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Yokogawa’s AI Is adopted at ENEOS Materials chemical plant

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Hydrocarbon Engineering,

ENEOS Materials Corp. (formerly the elastomers business unit of JSR Corp.) and Yokogawa Electric Corp. have reached an agreement that Factorial Kernel Dynamic Policy Programming (FKDPP), a reinforcement learning-based AI algorithm, will be officially adopted for use at an ENEOS Materials chemical plant.

This agreement follows a successful field test in which this autonomous control AI demonstrated a high level of performance while controlling a distillation column at this plant for almost an entire year. This is the first example in the world of reinforcement learning AI being formally adopted for direct control of a plant.

Over a 35 day (840 hour) consecutive period, from 17 January to 21 February 2022, this field test initially confirmed that the AI solution could control distillation operations that were beyond the capabilities of existing control methods (PID control/APC) and had necessitated manual control of valves based on the judgements of experienced plant personnel. Following a scheduled plant shut-down for maintenance and repairs, the field test resumed and has continued to the present date. It has been conclusively shown that this solution is capable of controlling the complex conditions that are needed to maintain product quality and ensure that liquids in the distillation column remain at an appropriate level, while making maximum possible use of waste heat as a heat source. In so doing it has stabilised quality, achieved high yield, and saved energy.

In this field test, the autonomous control AI demonstrated year-round stability, reduced environmental impact, lightened workload and improved safety, as well as robustness of the AI control model.

ENEOS Materials will look into applying this AI to other types of processes and plants, and will continue working to improve productivity and save energy by expanding the scope of autonomisation.

To promote plant autonomisation, on 27 February Yokogawa launched the provision of an autonomous control AI service for edge controllers, also a world first.

Going forward, ENEOS Materials and Yokogawa will continue to work together and investigate ways to carry out digital transformation (DX) through the use of AI for control and condition-based maintenance in plants.

Masataka Masutani, Division Director, Production Technology Division, ENEOS Materials Corp., said: “Amidst severe challenges impacting the petrochemical industry such as the retirement of experienced personnel who help to ensure the safe operation of facilities, we are pleased with this demonstration of the use of AI to autonomously control processes that had previously been controlled manually. In addition to reducing operator workload, this test, which has continued for about a year, has demonstrated that this system can operate stably without being affected by seasonal changes or regular maintenance and repairs, and can save energy and reduce GHG emissions. Through smart production, we will continue to strive for safety and stability, decarbonise operations, and enhance competitiveness.”

Takamitsu Matsubara, Professor at the Nara Institute of Science and Technology, added: “The key to reinforcement learning is how the reward function is designed. By closely incorporating process industry control knowledge in the reward function, it is possible to create an AI control model with a high level of reliability and validity that is able to achieve year-round stable operation. The fact that this field test confirmed the model’s ability to be applied as is even after the performance of regular maintenance and repair implies the robustness of the AI control model. I believe that FKDPP, a new control technology that can handle complex conditions, will make broad-ranging contributions to the development of industry around the world.”

Kenji Hasegawa, a Yokogawa Vice President and head of the Yokogawa Products Headquarters, commented: “I am very grateful to have been able to work alongside our customer to take up the challenge of this globally unparalleled autonomisation initiative. Given the difficulty of controlling operations in actual plants due to the complex effects of physical and chemical phenomena, there are many areas where highly-experienced operators have still had to intervene. With a focus on products and consulting, Yokogawa will develop and expand the use of autonomous control AI, and work with our customers to drive their decarbonisation, digital transformation, and autonomisation efforts.”

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