Recently, I stumbled upon an insightful piece from Asian Downstream Insights titled A Guide to Industrial AI and Digital Transformation by Emerson’s Jonas Berge. Let me share with you the key takeaways, with a dash of Emerson’s expertise sprinkled in.
The article emphasizes that today’s refineries, petrochemical complexes, and other industrial plants are no longer just about core process control.
Now it includes improving safety, sustainability and reliability – over and above production – as plants must achieve operational excellence, without increasing their workforces. The scope of industrial automation and control systems (IACS) includes distributed control systems (DCS), safety instrumented systems (SIS), machinery protection systems (MPS), manufacturing execution systems (MES), and energy management information systems (EMIS) just to name a few. Industrial artificial intelligence (AI) plays an important part in all these automation and control systems to support production and maintenance, but also in the deployment of the automation and control systems themselves.
Jonas points out that no single AI tool fits all scenarios and advocates for a diverse AI toolkit. Whether it’s causal AI for understanding cause-and-effect relationships, machine learning for predictive analytics, or generative AI for innovative problem-solving, each has its niche. The optimum strategy supports multiple AI tools, ensuring you’re using the right tool for the right job.
One exciting application Berge discusses is the ‘chat’ functionality in advanced process control (APC) software.
For instance, by way of a ‘chat,’ an operator of advanced process control (APC) software can ask a ‘copilot’ what controller setpoints to set to meet a certain target like feed rate, and the copilot will respond to the operator with a few options to choose from. Instead of only using traditional simulation tools to try out different settings before applying them to the APC, the user interface is now a conversational assistant with natural language input and response.
Jonas dives into how industrial automation and control systems (IACS) have evolved—everything from DCS (Distributed Control Systems) to EMIS (Energy Management Information Systems). Here, industrial AI isn’t just an enhancement; it’s a necessity, driving everything from maintenance predictions to energy optimization.
A critical point raised is the need for new automation paradigms to support AI. This includes better data integration across various systems and leveraging wireless sensor networks to reduce manual data collection. AI solutions are designed to automate these processes, ensuring data accuracy and timeliness, which are crucial for AI efficacy.
He explored AI’s role in maintenance and emissions control. Predictive maintenance, optimizing cleaning schedules, and controlling emissions are areas where AI solutions are making tangible impacts, pushing toward not just efficiency but also sustainability.
To wrap up, Jonas shared a strong call for the industry to embrace a full spectrum of AI technologies.
A vendor with only one type of industrial AI tool will tend to force-fit that tool into all applications, like a “hammer in search of nails.” Working with a vendor that supports multiple AI tools can help operators avoid single-tool bias.
