable, accurate and dependable level measurement.

Top image credit: iStock.com/VanderWolf-Images

How to centralise remote access: securing all access to your OT systems

Mon, 13 Apr 2026 00:00:00 +1000

Remote access is critical for cyber-physical systems (CPS) in industrial environments. First- and third-party vendors need access to their devices in your network. Sometimes that access is needed at 3 am because a system is offline unexpectedly, or remote access is needed when an engineer is based in another country and needs to perform regular device maintenance.

For many organisations, this need for remote access results in many tools. In fact, according to research, 55% of organisations have four or more remote access tools in their OT environment — and 33% have more than six.

Tool sprawl like this translates to an expanded attack surface, so it’s no coincidence that 82% of organisations have experienced at least one cyber attack related to third-party access. And that’s only breaches from third-party remote access — not including internal engineers remotely accessing critical devices.

Why are there so many tools performing the same function and adding to the attack surface? Often, OEMs, integrators and contractors have their own tool or prefer a specific avenue. If there isn’t a centralised tool available, engineers make do with consumer-grade or IT tools so they’re able to perform necessary actions.

Why tool sprawl is rampant

There are many reasons organisations end up in this position. Broadly, the list of concrete reasons below stems from the original disconnected nature of CPS devices. The assets were deployed in isolation, fully disconnected from other devices or any network. As connectivity was introduced, each vendor and internal team came up with its own way that worked to access these devices remotely.

Both machine builders and industrial automation manufacturers produce assets that work together to make a functional production line in a plant. The assets produced by each vendor type are used in production lines across many plants for all of their customers. You can see the compounding effect at work here — hundreds of assets in an industrial environment need secure remote access by multiple vendor types which each use their own preferred tool and method.

Why an organisation would consolidate tools

There are a number of common drivers for tool consolidation:

The primary negative drivers are failed audits and security breaches. These are the most common reactive reasons organisations seek this change.
Some organisations are motivated by other projects. One common cause is a segmentation project, during which remote access solutions are cut off because they’re segmented out or communication with a device is seen to break policies or deviates from accepted behaviours.
The final motivation is the most proactive. Some organisations are aware of the risks associated with IT or consumer remote access solutions and look for a way out.

Regardless of why you start, you likely have a few objectives, including:

reducing cost, complexity and risk;
increasing compliance, mean time to repair (MTTR), connectivity, change management and governance.

The secure access maturity model

Before initiating a consolidation project, we need to understand the five levels of mature, centralised remote access. You may be at different levels with different vendors depending on contracts, contractors and service level agreements (SLAs).

The five levels are:

Level 0: Do nothing — Every engineer, internal or third party, connects to your assets however they can. This likely includes external engineers connecting to workstations, and remote workstations tunnelling into OT assets.
Level 1: First-party access — Internal engineers use a centralised remote access tool. By starting with internal teams, you’re leading by example before introducing a central solution to third parties. Moving your engineers first makes subsequent vendor conversations far smoother, as you can definitively say it works.
Level 2: Introduction to third parties — Begin with the simplest vendors when bringing external engineers into your established tool. These are likely your third-party engineers connecting to onsite engineering workstations. In the playbook below, we’ll guide how to prioritise vendor types so you know where to start. This is the point at which you are most likely face vendor obstacles and objections.
Level 3: Advanced third parties — Now it’s time to address more complicated vendors with technically complex architectures, tools and processes. For example, if they’re connecting to your factory via a tunnel from a virtual workstation to a PLC, you move that tunnel into your remote access tool. This is not a perfect solution, but it reduces the attack surface and gives you some control.
Level 4: Cost optimisation — The final stage brings all remote access through your centralised tool. Vendor workstations become virtual machines that are brought into the industrial demilitarised zone (IDMZ), providing the same type of remote access as internal engineers.

Note: It is possible to jump from Level 2 to Level 4, depending on the vendor relationships and types of access used today.

Figure 1: The secure access maturity model. For a larger image click here.

A chasm is often created after Level 2. Internal and select third-party vendors are relatively simple to switch. Beyond those, you will likely encounter challenging conversations and real pushback.

Common vendor engagement obstacles

The following are obstacles often raised by vendors who end up switching to a centralised secure access hub. The root causes shared across these are either a loss of control over a customer account or concern about scalability.

Objection examples are:

IP Protection: “We can’t have you record what our engineers are doing.”
Binding Agreements: “Remote Access is built into our contract.”
SLA and Vendor Responsibility: “This breaks our standard SLAs.”
Financial: “This is not our model.”
Operational: “We are not trained on this tool.”

You may anticipate hearing some of these objections, and that may be why you still have multiple remote access tools.

Whether you can already pinpoint which challenge you’ll face with which vendors or you have no idea what’s to come, the playbook below will prepare you for consolidated, secure access.

Playbook: Centralising secure access

This plan includes three primary steps: Discovery, Prioritisation and Engagement.

Step 1: Discovery

Get to know the access tools and technologies that are being used in your sites.

Vendors may use diverse technologies and architectures. Understanding these solutions is key for risk assessments, prioritisation and planning the next steps.

Step 2: Prioritisation

Methodically prioritise the vendors you engage with based on mission criticality and ease of switching.

First, define criticality using concrete criteria. You can use defined asset criticality to assess risks if the device were breached, and define the criticality of remote access for each asset in your CPS protection tool.

Rank vendors and their assets on a scale from 1 (least) to 5 (most) when establishing mission criticality:

Consequence of incorrect operation: Measures the risk of harm if an asset is accessed improperly (whether accidental or malicious).
Frequency of remote support needs: Measures how often the asset requires remote diagnostics, updates or troubleshooting.
Distribution across sites: Measures whether the same asset or system type exists across multiple facilities.
Regulatory and audit exposure: Measures whether access to this asset is subject to external audit, regulatory control or contractual oversight.
Variability of user roles: Measures whether multiple types of users (OEMs, internal engineers, contractors) need access to the asset.
Sensitivity of mean time to recovery (MTTR): Measures whether delays in diagnosing or resolving issues increase downtime cost or safety risk.

Next, evaluate the ease of switching each vendor to your centralised remote access hub.

Table 1: Comparing vendors on mission criticality and ease of switching. For a larger image click here.

Rank these criteria on a scale from 1 (least) to 5 (most) challenging when establishing ease of switching each vendor:

Vendor openness to switching: Is the original equipment manufacturer (OEM) willing to consider a change? Factors that might make some more or less willing include whether remote access is embedded in OEM contracts, or if they have no ties to a specific tool.
Integration complexity: How deeply embedded is the current solution in the OEM’s architecture? Modular architectures are easier to tackle than those with custom remote access integrations.
Contractual lock-in: Are there penalties for your organisation if you terminate or alter the contract early by removing remote access? Long-term, inflexible contracts are tougher to exit than those with transparent exit terms.
Field technician enablement: Will the switch require field operational enablement or retraining? If minimal retraining is required due to intuitive UIs, they’re likely more open to the change.
Cybersecurity standards: What is the risk associated with implementing the new access tool? Tools that are pre-certified with proven hardening are likely to be perceived as easier to adopt.

Once all vendors and assets requiring access are prioritised, use metrics to evenly compare all vendors (see Table 1). Then plot these scores based on mission criticality versus ease of switching to plan your strategy and next steps (Figure 1).

Figure 2: Plotting vendors according to ease of switching and mission criticality.

Step 3: Engagement

Adjust your engagement methods to the vendor and the remote access tool. There are five stages to use while progressing through the vendor scores you’ve just completed.

Establish a standard access policy: Create a company policy for secure access that is backed by up-to-date regulations. This can be used to transition and sway all vendors.
Begin with the easy targets: Use your priority list to engage with the vendors who are likely to switch without much effort.
‘Birds of a feather’: Ask other vendors for support to convince those who are less willing to switch.
‘Odd one out’: Once a majority of vendors have been onboarded, use that as leverage with remaining vendors.
Make a procurement case: Establish that non-compliant access solutions will be excluded in future projects. This is the last step as it is the most extreme option if a vendor is still unwilling to change.

These five phases have been successfully worked through to move myriad vendors into a centralised remote access hub.

Conclusion

Remote access sprawl is not a theoretical issue — it poses real risks for industrial enterprises today. Centralising remote access creates benefits for engineer and system productivity, reduces risk, and adds control and governance.

This can be a daunting project with many complexities, but it can be done and the rewards include increased productivity, reduced risk and reduced complexity. You can make substantial progress fairly quickly by moving first- and easier third-party engineers. Then you can leverage this playbook to tackle the resistant or more complex vendor scenarios.

Top image credit: iStock.com/ultramansk

Shining a light on cyber threats hiding on the plant floor

Fri, 10 Apr 2026 00:00:00 +1000

Each year we analyse threat data from across the industrial sector and publish what we find. In 2025 one pattern stands out: manufacturing remains the most targeted industrial sector for ransomware. Unfortunately, too many incidents are still treated as IT problems rather than OT issues, even though they have direct operational consequences.

The Dragos 2026 OT/ICS Cybersecurity Report tracked 119 ransomware groups targeting industrial organisations last year, a 49% increase YOY, affecting approximately 3300 organisations globally. Manufacturing bore roughly two-thirds of that volume, around 2200 victims.

These figures are alarming, but understanding why manufacturing is such an attractive target points toward practical action.

The connectivity factor

As the backbone of the global economy, manufacturing has transformed over the past decade. Digital transformation, automation, and remote operations have improved efficiency and competitiveness — but also expanded the attack surface.

Modern facilities rely on increasingly connected, often standardised systems. Where facilities once ran isolated proprietary equipment, they now use shared network infrastructure and enterprise systems tightly linked to production. As a result, an incident in an enterprise system can cascade into operational disruption. A compromised supplier or vendor connection can become an entry point across multiple sites.

Our field data reflects this. Manufacturing environments have the highest rate of shared IT and OT network domains of any sector we assess, at 46%. While this integration is often necessary, it requires security architectures designed to prevent adversaries from exploiting those pathways.

What adversaries are actually doing

The threat landscape in 2025 showed not only higher volume but greater sophistication. Ransomware groups increasingly targeted virtualisation infrastructure — hypervisors and virtual machines hosting SCADA systems, historians and HMI platform critical to operations.

Because engineering workstations and HMIs often run on Windows, attacks are frequently classified as IT incidents. Yet the consequences — halted production, loss of process visibility, and complex recovery requiring OT expertise — are operational. Organisations that respond using only IT playbooks typically recover more slowly and less completely.

We also observed extensive operational data theft. Threat actors exfiltrated information on how industrial processes are controlled and monitored — activity that indicates preparation rather than immediate disruption. Understanding system configurations allows adversaries to develop more advanced future attacks.

Supply chains add another layer of risk. Threat groups deliberately targeted OT equipment suppliers, using compromised vendors as pathways into customer environments. Any facility relying on third-party remote access should treat that as a priority security concern.

