The following article on warehouse automation is written by Kobus Vermeulen, direct sales executive, process automation at Schneider Electric. It is Part 2 of a two-part series.

Compromised DCS systems can result in unauthorised access. Freepik.com
Process optimisation – waste not want not
Industrial processes generate vast amounts of data. When incorporating AI and ML, DCS learns from the generated data, which enables it to, in a real-time, finetune control strategies. This dynamic adjustment of control parameters delivers improved process efficiency, reduced energy consumption, and minimised waste.
As an example, in a plant, the integrated AI system AI can adjust variables such as temperature, pressure, and flow rates based on real-time data, ensuring that the process operates at optimal efficiency which in turn saves on valuable energy, ultimately contributing to environmentally sensitive practices.
Anomaly detection – safeguarding operational integrity
Anomaly detection is another critical area where AI and ML are making 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, 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:
- Disruption of operations – cyber-attacks 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 within 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.