What is the body without a brain? An empty vessel, uncoordinated and purposeless. The same can be said about industrial operations, which require a distributed control system (DCS) to coordinate and control their process subsystems in real time. Like the brain, a DCS is a multitasking maestro, controlling and coordinating complex processes in a myriad of industrial settings, such as large manufacturing plants, providing valuable top-down control.
The DCS communicates with subsystems such as sensors and other data collection devices, interpreting production trends to make automated decision and send instructions to individual controllers, actuators, programmable logic controllers (PLCs), and other industrial equipment throughout the plant.
It is certainly a marketplace that is seeing considerable growth. According to research, the DCS market size was valued at over $23,28 billion in 2023 and is anticipated to reach $48,44 billion by the end of 2036. Key to this growth is the continued proliferation and acceptance of loT technologies involving the DCS. The number of connected loT devices worldwide is projected to reach nearly 76 billion by 2025.
Brainfood for the system
Considering the above, it is clear that the DCS is in a growing market. What if one then adds AI and machine learning (ML)? For one, they act as software-based brainfood for the DCS, optimising several key operations such as predictive maintenance, process optimisation, analytics and anomaly detection. A perfect example is predictive maintenance. Traditionally reactive, maintenance addresses issues after they occur or is based on a scheduled timeline, which often leads to unnecessary downtime or unforeseen breakdowns.
AI and ML algorithms, however, analyse historical and real-time data from DCS sensors to predict potential equipment failures before they happen. This predictive maintenance capability enables industries to schedule maintenance activities strategically, which in turn reduces unexpected downtime while optimising asset performance.
For instance, in the oil and gas industry AI algorithms monitor data from pumps, compressors and other critical equipment. By detecting subtle changes in vibration patterns or temperature, the system can forecast when a piece of equipment might fail, allowing maintenance teams to intervene early. This not only reduces maintenance costs, but also enhances operational reliability.
Process optimisation
Industrial processes generate vast amounts of data. When incorporating AI and ML, a DCS learns from the generated data, which enables it to fine-tune control strategies in real time. This dynamic adjustment of control parameters delivers improved process efficiency, reduced energy consumption and minimised waste.
As an example, on a plant an integrated AI system can adjust variables such as temperature, pressure and flow rates based on real-time data, ensuring that the process operates at optimal efficiency. This saves on valuable energy, ultimately contributing to environmentally sensitive practices.
Safeguarding operational integrity
Anomaly detection is another critical area where AI and ML are having a substantial impact. By integrating ML algorithms into a DCS, industries can identify irregularities in process variables, equipment behaviour or overall system performance. Early detection of these anomalies is crucial for maintaining operational efficiency and preventing potential safety hazards.
In a chemical processing plant, for example, an AI-driven DCS can detect deviations from normal operating conditions such as unexpected pressure spikes or temperature fluctuations. Upon identifying an anomaly, the system can trigger alarms or automatically adjust parameters to mitigate risks, ensuring the process remains safe and efficient.
Addressing cybersecurity challenges
The above are undoubtedly compelling benefits. However, as DCSs become increasingly interconnected they also become more vulnerable to cybersecurity threats. Primary concerns include cyberattacks, risks associated with legacy systems, interoperability concerns and insider threats. These cybersecurity challenges can significantly affect industrial process integrity and security by potentially causing a number of problems:
• Disruption of operations: Cyberattacks can disrupt industrial processes, leading to downtime, production losses and safety risks.
• Data breaches: Compromised DCS systems can result in unauthorised access to sensitive operational data, leading to confidentiality breaches and intellectual property theft.
• Safety risks: Cybersecurity breaches can impact the safety controls and protocols in industrial processes, potentially leading to hazardous situations.
To address these challenges, robust cybersecurity measures such as network segmentation, regular security updates, access control and employee training are essential to safeguard interconnected DCS systems and ensure the integrity and security of industrial processes.
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