The visibility problem

A central challenge in OT cybersecurity is determining what happened when something goes wrong. On the plant floor, operators cannot often distinguish between mechanical failure, configuration error, or a cyber incident because the necessary monitoring data simply does not exist.

This is not negligence; it reflects how OT systems were designed. Industrial systems prioritise uptime and reliability, not security telemetry. Many are legacy platforms never intended to produce detailed logs. Consequently, incident response often means reconstructing events from incomplete evidence — precisely when clarity is most needed.

A practical path forward

Effective OT cybersecurity does not require solving everything at once. The SANS Institute's five critical controls for OT cybersecurity provide a practical framework: developing an ICS-specific incident response plan; implement defensible architecture with segmentation; gain visibility into OT networks; secure remote access; and apply risk-based vulnerability management.

Importantly, vulnerability management in OT differs from IT patching. Many industrial systems cannot be routinely taken offline. Prioritising vulnerabilities based on real operational exposure is more effective than applying standard IT timelines.

Remote access remains a major weakness. Most ransomware response cases Dragos handled in 2025 involved compromised VPNs or remote access systems, through vulnerabilities or stolen credentials. Strengthening controls, including multi-factor authentication and strict governance of third-party access, directly addresses the most common attack pathway.

Manufacturing leaders understand the value of visibility in their operations. The same principle applies to OT cybersecurity. Knowing what is running on operational networks, how systems communicate, and where anomalies occur is foundational. Without that visibility, both defence and recovery become far more difficult.

The 2025 data makes the case clearly: adversaries have adapted to manufacturing environments, and security programs must evolve. Facilities that treat OT cybersecurity as an operational discipline, not simply an IT function, will be best positioned to withstand future threats.

*Nicholas Tangey is the Senior Manager, Threat Hunting at Dragos, where he manages a team primarily focused on enabling and providing detection, threat hunting, and response services within the OT Watch managed service to monitor and safeguard industrial client environments through threat hunting, security assessments, and IR services.

Top image credit: iStock.com/Wanniwat Roumruk

Is machine monitoring worthwhile?

Wed, 08 Apr 2026 00:00:00 +1000

There are many ways of monitoring machines or even entire systems. However, monitoring for the early detection of damage and defects does not necessarily make good economic sense for every machine or system.

Maintenance strategies

Run-to-failure maintenance

Failure-based or reactive maintenance is also known as run-to-failure maintenance and is regarded as a passive strategy. With this form of maintenance, a system component is only replaced or repaired once it has actually failed. No information about the condition of the machine is collected or evaluated while the system is in operation. The problem with this approach is that the extent of the damage and the required restoration time cannot be predicted. The advantages of this approach are that no costs are incurred during smooth operation and the full wear reserve of the machine is utilised.

Failure-based maintenance is suitable for machines or components that are not critical to production, are easy to replace, and do not lead to expensive consequential damage.

The actual service life of machines and machine elements is often shorter than the basic rating life. Imbalance and misalignment (60%), bearing damage (20%), and other contributing factors such as structural problems, mounting issues and resonance (20%) are the most common causes that can lead to unexpected system failures and production downtime.

Preventive maintenance

In the case of preventive maintenance, it is assumed that a machine or system requires particular maintenance expenditure at defined time intervals. The definition of the time intervals is based on the average operating life of the system and on empirical values.

Since the time intervals in this maintenance strategy are fixed, they can be integrated in a targeted manner into existing production operations or downtime planning. However, they do not necessarily correlate with the actual condition of the system. It is therefore possible that maintenance measures will be carried out prematurely, thus making an unnecessary claim on resources.

Preventive maintenance is usually prescribed by the warranty provisions, as defined in the maintenance plan. When the warranty expires, a suitable monitoring strategy can be considered. In many cases, the switch is made to condition-based or predictive maintenance.

Condition-based maintenance

With condition-based maintenance, the machines and systems are not serviced on the basis of failures or time, but according to the established condition of the components. With this strategy, condition monitoring is used to carry out maintenance and repair work in accordance with the actual condition of a system or machine.

Different methods can be used in isolation as well as in combination to determine the current condition of the system. The outcome of the condition monitoring is incorporated into the planning of targeted maintenance measures, taking account of various parameters. The efficiency of the monitored machine is increased and an overall reduction in downtime costs is achieved.

Condition-based maintenance is suitable for process-critical systems, in which a high degree of accuracy is essential. As a rule, the cost of monitoring systems is already offset by preventing the first occurrence of consequential damage.

Predictive maintenance

Predictive maintenance is becoming increasingly important. The current condition of a system is not only considered by means of a defect analysis or causal investigation, but also optimised with the aid of accompanying measures. This is intended to further reduce the probability of a future failure in the long term.

The measures used can include an analysis of the machine history, special measurements to determine natural frequencies or phase relationships as well as improvements to the operating condition in the form of precision balancing and alignment.

Methods

Various non-destructive methods are available for recording the condition of a machine in operation. These include vibration analysis, lubricant analysis, thermography and endoscopy.

Vibration analysis

Vibration-based machine monitoring is a reliable tool for identifying and establishing the cause of machine problems at an early stage. With rotating machines in particular, this form of monitoring can detect a deterioration in machine condition early on, largely due to the increase in vibration behaviour.

Frequently detected sources of defects include imbalance, misalignments, rolling bearing damage and interlinking defects. Depending on the application, advance warning times of several months can be achieved with this measuring method. This method of condition monitoring offers considerable cost-saving opportunities if the operating life of the systems and machines can be almost fully utilised and their availability increased.

What is vibration analysis based on?

Simply put, vibration analysis is based on changing forces and power transmission processes. If the forces acting in the machine change, the vibration behaviour of the machine will also change. An increased vibration level with constant operating parameters indicates a deterioration in the machine condition.

Lubricant analysis

With lubricant analysis, the lubricant can be monitored directly in the machine by sensors or examined in the laboratory by taking samples. In most cases, viscosity, water content, contamination and aging are examined. In the offline monitoring of lubricating oil for solid and liquid contamination, samples are taken and examined at regular intervals.

The online monitoring of oil by sensors in the machine can take place either in the main oil flow or in a branched tributary. In addition to lubricating oil, it is also possible to monitor the condition of lubricating grease.

In such cases, offline monitoring is often used. Condition-based relubrication can also be achieved in conjunction with automatic lubricators.

Endoscopy

As an imaging method, endoscopy allows immediate conclusions to be drawn about the condition of components, such as rolling bearings and gear teeth, without necessitating the time-consuming process of dismantling the machine. The current condition can be clearly determined and documented in a video or image.

If the operating parameters, such as performance or speed reduction for example, or maintenance measures are adapted to the current condition, further damage propagation can be delayed.

Thermography

With thermography, heat sources caused by damage can be identified and monitored, both mechanically and electrically. The exceptional feature of this technology is that it not only targets the mechanical aspect of the system’s condition but is also applied to electrical components.

The major advantage of thermography is the rapid and contact-free recording of surface temperatures during operation. Using a photo produced in parallel, the temperature curves of a system or machine part can be assigned onsite and documented as the actual condition. Furthermore, any misalignment of motors, pumps or fans can often be detected in commissioning.

Structuring a condition monitoring system

Vibration monitoring systems

The selection and design of a suitable monitoring program is important for condition monitoring. Vibration monitoring is used very frequently. A structure is usually accompanied by standards, and the applicable standard for vibration analysis is DIN ISO 13373. For optimal planning, this standard recommends creating a flowchart to map the structure and implementation of an appropriate condition monitoring system. For successful vibration monitoring, the following points must be taken into consideration:

Selection of the machines to be monitored
Selection of a suitable measuring system
Selection and designation of the measuring points
Definition of the data acquisition interval
Definition of the measurement configuration
Recording of measurement data
Evaluation of the measurement and trend data
Recommended actions
Reports and documentation

When is condition monitoring appropriate?

The answer to this question is essentially determined by the criticality of the system, which includes how important the system is to the production process and even the accessibility of the system in the event of repairs. Once the critical systems have been identified and the risk of failure has been assessed, the appropriate maintenance strategy can be defined for the system (in this case, condition-based maintenance).

Condition monitoring is a practical solution for critical and hard-to-reach systems. Early fault identification and analysis allow appropriate measures to be taken to reduce downtime and optimise maintenance.

Is condition monitoring complicated?

The decisive factors for answering this question are the level of specialist knowledge available and how condition monitoring is to be integrated into operation as a building block for optimising maintenance. Novices can start with simple solutions that do not require any prior knowledge and then gradually expand on their knowledge. Where specialist knowledge is lacking within the company and needs to be developed, the available options include:

Becoming an expert: If a process engineer wishes to become an expert, they should obtain advice and training from experts with longstanding experience.
Outsourcing: If an organisation is simply looking for its system to work and not encounter unscheduled downtime, then expert third-party assistance can be sought.

Does condition monitoring save money?

When used correctly, condition monitoring definitely saves money. In most cases, savings are already achieved following the initial activation of an alarm, simply due to the prevention of consequential damage. Avoided production downtime constitutes the greatest saving.

Incidentally, condition monitoring can be used regardless of whether the machine is new or old. In both cases, the condition can be determined from the initial measurement.

Success factors

The success of a condition monitoring system is primarily dependent on how well the solution is tailored to requirements. When choosing a partner, an organisation should pay attention not only to the available hardware and software, but also to the service and training concept, as well as proven experience.

Takeaways

Condition monitoring is worthwhile, both for individual machines and entire systems. Conclusions about possible defects can be drawn from vibration analysis as early as the initial measurement, even with old machines.

There is no single solution, rather multiple solutions that should be individually tailored to a plant’s systems, and different methods can be used in combination and may be an appropriate solution in certain cases.

Image credit: iStock.com/imantsu

Australia's green metals gambit: the technologies to decarbonise steel

Tue, 07 Apr 2026 00:00:00 +1000

A switch to green iron, powered by renewables, could grow Australia’s iron ore export earnings from $116 billion in 2024–25 to around $386 billion a year by 2060, according to projections from the Superpower Institute headed by economist Ross Garnaut. Australia is already by far the world’s biggest exporter of iron ore, supplying just under 55% of global market share in 2024 (nearly all of it is from the Pilbara region in WA) and the world’s largest alumina exporter. Australia faces a significant obstacle however, if it is to transform the world’s largest iron deposits into fossil-free metals, according to news from the CSIRO.

With iron and steel production alone responsible for a massive 6–8% of global carbon dioxide emissions, and aluminium a further 2%, there’s global demand for decarbonisation of metal production, and this is where the problem lies.

“Making metals is an energy-intensive business,” said Keith Vining, CSIRO Group Leader for Green Metals Production. “Pilbara iron ores are challenging materials in existing low carbon iron-making processes.”

The problem lies in impurities (mostly silica and alumina) which push the ratio of iron content down and raise the ratio of waste rock and minerals, known as ‘gangue’. These impurities are tightly bound to iron oxide in the rock and need to be removed via high-temperature processing.

