Yokogawa and JSR use AI to control chemical plant autonomously

  • Industry News
  • May 13,22
A next-generation control technology that takes into account quality, yield, energy saving, and sudden disturbances
Yokogawa and JSR use AI to control chemical plant autonomously

Yokogawa Electric Corporation and JSR Corporation announce the successful conclusion of a field test in which AI was used to autonomously run a chemical plant for 35 days, a world first. This test confirmed that reinforcement learning AI can be safely applied in an actual plant, and demonstrated that this technology can control operations that have been beyond the capabilities of existing control methods and have up to now necessitated the manual operation of control valves based on the judgements of plant personnel. The initiative described here was selected for the 2020 Projects for the Promotion of Advanced Industrial Safety subsidy program of the Japanese Ministry of Economy, Trade and Industry.

Control in the process industries spans a broad range of fields, from oil refining and petrochemicals to high-performance chemicals, fibre, steel, pharmaceuticals, foodstuffs, and water. All of these entail chemical reactions and other elements that require an extremely high level of reliability.

In this field test, the AI solution successfully dealt with the complex conditions needed to ensure product quality and maintain liquids in the distillation column at an appropriate level while making maximum possible use of waste heat as a heat source. In so doing it stabilized quality, achieved high yield*4, and saved energy. While rain, snow, and other weather conditions were significant factors that could disrupt the control state by causing sudden changes in the atmospheric temperature, the products that were produced met rigorous standards and have since been shipped. Furthermore, as only good quality products were created, fuel, labour, time, and other losses that occur when off-spec products are produced were all eliminated.

The AI used in this control experiment, the Factorial Kernel Dynamic Policy Programming (FKDPP) protocol, was jointly developed by Yokogawa and the Nara Institute of Science and Technology (NAIST) in 2018, and was recognized at an IEEE International Conference on Automation Science and Engineering as being the first reinforcement learning-based AI in the world that can be utilized in plant management*7. Through initiatives including the successful conduct of a control training system*8 experiment in 2019, and an experiment in April 2020 that used a simulator to recreate an entire plant*9, Yokogawa has confirmed the potential of this autonomous control AI*10 and advanced it from a theory to a technology suitable for practical use. It can be used in areas where automation previously was not possible with conventional control methods (PID control and APC), and its strengths include being able to deal with conflicting targets such as the need for both high quality and energy savings.

Masataka Masutani, general manager of production technology at JSR, commented, “In an environment that is changing due to factors such as the fully-fledged introduction of 5G and other developments towards a digital society, as well as the aging of the human resources who ensure plant safety and a lack of human resources to replace them, the petrochemical industry is under strong pressure to improve safety and efficiency in its production activities by utilizing new technologies such as IoT and AI. The orientation of JSR is toward making production smart through a proactive incorporation of drones, IoT sensors, cameras, and other new technologies, and in this experiment, we took on the challenge of the automation of plant process control using AI control technology. We verified that AI is able to autonomously control the processes that were previously performed manually on the basis of operators’ experience, and we are firmly convinced of the usefulness and future potential of AI control. From those in the field, we have heard comments saying that not only has the burden on operators been reduced, but the very fact that we have taken on the challenge of this new technology and succeeded is motivation for taking forward DX into the future. Henceforth, we shall expand operations controlled with AI, and work to enhance chemical plant safety, stability, and competitiveness.”

 Ajoy Kumar, General Manager for Product Sales & Marketing, Yokogawa India Limited comments

“A general assumption is that increased industrial autonomy, with self governing systems and higher levels of autonomous operations, will lead to job losses. But I believe greater emphasis on industrial autonomy will create more jobs. Of course, there will be an evolution of roles. Some of the work force will need some amount of re-skilling. But the overall impact is going to be more jobs creation. We need to see this technology as something that augments human tasks, provides decision making assistance and adapts to changing conditions.”

 

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