Enhancing Efficiency and Safety in Industrial Plants through AI-Driven Dynamic Pattern Understanding
In today’s rapidly evolving industrial landscape, the need for efficient, safe, and proactive control systems in chemical and other plants has never been more pressing. With the advent of artificial intelligence (AI) technologies, specifically reinforcement learning-based AI, it is now possible to autonomously control and optimize the operation of such plants in a way that was previously thought impossible.
Recent developments in AI technology have demonstrated that it is possible to control operations beyond the capabilities of traditional control methods (PID control and Advanced Process Control) that often require the manual operation of control valves based on the judgment of plant personnel. This breakthrough has been achieved by integrating AI into plant management systems, enabling autonomous control of complex processes for extended periods.
One notable example of this is the successful use of AI to control the valves of a distillation column at the ENEOS Materials Corporation chemical plant in Japan for 35 consecutive days. This achievement has shown that AI can control processes that were previously performed manually, based on operators’ experience. This not only improves the efficiency of the plant but also helps to ensure safety by integrating the AI protocol into the plant’s existing control system, allowing for the deployment of safety interlocks and other safety functions at any time during the trial if needed.
The AI technology used in this case, the Factorial Kernel Dynamic Policy Programming (FKDPP) protocol, was jointly developed by Yokogawa Electric Corporation and the Nara Institute of Science and Technology (NAIST) in 2018. It is recognized for being the first reinforcement learning-based AI in the world that can be utilized in plant management.
Looking ahead, the development of an improvement point discovery AI algorithm is underway. This algorithm will aim to discover potential improvement points in multiple processes and autonomously identify problems by analyzing big data. When combined with problem analysis AI and autonomous control AI, such as the FKDPP algorithm, it will be possible to create an autonomous plan-do-check-act (PDCA) loop for continual process optimization.
These advancements in AI-driven dynamic pattern understanding and autonomous control have the potential to revolutionize the way industrial plants operate. By improving efficiency, reducing the need for manual intervention, and enhancing safety, AI can help to optimize operations and ensure the sustainability of the industry. This not only benefits the companies involved but also contributes to the broader goal of achieving energy savings and reducing CO2 emissions.
In conclusion, the integration of AI into plant management systems marks a significant step forward in the quest for efficient, safe, and proactive control in chemical and other plants. As AI technology continues to evolve, we can expect to see further developments that will enhance the capabilities of these systems and drive the industry towards a more sustainable future.
“AI: Turning the intricate into the intuitive.”