Traditional blast furnaces use coal to remove oxygen and associated impurities from iron ore in the steelmaking process, while the newer direct reduction (DRI) process uses gas or hydrogen as a reducing agent instead of coal. But DRI typically requires ores with an iron content over 67% which excludes Australia’s lower-grade Pilbara ore. With 55–62% iron, Pilbara ore is not pure enough for current low-carbon and carbon-free technologies.

Working on a solution

Instead of waiting decades for new technologies to emerge, CSIRO is currently testing adaptations of existing DRI technology that can work with Pilbara ore.

“We want to use existing technologies because the pathway for brand new technologies is so much longer,” Vining said.

CSIRO researchers are part of a cross-industry project testing whether the current narrow specifications around the operating envelope for DRI can be expanded and whether, by accepting some productivity loss, Australian iron ore could be viable for these new processes.

Value-add opportunity

The potential economic benefits are enormous. Australia’s steelmaking capacity is relatively small and limited with just two major steelworks at Whyalla and Port Kembla using primary ore. The economic opportunity lies in processing iron ores.

The Superpower Institute’s $386 billion annual revenue projection is based on ramping up green iron production gradually between now and 2060. Transforming our metals production away from fossil fuels would shift Australia from ore exporter to value-added producer, embedding renewable energy into what the nation ships overseas.

“Is it doable? Theoretically, yes, absolutely,” Vining said. “Those numbers are not based on everything suddenly getting made into a green iron product. The production ramps up over that period of time, so they’re pragmatic sort of numbers; but we need a lot of other things to come together, for us to pull it off.”

Infrastructure gap

The biggest barrier to achieving that $386 billion opportunity, Vining said, is that it requires a massive investment in infrastructure, including the development of an industrial-scale renewables grid to service metals processing works.

“We’re going to need a renewable energy network at a scale that simply does not exist at the moment,” he said.

Beyond the renewable energy network itself, Australia faces the challenge of building processing plants in an area with some of the world’s highest capital costs. The Pilbara region is remote and its ports are configured for outgoing shipments, not incoming materials and equipment.

“Most things have to go into Fremantle, and get trucked to the Pilbara,” Vining said.

That’s a journey of more than 1300 kilometres by road, and all of the labour will need to fly in and fly out.

Energy costs must also come down to competitive levels. Producing hydrogen from renewable energy is currently too expensive to compete with Chinese blast furnaces, even before factoring in labour and capital costs.

Bridge technology

Near-term projects are already moving forward using reformed natural gas (methane) as a bridge fuel. At 700–1000°C, gas reforming uses steam to convert methane into hydrogen and carbon monoxide.

“Using that process is actually 50% less CO₂-intensive than using solid carbon in a blast furnace,” Vining said.

BHP, in a partnership with Korean steelmaker POSCO, has announced plans for a hydrogen-ready DRI demonstration plant adjacent to POSCO’s steelworks in Korea established to process Pilbara ores without these first going through a pelletising process.

Another joint venture, NEOSMELT — led by BlueScope, in partnership with Rio Tinto, BHP, Mitsui and Woodside Energy, and supported by the Australian Renewable Energy Agency — will build an electric smelting demonstration facility in Kwinana, south of Fremantle in Western Australia. The project involves feeding direct reduced iron into an electric smelting furnace, with plans to operate for three years to test the technology at scale.

The projects making Australian ore work

A key focus for CSIRO is adapting Pilbara ore for these new processes, with research spanning the full production chain, from grinding to pelletising to electric smelting.

CSIRO’s three-year India-Australia Green Steel Partnership supports five different projects to reduce emissions and address the challenge of processing low-grade iron ores without fossil fuels. Under this program, researchers have developed what Vining describes as “a very promising method for making the pellets that will go into the shaft furnace”.

On the pre-processing front, CSIRO is working both sides of the energy-intensive grinding circuit that breaks ore down into fine particles suitable for processing. In the first stage of the process, researchers are finding ways to selectively remove more of the unwanted material like silica and alumina, so less ore goes through the energy-intensive grinding step. After grinding, they’re improving classification systems to avoid recycling fine material back through the circuit.

The key technology in that classification stage is the hydrocyclone, which uses water and centrifugal force to sort particles at industrial scale.

“The challenge is trying to get the accuracy of a sieve with the throughput of a hydro-cyclone,” Vining said.

CSIRO is also building end-to-end pilot-scale capability, from pelletising to gas-based DRI to electric smelting, to simulate the complete process with Australian ores. The pilot is in the early stages with industry and university partners being established, and will couple an electric smelting furnace with existing pelletising equipment.

Image credit: iStock.com/Taitai6769

Australian manufacturers should prioritise energy optimisation over net zero

Thu, 05 Mar 2026 00:00:00 +1100

Australian manufacturers should be looking at energy waste and fix the basics as the first step in reducing spiralling energy costs, before committing to expensive new technologies and overly ambitious emissions reduction targets.

A recent CommBank survey found 89% of SMEs reported increased business costs over the past year, with utilities the largest contributor for 66% of businesses. Data from Energy Consumers Australia shows small business electricity bills have risen around 8% nationally in the past year, with some states recording increases above 20%.

DETA Consulting, an Australian and New Zealand engineering and energy advisory firm, says the issue is not ambition but sequencing.

“Too many organisations chase shiny technology or public net-zero commitments without fixing the basics,” said DETA’s managing director Jonathan Pooch. “It’s like worrying about the hot dog when you don’t have the bread yet.

“We regularly see 20–30% energy savings sitting there because no one has done a proper site walk-through and data review.”

Pooch said many manufacturing and commercial sites are leaking value every day through inefficient scheduling, poor controls and neglected maintenance.

“Energy is not always the biggest cost in a business, but it is often the easiest cost to reduce without harming production,” he said. “You are not cutting staff or ingredients. You are stripping out waste.”

A recent example is Cheetham Salt Australia, the country’s largest producer and refiner of solar salt.

DETA identified opportunities through an energy audit and carbon roadmap to save 4033 tonnes of CO2e per year — a 96% reduction — and deliver financial returns within one to three years. The audit gave the board actionable data, showing exactly where energy inefficiencies were costing money and how improvements could be sequenced for maximum impact.

Priority reduction initiatives were identified, a baseline established and a clear transition plan created, linking cost savings directly to decarbonisation outcomes.

Industry analysis suggests many manufacturers could reduce energy consumption by 20–30% through relatively straightforward operational changes before investing in new tech, including:

Stop waste: Regularly review metering data, switch off idle equipment and align plant schedules with actual production demand rather than running equipment continuously by default.
General maintenance: Compressed air systems, pumps and other utilities often run constantly with leaks or inefficiencies that go unnoticed. In energy-intensive environments, small faults translate into significant annual costs.
Control: Introduce stronger operational controls over when major energy loads run. Before changing fuel sources or installing new technology, ensure existing systems are operating only when required and at optimal settings.

Once waste is removed and savings are banked, businesses are in a stronger position to invest in lower-emissions technologies such as heat pumps, electrification of process heat, renewable energy procurement or longer-term power purchase agreements.

“If you unlock 25% savings through efficiency, that cash flow can help fund the next stage,” Pooch said. “The mistake is trying to fund major technology shifts before you have stabilised the business case.”

Pooch said part of the barrier is cultural, as many engineering teams are focused on output and production targets, with limited time or resources dedicated to analysing energy inputs in detail.

“Businesses understand how their product is made. They do not always understand how energy moves through their plant. That gap is where significant savings sit,” he said. “In a volatile energy market, sustainability only works if it is financially sustainable. Fix what you have first. Deliver measurable savings, then scale into the next phase of decarbonisation with confidence.”

Image credit: iStock.com/Smederevac

Virtual PLCs — a big step forward!

Fri, 20 Feb 2026 00:00:00 +1100

It wasn’t that long ago that PLCs executed just a single task — their one and only control program. Each PLC vendor created its own dedicated hardware, which for the sake of reliability, utilised technology that was several generations behind current offerings. Similarly, any software used by PLCs was proprietary and strictly limited to that vendor’s ecosystem.

While such a design strategy may have served us well in the past, it’s become increasingly clear that a far more flexible approach to system architecture will be needed to meet the heavy demands placed on modern‑day industrial automation systems.

For some years now, industrial automation has been trending towards becoming far more software oriented, espousing many of the techniques and practices of the software industry. Industrial control hardware platforms, and the operating systems they use, have become much more open, with Linux and Windows running on industrially hardened PCs being prime examples.

Furthermore, industry has adopted the internet for high‑speed and world‑wide connectivity and has accepted the widespread influence of the IT community into the OT space.

These advancements would be very difficult, if not impossible, to implement using the traditional paradigm of closed, hardware-orientated controllers.

Key to these new developments is the concept of virtualisation. Virtualisation is where several software-based implementations of physical hardware (called ‘virtual machines’) run concurrently on a single physical computer. Software applications are thus abstracted away from the underlying hardware. The host computer’s resources can be used more efficiently overall by having a hypervisor program allocate resources to each virtual environment, as needed.

That several applications can run independently in their own virtual environment is a major advantage. It means control programs can be constructed into discrete runtime modules — a commonly requested feature for segmented systems, where not every section will be available or active at once. Other software unrelated to control can also run simultaneously on the same platform.

Containerisation is a particularly efficient form of virtualisation. Dockers within the Linux environment are one implementation that’s gained a lot of attention recently, particularly from the German automobile industry.

Unlike virtual machines, which run a full, separate guest OS for each application, containers share the host’s operating system kernel, making them more lightweight. This is an important consideration for industrial controllers, which often provide only limited computing resources. Being lightweight also helps maintain real‑time performance — a mandatory requirement for control applications.

High portability is another advantage of Docker, as each container runs as an isolated process, with its own resources. This facet also provides a high degree of security and isolation, which is important for consistency of operation between systems.

Cloud-based engineering is another notable implementation of virtualisation. Here, virtual PLCs reside in a data centre, which can be hosted either on premise or in the cloud. The data centre serves up web pages as its user interface. As all data is presented using HTML, users have access via any device that supports a web browser, such as a PC, tablet or even a mobile phone.

Improved program management is one advantage gained by cloud‑based virtualisation. Programmers can utilise a wide variety of software tools available, including CI/CD (continuous integration/continuous deployment) tools, like Azure DevOps. Source control is also integrated, meaning easy collaboration and faster program development.

Cloud-based control is where one (or more) virtual PLC programs execute in the cloud, rather than being processed in localised computing hardware. This ‘controller’ connects to localised devices and I/O systems on the factory floor, typically using standard protocols such as MQTT and OPC UA.

Process applications are better suited to cloud-based control as time‑sensitive requirements are rare. However, as manufacturing processes generally need update rates nearing one millisecond, manufacturing normally uses localised control.

Cloud-based systems also provide mass data storage. Aggregating data over the longer term is ideal for in‑depth analytics and dashboard displays.

While none of us have a crystal ball, it looks as though the days of PLCs running a single control task, on dedicated hardware, using a propriety operating system, are numbered. It seems the requirements of modern‑day industrial control systems will demand at least some form of virtualisation, allowing multiple runtimes to execute on a choice of operating systems and open hardware.

Top image credit: iStock.com/Vertigo3d

Open Process Automation: how and where to start

Wed, 18 Feb 2026 00:00:00 +1100

Industrial process automation has evolved significantly over the years, playing a pivotal role in improving operational efficiency, safety, cost reductions, and ensuring product quality. Traditional automation systems, however, often relied on proprietary, closed architectures, leading to vendor lock-in, interoperability challenges, and limited adaptability to changing business needs often required for enterprises to expand or progress. Open Process Automation (OPA) seeks to address these issues by promoting open standards, modular design, and interoperability. Organisations looking to embrace OPA will need to address critical but navigable steps to ensure a smooth transition.

What can the OPA framework offer?

The first step for an enterprise is to comprehend the OPA fundamentals. The OPA standards framework consists of protocols, data models and reference architectures that enable interoperability between automation ecosystem components. This understanding is crucial for decision-makers to grasp how OPA can align with their operational goals and requirements.

It is also important to know how OPA is charting a path forward with scalability, security and portability.¹ With OPA, a company can run process control logic in all parts of the system architecture, deployed on the hardware of its choice, since OPA is a software-defined automation system (SDAS). Knowing how OPA functions allows companies to make informed decisions on architectures and deployment strategies.

Assessment of current systems

As part of the OPA journey, an enterprise should conduct a comprehensive assessment of the existing automation infrastructure. This assessment should identify legacy systems, proprietary components and integration challenges. It should also identify gaps in information integration. The gap analysis will serve as a basis for understanding the extent of transformation required and the potential benefits of adopting an OPA platform.

Defining business objectives

Clearly defined business objectives are essential to driving the OPA component selection and implementation processes. Enterprises should outline goals, such as improving agility, reducing operational costs, enhancing scalability, or simplifying current and future maintenance. These objectives will guide the decision-making process and shape the customisation of the OPA framework.

Once these guiding principles are decided, it is possible to select the pieces needed for maximum effectiveness and minimum cost. Taking advantage of the OPA platform means a customised and optimised system. The component selection is based on the required business application needs, with a focus on functional desired features and the available budget.

Vendor selection and collaboration

Choosing the right vendors and partners is a critical aspect of OPA implementation. Enterprises should collaborate with vendors that align with the open standards philosophy, preferably an Open Process Automation Forum (OPAF) member who offers interoperable solutions, and provide a clear roadmap for OPA adoption. Successful collaboration guarantees the smooth integration of selected solutions into an OPA framework or the adoption of an OPA ecosystem for operating their current automation system.

Create expansion or migration plans

OPA allows a company to expand existing OT systems, incorporating new strategies, new hardware and new software tools. Expanding existing platforms with OPA technologies allows for easy incorporation of AI/ML and other industrial performance applications, providing additional avenues for plant performance enhancements and cost reductions.

An organisation could migrate an entire system; migrating from traditional automation systems to an OPA environment requires a well-defined migration plan. This plan should outline the sequence of steps, timeline and potential challenges. It should also address data migration, training requirements and contingency measures to minimise disruptions during the transition. Disruptions to daily business can be costly so these need to be — and can be — avoided.

The beauty of modular design and scalability

OPA’s modular design philosophy allows enterprises to scale their automation infrastructure more efficiently. Enterprises should embrace this aspect by designing systems as a collection of interchangeable modules. This approach ensures that future expansions, upgrades and adaptations can be accomplished without overhauling the entire system. There is virtually no limit to what can be created with OPA. OPA utilises common, commercially available hardware and software, selected and optimised to meet the business objectives at hand. Modular design means the organisation can select the components it needs to perfect its vision.

The modular design and scalability allow users to incorporate features such as AI, advanced controls, asset management and other tools together with the control system, reducing implementation costs and improving response times.

Data management and security

As data becomes increasingly central to industrial processes, effective data management and security are paramount. OPA is secure by design: from the hardware to the software, OPA incorporates the best-of-class cybersecurity technologies such as functional security (protecting both devices and resources), data encryption and certificates.

While security is baked into OPA, enterprises must still establish robust practices for management of the security features as required in IEC-62443. As part of an OPA project, a security assessment is conducted, including end user requirements to comprehensively assess risk and implement best practice security measures.

Training and workforce development

OPA introduces new concepts and technologies that may require upskilling of the workforce. Training programs should be developed to equip employees with the knowledge and skills necessary to secure, operate, maintain and troubleshoot OPA systems. Workforce development also ensures that the organisation fully realises the benefits of its investment in OPA. In addition, there are also podcasts and business guides that can be accessed on the opengroup.org site.²

Testing and validation

Rigorous testing and validation are essential before fully deploying OPA solutions in a production environment. Enterprises should conduct comprehensive testing to identify and rectify any glitches or compatibility issues. This will guarantee that the OPA ecosystem performs reliably and meets the required performance benchmarks.

There are a number of tests that can be carried out to identify possible issues and guarantee a seamless project completion. While robust testing and more traditional FATs (Factory Acceptance Tests) are suggested at this time, this effort will be reduced as more products and certifications become available.

A journey of continuous improvement

OPA implementation is not a one-time endeavour; it is an ongoing journey. Enterprises must establish mechanisms for continuous improvement and adaptation. Regular assessments of OPA’s impact on operations, along with feedback from stakeholders, enable enterprises to fine-tune their implementation strategy and optimise system performance to meet their specific outcome goals.

Conclusion

Open Process Automation presents a transformative opportunity for enterprises seeking to modernise their industrial process automation systems. By embracing open standards, interoperability, and modular design principles, organisations can achieve enhanced operational efficiency, flexibility and scalability. The journey to OPA implementation requires a thorough understanding of the framework, strategic planning, collaboration with the right partners, and a commitment to continuous improvement. As enterprises navigate the complexities of OPA adoption, they position themselves for a more agile and competitive future in the realm of industrial automation.

^{1. Smith J 2023, ‘Understanding Open Process Automation’, Automation.com, <<https://www.automation.com/en-us/articles/january-2023/understanding-open-process-automation>>

2. The Open Group 2024, Open Process Automation Forum, <<https://www.opengroup.org/forum/open-process-automation-forum>>}

Image credit: iSitock.com/Gri-spb

Five common mistakes in industrial temperature monitoring

Tue, 17 Feb 2026 00:00:00 +1100

In industrial production, temperature and humidity play a bigger role than many realise. They influence how materials behave, how stable processes run, and whether products meet the required quality standards.

Even small changes in ambient conditions can have serious consequences. Materials might react differently, production steps can get disrupted, and the final product may no longer meet specifications.

Yet mistakes in environmental monitoring happen more often than expected. The following five examples show the most common pitfalls when monitoring temperatures in industrial environments — and how to avoid them with the right approach.

Mistake 1: Skipping regular calibration

Temperature sensors, though highly reliable in design, are subject to drift. Drift can be gradual or an abrupt shift in the measured value, and can arise from several mechanisms: mechanical strain such as vibration or constriction of sensing wires; thermal cycling that induces repeated expansion and contraction of the platinum element; contamination of the sensor surface with foreign atoms; or moisture ingress that alters insulation resistance. Depending on the cause drift can be temporary, occurring only during the process, or permanent, irreversibly changing the calibration baseline and detectable only through recalibration.¹

Longitudinal studies confirm that drift is not merely a theoretical risk but an observed reality. Investigations of industrial platinum resistance thermometers have shown that fewer than 15% maintained calibration stability within ±0.005°C after thermal treatment and handling, while the majority exhibited measurable shifts, often linked to strain or humidity exposure.² Such findings demonstrate why drift is considered an unavoidable characteristic of temperature sensors, regardless of manufacturing quality.

Given this inherent instability, calibration intervals play a decisive role in maintaining measurement reliability. While some operators extend intervals to reduce cost or downtime, evidence suggests that recalibration should be performed every 12–24 months, depending on application and environmental stress factors.³ Intervals longer than two years are associated with a markedly higher risk of undetected drift beyond acceptable tolerances. In highly sensitive production environments, where even deviations of a few hundredths of a degree can affect process stability, annual or shorter intervals are recommended.

Operational concerns about downtime during calibration are valid but solvable. Modern hot-swap capable sensors allow replacement of probe modules without interrupting ongoing measurements, thereby enabling regular calibration without data gaps or production stoppages. By combining systematic calibration schedules with drift-mitigating hardware strategies, plants can ensure that temperature monitoring remains a reliable foundation for quality assurance and regulatory compliance.

Mistake 2: Incorrect installation

Even the most advanced monitoring system cannot deliver accurate results if it is installed in the wrong place. A common mistake is mounting a sensor in an area that does not represent the actual conditions in the monitored space, resulting in poor measurement accuracy and even distorted datasets.

For example, placing it directly under an air conditioning outlet or too close to a heating source will produce readings that are not representative of the room as a whole. In storage areas, sensors are sometimes fixed too high or too low, causing them to miss the actual temperature range relevant to the products. The result: deviations go unnoticed until they cause quality issues.

Another mistake is sensor placement without account to large machines, metal structures and dense shelving. Such obstacles can create signal shadows that interfere with data transmission, which leads to incomplete data or delayed transmissions.

The third mistake is often the reason for the previously mentioned ones. Temperature monitoring is often not considered early enough in the planning of a facility or production line. Without integrating it into the infrastructure design, sensors may end up in suboptimal locations, or cabling routes may be impractical.

To avoid sensor placement issues, it is important to carry out detailed temperature mapping before installation. This process identifies warm and cold spots, ensuring sensors are placed where they reflect the actual ambient conditions. Communication mapping (eg, radio mapping) is also advised in order to guarantee efficient data transmission between the data loggers and the gateway. Expert support services during installation can provide onsite mapping and placement planning, from the project phase through to commissioning. This includes determining optimal sensor and gateway locations and selecting the right mix of wired and wireless solutions to match local conditions and IT security requirements. The result is a monitoring set-up that works reliably from day one.

Mistake 3: Poor alarm management

An alarm is only useful if it reaches the right person in time and is acted upon. In practice, this often fails due to unclear responsibilities, especially during shift changes. If an alarm is triggered shortly before the end of a shift, it may be passed on informally to the next operator — and sometimes goes unaddressed for hours.

Outdated user interfaces make matters worse. Many systems still rely on cluttered, decades-old layouts that obscure critical information. Modern, well-structured dashboards shorten training times and help staff react faster. Visual tools, such as traffic-light indicators, provide an immediate overview of system status and highlight where action is required.

Relying solely on email notifications is another weak point. Inboxes in industrial environments are often overloaded, and alarm messages can easily get buried. Direct alerts — for example via SMS or push notifications — ensure that critical warnings stand out and reach the right person without delay.

An effective monitoring system combines a clear, intuitive interface that makes alarms easy to recognise with flexible notification options adapted to site requirements. Depending on the set-up, alerts can be shown via visual indicators, sent as emails, or delivered directly to mobile devices through SMS-based or push notifications — ensuring they are noticed and acted upon immediately.

Mistake 4: Missing system integration

With the rise of system automation, production facilities have evolved into a patchwork of isolated solutions. A facility nowadays will typically have a building management system, an inventory management system, and an environmental parameter monitoring system, among other things. Each works independently, with different interfaces and even different data formats. This fragmentation creates silos as there is no data integration between systems.

Now the opportunity arises to fully exploit stored data by choosing systems capable of sharing data among themselves or exporting data to other platform (eg, using APIs or webhooks). An integrated monitoring landscape offers a clear advantage. It gives the possibility to visualise all parameters in one place (eg, PowerBI), improving cost efficiency while cooling a room by sending pre-alarm warnings to the building management system to control HVAC accordingly, or combining inventory data (eg, from an inventory management system) with monitored storage conditions, further improving product quality assurance.

Choose a monitoring system with integration in mind. Modern platforms offer webhooks and APIs to connect with existing infrastructure, as well as tools that visualise all relevant parameters at a glance. The result is full transparency, faster decision-making, and greater efficiency in maintaining process stability.

Mistake 5: Ignoring data redundancy

For industrial production environments, data redundancy can be a decisive factor when choosing a monitoring system. Relying solely on direct data storage on a server leaves a facility dependent on a continuous internet connection. If that link fails, so does the data flow — and in the worst case, critical records are lost entirely.

The consequences can be costly. Imagine a long-running test in product development or emissions analysis. If the internet connection drops midway and the system has no backup, hours of data may be gone. Without complete records, the test results cannot be validated, and the entire process has to be repeated. In industries with strict quality requirements — from battery production to cosmetics manufacturing — such interruptions can mean missed deadlines, wasted resources, and compliance risks.

The solution to this challenge is simple: redundancy. Robust redundancy strategies eliminate this risk by ensuring data is captured and stored at multiple points. Even if one system layer fails, the information remains safe and accessible.

This includes buffering data locally at the logger, storing it in a base unit independent of the server, and maintaining a secure central database. Alarm redundancy is equally important, making sure alerts are sent even if the primary channel is unavailable.

Advanced monitoring systems are designed with triple data redundancy: data is stored on the data logger, on the base unit and on the server. This approach can support both cloud and self-hosted (on-premise) monitoring systems, especially in facilities with strict data security policies, ensuring that no information is lost, and that monitoring remains uninterrupted under any network conditions.

Conclusion

In industrial production, effective temperature and humidity monitoring is more than just installing sensors. It requires regular calibration, correct placement, clear alarm processes, integrated systems and robust data redundancy. Addressing these points not only protects product quality and compliance but also reduces downtime and costly rework.

^{1. International Electrotechnical Commission 2008, IEC 60751: Industrial platinum resistance thermometers and platinum temperature sensors, Section 5.4.

2. Mangum BW, Furukawa, GT 1984, ‘Stability of Small Industrial Platinum Resistance Thermometers’, Journal of Research of the National Institute of Standards and Technology, vol 89, no 6, pp 795–801.

3. Kowal D, Nwaboh, J et al. 2020, ‘Long-term stability of meteorological temperature sensors’, Meteorological Applications, vol 27, issue 5 2020, pp 12–15.}

Image credit: iStock.com/nimis69

Cyber risk is rising faster than Australian manufacturers can respond

Fri, 13 Feb 2026 00:00:00 +1100

Manufacturing is vital to Australia’s economy, but the growing risk of cyber attacks poses a significant threat to the sector’s operations. Globally, manufacturing faced the highest number of attacks during the last three years, accounting for 25.7% of all incidents. As Australian companies continue to embrace the digital transformation megatrend of smart manufacturing and AI to remain competitive, their attack surface has continuously expanded at a rapid rate.

We are now at a point where many Australian manufacturers simply can’t keep up. New data obtained by the ABC under freedom of information laws revealed some manufacturing and mining organisations are taking up to two years to notice and report breaches to authorities, prompting concerns about how secure our critical infrastructure really is.

Why is manufacturing more at risk than other sectors?

Industrial control systems (ICS) and other equipment that was once isolated from the internet are increasingly being connected as manufacturers adopt new smart manufacturing capabilities. These devices are now exposed to the same threats as their IT counterparts, introducing new risks to manufacturing environments that hackers are seeking to exploit.

Cyber attacks on manufacturers do more than disrupt daily operations. They can affect production quality, create costly operational downtime, and even jeopardise public safety. Therefore, it’s imperative that cybersecurity moves from being an afterthought to an operational necessity.

The most common attack vectors

As seen with the Jaguar Land Rover cyber attack, threats to manufacturers are on the rise and can literally bring businesses to a halt. Ransomware is leading the way, going from a nuisance affecting SMBs to a systemic issue threatening critical infrastructure. From 2024–25, the manufacturing sector experienced a 61% surge in ransomware attacks — the most of any critical industry. Unfortunately, hackers know manufacturers face intense pressure to maintain production, making them more likely to pay ransoms.

One of the most common attack vectors is remote access connections. These insecure third-party connections, VPNs and remote access tools used by contractors and vendors are easily exploitable. Furthermore, legacy IIoT devices (which often remain unpatched for many months due to the high cost of replacing them) are another easy target. Improperly segmented networks also lead to a litany of issues following a breach, causing a ripple effect across the entire network.

A multi-layered approach to cybersecurity is critical

Manufacturing environments are inherently complex, so protecting them requires a multi-layered approach that addresses organisational and technological challenges.

Step one is maintaining a comprehensive asset inventory of all devices and communication pathways — a must-have for industrial cybersecurity. Additionally, implementing an exposure management program that accounts for asset complexities and unique governance is mission-critical.

Step two is network segmentation. Dividing the enterprise network into isolated zones dramatically reduces the blast radius of an attack, but this division must be done in line with manufacturing protocols like Modbus and EtherNet/IP.

Step three is secure remote access. While remote maintenance on OT asset-heavy environments saves considerable time and money, to reduce the risk of a breach, organisations must choose a secure access solution with granular access controls, multifactor authentication and time-limited access windows.

The heightened level of cyber risk facing Australian manufacturers is unlikely to die down anytime soon, so it’s time for the industry to collectively rethink its approach to cybersecurity.

*Leon Poggioli is Vice President ANZ at cybersecurity company Claroty. Leon’s mission is to help Australia’s critical infrastructure and industrial organisations on their journeys to discover, assess and protect their entire cyber-physical infrastructure.

Top image credit: iStock.com/narvo vexar

Electric actuation: a gamechanger for upstream processes

Thu, 12 Feb 2026 00:00:00 +1100

The International Energy Agency has warned that the oil and gas industry needs to reduce its emissions by 60% by 2030 to align itself with a global rise in temperatures of just 1.5°C.¹ Fortunately, it believes the sector is well placed to scale up some crucial technologies for the clean energy transition.

Currently, methane is released at wells when gas is vented directly to the air, when flaring to burn off the gas is incomplete, or through leaks. But these emissions can be reduced by more than 75%² with simple solutions such as leak detection, repair programs, and upgrading leaky equipment to include electric actuators.

Methane is also a valuable resource that could be sold as natural gas, converted to fuels, used for chemical production or stored underground. More than 260 billion cubic metres (bcm) of natural gas is wasted through flaring and methane leaks globally. With the right policies and implementation, around 200 bcm of additional gas could be brought to market.

Improving on traditional valve technologies

In upstream oil and gas production, control valves have historically been operated by pneumatic diaphragm actuators that use the well-stream gas as their motive power, releasing methane every time the valve is stroked. To reduce these emissions some operators have now replaced well-stream gas with air compressors, but these require a large amount of energy.

Electric actuators do not vent, and many provide one-piece actuation solutions, which reduces the risk of failure compared to the typical pneumatic solution involving multiple pieces of equipment — where each of these parts can suffer from air quality fluctuations, temperature variations and other environmental factors.

Electric actuators are less susceptible to these influences and more energy efficient, since they only consume electricity when in operation — making them suitable for in-field solar powered infrastructure in remote locations. They provide the required torques and thrusts while operating at the necessary speeds for choke and process control valves. In addition, they offer the highest resolution output and modulating duty for accurate pressure and flow rate control.

Improving energy efficiency and reducing costs

Electric actuators are suitable for upstream applications such as gas metering, production trees, processing, saltwater disposal and gas lift systems. Many come with fail-to-position options that automatically return valves to a predetermined position in case of power loss or emergencies, enhancing safety and preventing potential damage to equipment.

In-field interventions can also be rapid and simple, whether carried out remotely, in control rooms or by physical interaction with the actuator.

Using self-contained electric actuators instead of pneumatic solutions not only helps to reduce methane emissions but results in cost savings and increased operational efficiency. Electric actuators also feature user-friendly interfaces and software tools that simplify the commissioning process, making them a perfect solution for valve applications in the oil and gas industry.

Data logging and asset management

Intelligent actuators are designed to not only provide reliable and repeatable performance in the challenging environments of remote oil and gas wells — they can also monitor temperature, torque and voltage to ensure the unit’s integrity and operating performance, resulting in a longer product lifespan.

Intelligent electric actuators with data logging can capture a large amount of data, such as the number of valve operations, alarms, valve torque profiles and unauthorised operation attempts. Monitoring valve behaviour enables them to identify patterns indicative of impending malfunctions — a predictive capability that allows maintenance teams to address issues before they cause disruptions, saving time and money on repairs, and helping to prevent unplanned downtime.

Intelligent valve actuators can also adjust valve positions based on real-time operational conditions. This helps to minimise energy consumption and reduce emissions, while also helping to prevent leaks and other safety hazards.

Replacing pneumatic actuators

A huge amount of oil field infrastructure still includes isolation valves, choke valves and process control valves operated by well-stream-gas-driven pneumatic diaphragm actuators. As mentioned, these valves constantly release methane but another challenge is ensuring the gas is dry enough to prevent system failure from condensation.

The right electric flow technology can significantly increase efficiency and uptime, while reducing emissions in line with the aspiration of being among the industry’s lowest methane emitters. Every valve in an oil field can be replaced with electric actuators that result in no emissions.

Electric solutions can play an important role in controlling the flow throughout the oil and gas value chain and are critical in upstream applications. Valve actuators are used for all types of valve operation including metering, processing and isolation duties, while fail-safe flow control solutions are specified on safety-critical systems such as oil and gas storage tanks.

The benefits of electric actuators

The benefits are evident across upstream applications.

Production trees

The production tree (or Christmas tree) is an assembly of valves, spools and fittings that regulate the flow of oil or gas from a well.

In the event of overpressure, a fail-safe/shutdown valve installed at the upper wellbore known as a surface safety valve (SSV) is used to protect the production systems. A production choke valve also controls the flow of well fluids being produced and regulates the downstream pressure in the flowlines.

Electric actuators are suitable for advanced production choke valve actuation, with proportional control, high accuracy and low power consumption, while modular electro-hydraulic actuators combine the simplicity of electrical operation with the high torque/thrust and fail-safe fast-action capabilities of hydraulic high-pressure control needed for failsafe operation of the SSV valve.

Production processing

Electric control valve actuators are an advanced and energy-efficient solution to replace leaky and energy-inefficient pneumatic diaphragm actuators. They are suitable for dump valves and back-pressure control valves, commonly used in upstream production processing applications. Such actuators not only help achieve net-zero emissions with a solar-powered 24 VDC supply option but also help reduce overall lifecycle costs compared to the instrument air actuator alternative.

Gas metering and LACT skids

Natural gas production metering and lease automatic custody transfer (LACT) for oil production metering are two crucial aspects that connect upstream operations to midstream gathering infrastructure. The pipelines and valves used in midstream operations are usually larger and require higher torque/thrust ranges for valve actuation than those used in upstream production processing infrastructure.

Multiple flow control systems operate together on a custody transfer metering skid to ensure low measurement uncertainty and high metering accuracy. The flow control on metering skids must be highly accurate and reliable and always provide safe valve operation. To automate large control valves with high-pressure ratings, a high-output electric actuator can deliver increased linear thrust and stroke length.

Electric high-torque/thrust valve actuators can be utilised without the complexity and cost of a pneumatic supply, and are capable of high duty cycles suited to the requirements of LACT valve actuation.

Gas lift systems

When extracting oil from underground wells, a gas lift system is used to lift the fluids to the surface. This system works by injecting high-pressure gas into the well to reduce the density of the fluids and create a ‘scrubbing’ effect that lowers the pressure at the bottom of the well, allowing the fluids to flow more easily.

A reliable and adequate supply of high-quality lift gas is required for the gas lift system to work correctly. A control valve modulates the flow and pressure of the gas being injected into the well. Electric actuators are also suitable for this application, designed to operate continuously and precisely, making them ideal for continuous modulating applications like gas lift systems.

Saltwater disposal systems

Produced water is the largest liquid produced in the oil and gas industry. The water from the well can be 4–5 times the volume of produced gas or oil from the same well. This water is then transported to recycling tanks or saltwater disposal wells through an intricate gathering line network.

The entire system is fitted with several actuated valves that ensure the safe and efficient flow control of produced water. Most control valves in the constructed water infrastructure require a high degree of controllability to prevent water hammering.

Additionally, back-pressure control valves must operate with high-frequency modulation duty to ensure optimal performance of water injection pumps. Many electric actuators offer adjustable speed, including a slow mode for precise positioning, high accuracy, high-resolution micro-step movement, and flexible torque/thrust protection.

Conclusion

Intelligent, electric actuation is rapidly becoming a necessity for upstream oil and gas processes as the need to reduce harmful emissions and costs becomes more pressing. Electric actuators are emission-free and provide the necessary capabilities with low energy consumption suitable for remote solar-powered applications and the data logging and intelligence for modern asset management and predictive maintenance.

^{1. International Energy Agency 2023, ‘The Oil and Gas Industry in Net Zero Transitions’, World Energy Outlook 2023, <<https://www.iea.org/reports/the-oil-and-gas-industry-in-net-zero-transitions>>

2. International Energy Agency 2023, Global Methane Tracker 2023, <<https://www.iea.org/reports/global-methane-tracker-2023>>}

Image credit: iStock.com/Leonid Eremeychuk

Seeing with AI: flexible navigation in dynamic environments with Visual SLAM

Tue, 10 Feb 2026 00:00:00 +1100

Automated materials handling and transport in logistics and manufacturing centres, as well as in major retail facilities, offers vast benefits, including increased efficiency, profitability, safety and flexibility in terms of labour fluctuations. Nevertheless, the vast majority of tasks and processes that could benefit from mobile robotics are still executed manually. However, the trend in many industries towards mass customisation — in other words, producing smaller lots of greater variety in shorter product lifecycles — is affecting manufacturing as well as warehousing and logistics operations, which calls for increased use of flexible robotics.

This low adoption rate can ultimately be linked to the fact that most mobile robots sold today rely on expensive, legacy fixed-floor installations or 2D laser scanners — devices that can perceive the environment in only a narrow slice, as if looking through a mailbox slit. To know its location and how to navigate to its destination, a vehicle that depends on such outdated technologies requires a structured, static environment and can perform only very simple, precisely defined tasks — the opposite of the way modern warehouses, production plants and big box stores operate.

Automation solutions based on camera vision and powerful AI models overcome these limitations, as they offer a much richer and more intelligent perception of the environment. Automated vehicles equipped with such capabilities can easily navigate dynamic environments, execute complex tasks, and work in unstructured spaces shared with people. This technological progression is needed to bring autonomy to warehouses and to all other industries that depend on manual labour today.

Visual AI for localisation and navigation

The solution that makes the above-mentioned capabilities possible is called Visual Simultaneous Localisation and Mapping (Visual SLAM). Using cameras, cutting-edge computer vision algorithms and AI models to perceive their surroundings, Visual SLAM-equipped robots build rich 3D maps of the environment to precisely locate themselves within it. Visual SLAM’s advantages are:

No costly infrastructure installations required; the environment is perceived as it is and in real time.
Functions in dynamic environments such as warehouses where the layout may change frequently and where people and objects are constantly moving around.
Extremely robust: perceives visual characteristics of surroundings such as light intensity, contrast and shapes, thus avoiding confusion and allowing mobile systems to work alongside people and moving objects.
Allows robots to navigate on ramps and uneven floors. The number of mapped environment elements is so high that parts of the environment can be changed without degrading localisation quality.
Finally, observing the environment in 3D at all heights enables robots of different kinds to collaborate, exchange data and use the same map of the environment to localise, thus forming swarm intelligence.

Naturally, these advantages add up to considerable market potential. For instance, according to DHL Logistics Trend Radar¹, sales of mobile robots in the logistics industry are projected to grow from US$7.11 billion in 2022 to $21.01 billion by 2029. This growth is facilitated by a technology shift from 2D laser scanner autonomy to 3D Visual SLAM autonomy, which is significantly advancing mobile robots in various applications and boosting their rate of adoption (Figure 1).

Figure 1: 3D visual autonomy’s potential compared to alternate technologies. Source: DHL.

A 3D VSLAM system’s hardware consists of an AI-enabled compute unit that runs proprietary algorithms and a set of cameras that perceive the environment. Tight integration of the hardware and software enables optimal performance of the underlying software and AI models.

How vision-based positioning works

3D Visual SLAM software and hardware reliably estimate the 3D position, orientation and velocity of mobile robots. Due to sophisticated computer vision algorithms and AI models, these systems can reliably and very precisely estimate their own position even under the toughest conditions. Built-in AI algorithms never stop learning and with each hour of operation, they can leverage new experiences to update 3D maps of the environment, thus reflecting changes in the operational space and further enhancing the system’s long-term robustness.

Visual SLAM technology serves use cases ranging from single robots working on their own, such as a scrubber-drier robot in a medium-sized grocery store, up to fleets of hundreds of mobile robots of different types in large manufacturing plants. In the latter case, units can intelligently interact and learn from each other by building a joint 3D visual map that leverages AI models and the latest data collected from the environment.

A complete solution for autonomous navigation

3D Visual SLAM systems enable mobile robotics platforms for materials handling, manufacturing, professional cleaning and other service robotics applications with complete navigation and obstacle avoidance capabilities.

One key differentiator is the ability to cope with challenging and unstructured environments, such as busy warehouses or crowded airports. The AI-based perception capabilities make mobile machines completely autonomous, without the need for costly human-in-the-loop interventions.

With on-board Edge AI, robots can plan their best and most efficient paths. They can operate as single entities or, as members of a fleet, can share map and traffic information with each other, accepting orders and commands from third-party fleet management systems (FMS) using modern and accepted standards such as VDA5050.

Fusing information to maximise efficiency and safety

To work effectively in as many environments as possible, the 3D Visual SLAM system must be able to orchestrate up to eight cameras and perform precise synchronisation, automatic gain control and camera exposure control to maximise the information content of images acquired by the cameras, regardless of lighting conditions. Visual AI workflows must execute directly on the unit, with minimal latency or computational burden while benefiting from precisely tuned camera image signal processing (ISP). The associated proprietary hardware design, which addresses the technological challenge of transmitting high-quality synchronised camera imagery over long wiring, allows OEMs to position up to eight cameras anywhere within the chassis of the vehicle, enabling 360° surround view coverage.

AI-powered algorithms also enable robots to precisely estimate their position. This is achieved by extracting discriminative elements from images, such as, for example, the corner of a window, and using these elements to build a representative yet compact 3D model of an environment. To achieve a high level of reliability and enable operations regardless of lighting conditions or perspective, detection and association of discriminative elements is made using AI-based models that run on fast on-board graphic processing units (GPUs).

In order to effectively use the above-mentioned sensor types, robotics systems require accurate calibration. The processes underlying calibration are complex yet essential in achieving both robot positioning and autonomous navigation. Calibration makes it possible to identify internal camera parameters, such as focal length and distortion of lenses, as well as the spatial transformations between different sensors, for instance inertial measurement units, ultrasonic sensors and time-of-flight cameras.

People first

Naturally, all the above-mentioned capabilities not only add up to precise and accurate interpretations of environments but also to a high level of safety for people. AI-based computer vision techniques leverage the rich information content of visual images in order to obtain not only 3D distance information, but also much richer scene understanding, such as the detection of people and prediction of their relative motions. Through the combination of AI-based detection methods with 3D geometrical rules, robots equipped with this technology can detect and track people using cameras all around a vehicle. This crucial scene-based information enables the technology to adapt vehicle behaviour accordingly, for example, by allowing vehicles to swiftly evade static obstacles that are blocking their path but drive more cautiously in the presence of people or come to a complete halt in front of people. Modern AI0-based Visual SLAM technology can use multiple stereo cameras for detecting and tracking people to ensure safe and human-friendly navigation in crowded industrial environments.

Table 1: Compared with alternative sensor technologies, Visual SLAM technology offers a range of advantages. Source: ABB Review.

Lifelong Visual SLAM

One challenge for any type of positioning system is that environments change over time. This may be due to changing seasons or lighting conditions, changes in factory floor layouts, or simply in warehouse or shop inventories. Without smart algorithms, it is impossible to safely deploy mobile robots in dynamic environments. To master this challenge, AI-based algorithms enable the building and maintaining of always-up-to-date maps of environments. These maps incorporate data from multiple conditions, such as during cloudy and sunny weather, bright and dark lighting situations, and changes in the elements present in an area. These changes are detected automatically and swiftly incorporated into a lifelong map without any intervention.

The path to human-like intelligence

As the number of mobile robots in production centres, warehouses, logistics centres and retail facilities grows, complexity will increase. But to efficiently manage this complexity, mobile robots will need to be even simpler to set up and operate than they are today. Ideally, as they become increasingly capable of harnessing generative AI and large language models, they will be able to set up and optimise their configurations and interactions on their own. At that point, based on a few simple instructions from human operators, they will be able to autonomously explore the available space and plan the paths and flows of materials through that space. This revolutionary shift will increase the overall robustness and throughput of operations — and will likely generate unprecedented additional value.

^{1. DHL 2024, Indoor Mobile Robots: Trend Overview, <<https://www.dhl.com/au-en/home/innovation-in-logistics/logistics-trend-radar/amr-logistics.html>>}

This article is based on an article previously published in ABB Review.

Top image: Supplied

The US market opportunity Australian engineering firms need right now

Wed, 21 Jan 2026 00:00:00 +1100

Australia’s infrastructure pipeline is filled with important megaprojects. Worth $242 billion, the pipeline spans critical climate projects, energy transition upgrades, and major developments like those required for the Brisbane 2032 Olympics. However, this national ambition is being hindered by a deepening skills gap.

The numbers are stark. Australia is projected to be short of approximately 200,000 engineers by 2040. This dramatic shortfall is already impacting project delivery. Infrastructure Australia sounded the alarm in November, highlighting the industry is currently short of 141,000 workers needed to deliver the five-year Major Public Infrastructure Pipeline.

The required scale of investment and construction simply cannot be met by the domestic workforce alone. This is where the US market presents a solution.

The US graduate surplus

In the US, 141,000 students graduate with a bachelor’s degree in engineering every year. With only about 5500 of the 765,000 engineering roles advertised in the US targeting new graduates (as of 21 January 2026), the country is struggling to absorb its influx of young talent. By actively targeting and recruiting this pool of skilled, yet underemployed, talent, Australian firms can secure the staffing levels needed to keep major projects — including essential energy, transport and housing work — on track and on budget.

Although a few recent visa changes have tightened pathways for fresh graduates, firms with established global mobility programs can still leverage targeted recruitment to bring US talent across borders. A coordinated approach to hiring, utilising employer sponsored pathways, could drive these US graduates to join Australian firms. This partnership wouldn’t just fill a gap; it would inject fresh ideas and perspectives into Australia’s engineering culture, ensuring the nation’s most critical infrastructure projects are completed at the highest possible quality.

A proposal for Australian firms

To capitalise on this recruitment opportunity, firms need to extend their standard hiring practices and incorporate a US talent acquisition strategy. Here are two tactics firms can rely on:

Targeted university recruitment: Firms should look to recruit directly from US universities. By establishing a presence or partnerships with colleges known for their engineering departments, Australian firms can create a structured system that adds promising graduates to their talent pipeline.
Digital headhunting: We live in a digital world, so targeting online US job boards and utilising LinkedIn targeting is essential. When contacting graduates online, there is a massive opportunity to lean on the warm-weather Australian lifestyle and the opportunity to work on critical mega projects to attract US engineers who are struggling to find relevant experience at home. Rather than framing the move as a job opportunity, it should be positioned as a chance to build unique life and work experience.

Building for the future

We need to ensure that ambitious infrastructure plans are delivered on time and on budget using the right skills. This means the time for Australian engineering firms to act as global operators is now.

This partnership wouldn’t just fill a gap; it would inject fresh ideas and perspectives into Australia’s engineering culture, ensuring the nation’s most critical infrastructure projects are completed at the highest possible quality. This isn’t just a future ambition: looking at our own Projectworks data, we see our customers growing their global footprint every day, proving the industry is ready to rise to this challenge and take the steps necessary to turn megaproject plans into reality.

*Mark Orttung is CEO of project management platform Projectworks and was formerly CEO of US consultancy Nexient and COO of Bill.com. He is based in the US and works directly with Australian engineering firms like MBB, Oculus and BDO.

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2026 Thought Leaders: Jimmy Martin

Tue, 13 Jan 2026 00:00:00 +1100

What growth opportunities do you predict for your industry in 2026?

The biggest growth opportunities in waste and recycling lie in the intersection of artificial intelligence and the circular economy. AI enables our customers to use resources efficiently, automate processes, and create smarter systems for waste and recycling management. By leveraging AI, our customers ensure that products and materials circulate at their highest possible value, extending their life cycle and minimising environmental impact.

In 2026, I see more operators embracing the power of AI-driven analytics for predicting material flows, automating sorting, and creating more transparent supply chains. This is a combination of technology and sustainability which not only reduces costs but also unlocks new business models and revenue streams that thrive on efficiency and environmental responsibility. AMCS customer Auckland Council reaffirmed its commitment to eliminating landfill disposal by 2040 by adopting AMCS Vision AI to replace manual identification and reduce contamination of recycling collections. The council has recognised that this technology is a key enabler of its zero-waste strategy.

In the face of current global uncertainty, what are the three biggest challenges or threats facing your industry?

There are three big challenges we’re watching closely. Firstly, economic ups and downs like market swings and inflation make it harder to plan and invest in circular infrastructure and new technologies. But they also remind us why these investments pay off over time. Circular systems keep resources circulating within communities, providing stability and making them less dependent on volatile global supply chains.

Second, regulations are continuously changing. This can feel complicated and tiring but it’s a sign we’re moving toward a more consistent approach to tackling climate and waste, which is a positive step. Sustainability isn’t just about ticking boxes, it’s a performance strategy that improves operations while meeting environmental goals, delivering real business results.

The third challenge is the rapid rise of AI. Up to 90% of AI projects fail to deliver real business value, usually due to poor planning, unclear goals, and integration complexity. That’s why AMCS is building agentic AI as a complete system: governance and compliance for trust, agentic layers for scalability, and ROI discipline to ensure every project drives measurable value.

What plans have you implemented to progress artificial intelligence solutions in 2026?

AMCS has been enabling waste operators with Vision AI, which is fully integrated into the AMCS Platform. This technology records overfill and contamination events and enters them into the platform workflow. A Vision AI dashboard then provides actionable, up-to-date insight on overfill and contamination trends and sources.

Vision AI also detects other exceptions like gas canisters, resulting in improved safety and financial outcomes for our customers.

We use AI machine learning when selecting parameters to optimise route planning and schedules, which is more accurate than a traditional consultant lead approach. A prime example of performance sustainability in action; optimising routes delivers significant fuel, driving time and emissions savings.

In 2026, we are also looking to adopt agentic AI automation in our core enterprise management system. Agentic AI is a growing type of AI that can autonomously reason, plan and act towards goals, making it promising for enterprise software as it supports workers in their daily tasks.

What’s your outlook for the industry in 2026?

The outlook for Australia’s waste and recycling industry in 2026 is one of transformation and opportunity. Major regulatory changes will reshape how businesses operate and national packaging reforms are moving toward mandatory design standards and extended producer responsibility that will push companies to adopt more sustainable practices. In New South Wales, for example, food organics and garden organics (FOGO) mandates will start applying to large businesses from mid-2026, accelerating the shift toward circular solutions.

At the same time, consolidation is picking up pace. I expect we’ll see more mergers and acquisitions as companies look to scale, optimise routes, and invest in advanced infrastructure. This trend is already visible with major players acquiring specialist waste firms to strengthen service offerings and improve efficiency.

Technology will be a game changer. AI and automation are moving from pilot projects to mainstream adoption, whether it’s smart bins, AI-driven sorting systems, or predictive analytics for fleet management.

Overall, 2026 will be about collaboration and innovation. Businesses that embrace regulatory change, invest in technology, and build strong partnerships will be well-positioned to lead in a market that’s becoming more competitive, more sustainable, and more data-driven.

Jimmy Martin is the CEO and co-founder of AMCS, headquartered in Limerick, Ireland. AMCS is a leading global technology provider in the waste, recycling and resource industries, and aims to help customers digitise their businesses to improve operational efficiencies, drive margin expansion, reduce business and operational risk, enhance their customer experience, and deliver environmental sustainability.

Top image credit: iStock.com/inkoly

Australia must rethink the foundations of industrial automation

Thu, 04 Dec 2025 00:00:00 +1100

Australia’s manufacturing sector is at a pivotal moment. Competitive pressure, sustainability expectations, energy costs and a new wave of digital tools are converging, exposing the limits of the automation approaches that have defined industrial production for decades.

Across industry, conversations are shifting. Manufacturers no longer ask whether they should adopt digital technologies. They are asking how to build an automation environment that can keep pace with them. The challenge is not ambition; it is architecture. This is where the sector’s next competitive advantage will be won or lost.

For years, industrial automation was built on systems designed for stability, isolation and long lifecycles. These systems served their purpose well, but they belong to an era where operational change was slow and integration requirements were simple. The technologies now reshaping manufacturing — from AI-driven optimisation to real-time analytics and low-latency decision engines — demand a fundamentally different level of flexibility.

The shift is already underway. Manufacturers want automation platforms that are open, interoperable and vendor-agnostic. They want to adopt AI tools without rebuilding entire control systems. They want access to the thousands of new industrial applications emerging globally, not to a fraction of them limited by proprietary interfaces. And they want the confidence that decisions made today will not restrict their options tomorrow.

This desire for openness is not philosophical; it is operational. When a modern facility needs to trial a new optimisation algorithm, integrate additional sensors or adopt an emerging AI model, the question should not be whether the system will allow it, but how quickly it can be done. That expectation, more than anything else, is driving the momentum towards flexible, software-led automation.

AI is accelerating this shift. What was once explored as a future possibility is being tested in live environments — improving yield, reducing waste and identifying process inefficiencies that traditional tools simply cannot detect. The interest is practical and immediate: manufacturers want AI that strengthens decision-making and provides operational certainty. But AI cannot flourish on rigid foundations. It requires systems that support continuous iteration, transparent data pathways and seamless integration across OT and IT environments.

Sustainability is reinforcing this direction. Manufacturers are facing growing pressure to reduce energy use and resource consumption, yet the most meaningful gains come from deeper operational insight — something only modern automation can deliver. When organisations talk about lowering emissions, they are increasingly talking about an automation strategy. Efficiency and sustainability are no longer parallel goals; they are the same outcome delivered through the same transformation.

Meanwhile, the speed of technological innovation continues to accelerate. Industrial software development has exploded, with thousands of new applications created daily. This pace will not slow, and manufacturers cannot afford systems that evolve at a fraction of that speed. To remain competitive, automation environments must be capable of absorbing innovation continuously, without the disruption of major hardware overhauls.

This is where Australia has a genuine opportunity. The technical fluency of local teams, combined with an openness to adopting new approaches, positions the sector to move faster than many of its global counterparts. What Australian manufacturers often lack is not capability, but the enabling architecture. When modernisation is approached not as a procurement exercise but as a strategic redesign of how automation functions in the organisation, the full potential of AI, sustainability gains and operational flexibility becomes achievable.

The real inflection point is that the future of manufacturing will not be shaped by who adopts the most technology, but by who builds the most adaptable systems. The winners will be the organisations that view automation not as a fixed asset, but as a living platform — one that supports constant evolution and embraces a broader ecosystem of tools, partners and innovation paths.

Australian manufacturing has a narrow but powerful window to lead. By shifting from rigid, hardware-bound systems to open, software-defined architectures, the sector can position itself at the forefront of global competitiveness.

The technology is ready. The capability is here. What’s needed now is a willingness to rethink the foundations and build automation environments prepared for a future that will not wait.

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The environmental impact of AI: a help or hindrance for industry?

Wed, 03 Dec 2025 00:00:00 +1100

Artificial intelligence (AI) is often framed as a technological silver bullet — able to squeeze inefficiency out of supply chains, predict failures before they happen and optimise scarce resources. Yet the same AI systems driving those improvements are themselves energy-intensive, thirsty for water (for cooling), and dependent on materials-heavy infrastructure.

For industries wrestling with sustainability challenges in water, waste and energy, the real question is not whether AI can help, but whether it will be deployed in ways that reduce net environmental harm.

Where AI helps: precision, prevention and systems thinking

First of all, we should consider how AI can help optimise environmental sustainability in various industries.

Among the strongest arguments for the use of AI are:

detailed sensing and prediction
fast optimisation of complex systems
automation that reduces human error and labour costs for repetitive, hazardous tasks.

Some examples of these capabilities have already found use cases in water, waste and energy-related systems.

Water and wastewater

Machine learning models coupled with IoT sensors can predict demand, detect leaks and optimise pumping schedules — all of which reduce unnecessary water withdrawals and energy used to move water.

In agriculture, AI-driven irrigation systems determine when and how much to water based on soil moisture, weather forecasts and crop models, cutting consumption while protecting yields.

Waste

Automated sorting systems powered by computer vision and robotics improve recycling rates: AI-powered sorting robots are capable of sorting up to 1000 items per hour, compared with humans who generally manage 80–100 items per hour.

AI can also assist by identifying and separating materials more accurately than manual sorting — reducing contamination that places limits on the success of recycling.

Energy

AI excels at forecasting renewable generation and demand, enabling smarter grid balancing, battery dispatch and demand response. This is one area where AI can have the greatest impact: taking into account numerous factors to optimise the electricity grid as load and other conditions change throughout the day.

Machine learning controllers in industrial energy management systems (EMS) can also be used to squeeze more useful work from the same inputs, cutting both energy consumption and emissions.

The problem with AI is its own resource footprint

Notwithstanding AI’s great potential in assisting with environmental sustainability, the sheer power of the computing infrastructure required to drive AI is pushing energy grids and challenging water resources in many parts of the world.

According to an Australian Government committee of enquiry in 2024, the “environmental impacts of artificial intelligence (AI) are significant and arise across the AI lifecycle, from the development and training of AI models; the deployment of AI systems for various uses in industry, business and society; and the building, decommissioning and renewal of the Information Technology (IT) infrastructure and equipment that support and comprise AI technology.”¹

Training and running large AI models requires vast computing resources, which consume electricity and produce greenhouse gas emissions depending on the electricity’s carbon intensity. Hyperscale data centres — the backbone of modern AI — also use significant water for cooling, sometimes in water-stressed regions. These infrastructure impacts introduce trade-offs: deploying AI to save energy in a factory (for example) could still increase net emissions if the compute and cooling footprint is large and powered by fossil fuels.

The global electricity consumption of data centres has grown by around 12% each year since 2017, according to the IEA, and companies such as Google, Meta and Microsoft have reported large emissions spikes over the past few years due to data-centre expansion, despite their net-zero pledges.

One thing is certain: although data centres currently account for around 1% of electricity consumption and around 0.5% of global carbon emissions at present, the International Energy Agency has predicted² that they will be the fastest growing consumers of electricity from now until 2030 (Figure 1).

Figure 1: IEA projections of electricity demand growth 2024–2030, TWh.³ For a larger image, click here.

The latest news: Google and Microsoft chasing nuclear energy

To counter the energy sustainability problem, and knowing the extent of the growing need for energy, tech giants Google and Microsoft recently announced that they will invest in the reopening of mothballed nuclear power plants in the US to feed their ever-growing energy needs.⁴

Google has announced a plan with NextEra Energy to restart the Duane Arnold Energy Center in Iowa, a nuclear power plant that closed in 2020. The company has signed a 25-year power-purchase agreement (PPA) to secure electricity for its expanding network of data centres. Meanwhile, Microsoft has partnered with the famous Three Mile Island Nuclear Generating Station in Pennsylvania — which was shut down in 2019 — under a new name, the Crane Clean Energy Center.

For countries such as the US that already have established nuclear energy infrastructure this may seem an obvious solution, but for countries such as Australia, the drive to renewable energy becomes even more important in sustaining AI infrastructure growth.

In August, Treasurer Jim Chalmers announced that the development of data centres was in the national interest, and others have suggested that Australia could become a ‘sustainable AI hub’ for the Asia–Pacific. However, the power-hungry nature of data centres poses major problems for the current energy grid in Australia, and is seen as one of the reasons for the slight downgrade of Australia’s climate target to a 62–70% cut in carbon emissions below 2005 levels by 2035 — slightly below the previous target of 65–75%.⁵

Net impact: the context matters

Whether AI is a net help or hindrance depends on context and design choices:

Energy sources: AI powered by grids dominated by renewables has far lower lifecycle emissions. In Australia, this reveals a problem: of the current 274 data centres in Australia, 149 are in Sydney and Melbourne: in the states with the least percentage of renewable energy generation.⁶
Model size and usage pattern: Smaller, efficient models carry far lower overhead than massive generative models. For individual organisations such as manufacturing sites, utilising their own smaller, business-specific AI models is both more reliable and trustworthy in the context of the business, and consumes far fewer resources than general-purpose AI infrastructure.
Size of the benefit: If AI reduces industrial energy use by 10–30%, the saving can outweigh the compute footprint, especially when smaller private models are used.
Location and water stress: Placing data-intensive infrastructure in water-scarce regions can create local sustainability crises. As the driest continent on Earth, this is an issue that Australia will also have to deal with at some stage in the near future, but there appears to have been far less discussion of this issue than of electricity consumption.

A balanced conclusion: AI is here to stay

It is now too late to be worrying about how AI may affect environmental sustainability — the AI juggernaut appears to be unstoppable already. However, the technology itself can offer tools to cut water consumption, raise recycling yields and improve energy system flexibility — all vital for sustainable industry and a sustainable grid.

While the environmental costs of training and operating large models are real, growing and unevenly distributed, the net outcome will be determined by choices: model architecture, where compute happens, which energy sources are used for power and whether industries commit to measuring and offsetting the full footprint.

For industry leaders, the pragmatic path is clear: harness AI where it delivers measurable resource savings, design deployments to minimise computation and water intensity, and push for clean energy and smarter cooling at the infrastructure level.

^{1. Australian Parliament 2024, Select Committee on Adopting Artificial Intelligence (AI): Final Report, Chapter 6: Impacts of AI on the environment, <<https://www.aph.gov.au/Parliamentary_Business/Committees/Senate/Adopting_Artificial_Intelligence_AI/AdoptingAI/Report/Chapter_6_-_Impacts_of_AI_on_the_environment>>

2. International Energy Agency 2025, ‘Energy and AI’, <<https://iea.blob.core.windows.net/assets/601eaec9-ba91-4623-819b-4ded331ec9e8/EnergyandAI.pdf>>

3. Gabbatis J 2025, ‘AI: Five charts that put data-centre energy use – and emissions – into context’, CarbonBrief.org, <<https://www.carbonbrief.org/ai-five-charts-that-put-data-centre-energy-use-and-emissions-into-context/>>

4. Australian Business Journal 2025, ‘Google and Microsoft Turn to Nuclear Power for AI Energy Needs’, 29 October 2025,<<https://theabj.com.au/2025/10/27/google-microsoft-nuclear-power-ai/>>

5. The Conversation 2025, ‘Power-hungry data centres threaten Australia’s energy grid. Here are 3 steps to make them more efficient’, <<https://theconversation.com/power-hungry-data-centres-threaten-australias-energy-grid-here-are-3-steps-to-make-them-more-efficient-266992>>

6. Data Center Map 2025, ‘Australian Data Centers’, <<https://www.datacentermap.com/australia/>>}

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Three strategies that will enable IIoT deployment

Fri, 21 Nov 2025 00:00:00 +1100

The Industrial Internet of Things (IIoT) has the potential to revolutionise Australia’s food and beverage sector, enabling production enhancements and driving more efficient processes. Yet, according to Beckhoff Technical Sales Engineer Rakitha De Alwis, local uptake still lags behind its international counterparts.

One major concern of adopting IIoT is opening up the factory to a potential cyber attack. These risks can be mitigated and should not be a deterrent. In many ways the benefits will outweigh the risks.

Another barrier is the perceived high cost of IIoT technology and deployment. Yet IIoT can be implemented in small steps, and hardware items have become more competitively priced over the years.

“A pilot project is not costly, especially for companies who have an internal IT team. In addition, manufacturers can make use of the varied free and open-source software solutions on offer, as well as the free trials offered by large enterprise solutions,” he said.

As more industry players make use of the technology, and deepen their understanding of its application and benefits, adoption of IIoT will accelerate.

“Globally, IIoT is growing in popularity and is expected to reach a value of over US$40 billion by 2033, De Alwis added. “It offers a host of benefits — from more efficient monitoring of cold chains and stock levels to predictive maintenance and on-demand manufacturing. The result is optimised consumption of raw materials, less wastage and — most importantly — enhanced energy efficiency.”

With its interconnected network of smart devices and sensors that collect, share and analyse real-time data, IIoT empowers manufacturers with insights that could impact the quality and quantity of production and, ultimately, the business bottom line. In addition, IIoT also addresses industry-specific challenges such as labour shortages and food safety regulations.

“Instead of relying on yesterday’s data, manufacturers using IIoT tech have access to real-time data, allowing them to rectify or optimise processes on the go, helping to prevent losses before they occur. The cost saving and quality improvement benefits are experienced not only by manufacturing businesses, but by their end consumers too.”

Getting started

De Alwis offers the following advice to speed up IIoT implementation in production facilities:

Refine your needs: Understand your processes and products thoroughly to pinpoint where your IIoT needs truly lie. Keep in mind that the tech is best applied to enhance processes or products that are already sound.
Start small: Identify one aspect of the business that can be improved with a pilot project.
Choose the right partners: Hire experienced professionals to support your IIoT implementation.

Overcoming challenges

A current hurdle for local industries is the scarcity of IIoT specialists.

“There won’t be a supply of these skills without demand from local industry. It would be beneficial for Australian universities and TAFE colleges to train up the next generation of technicians and engineers who can implement IIoT. As more and more of these skills will be required moving forward, specialists should be available and affordable to speed up the implementation of IIoT,” De Alwis added.

Looking ahead, he anticipates that that the market will embrace IIoT and many of the larger manufacturing companies have already implemented it to some degree.

“The Australian food and beverage manufacturing sector shouldn’t be left behind, especially as we rely heavily on our food and beverage exports. We won’t remain competitive if we are not up to date with the latest innovations,” he concluded.

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