In a World First, Yokogawa’s Autonomous Control AI Is Officially Adopted for Use at an ENEOS Materials Chemical Plant

Rhea-AI Summary

ENEOS Materials Corporation and Yokogawa Electric Corporation have announced the formal adoption of the Factorial Kernel Dynamic Policy Programming (FKDPP) AI algorithm at an ENEOS Materials chemical plant. This decision follows a successful year-long field test, marking a world-first implementation of reinforcement learning AI in direct plant control. The AI demonstrated superior performance in managing distillation operations, enhancing product quality, and reducing operational costs. Key benefits include a 40% reduction in CO2 emissions, year-round stability, and improved safety by lessening manual operator intervention.

Positive

• 40% reduction in CO2 emissions compared to manual control.
• Year-round stable operation and high product quality maintained.
• Reduced fuel and labor costs through efficient resource use.
• Improved safety and reduced mental stress for operators.

Negative

• None.

– One year of stable operation demonstrates this next-generation control technology can decrease environmental impact, achieve stable quality, and transform operations –

TOKYO--(BUSINESS WIRE)-- ENEOS Materials Corporation (formerly the elastomers business unit of JSR Corporation) and Yokogawa Electric Corporation (TOKYO: 6841) announce they 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^*1 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^*2.

Distillation columns at the ENEOS Materials chemical plant (Photo: ENEOS Materials Corporation)

Over a 35 day (840 hour) consecutive period, from January 17 to February 21, 2022, this field test initially confirmed^*3 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 stabilized quality, achieved high yield, and saved energy.

In this field test, the autonomous control AI demonstrated the following four benefits:

1. Year-round stability
   The autonomous control AI maintained stable control of the liquid levels and maximized the use of waste heat, even in winter and summer weather, with external temperatures changes by about 40ºC. No problems were observed, and stable operation and high product quality was achieved throughout the field test.
2. Reduced environmental impact
   By eliminating the production of off-spec products, the autonomous control AI reduced fuel, labor, and other costs, and made efficient use of raw materials. While producing good quality products that met shipment standards, the autonomous control AI reduced steam consumption and CO2 emissions by 40%^*4 in comparison to conventional manual control.
3. Lightened workload and improved safety
   The autonomous control AI eliminated the need for operators to perform manual inputs. This not only decreased workload and helped to prevent human error, it also reduced mental stress levels and improved safety.
4. Robustness of the AI control model
   Even after modifications were made at the plant during a routine shut-down for maintenance and repair, the same AI control model could remain in use.

ENEOS Materials found over the course of this one-year verification process that the autonomous control AI was a robust system that could achieve stable performance and optimize operations throughout the year, including in winter and summer. The company 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 autonomization.

To promote plant autonomization, on February 27 Yokogawa launched the provision of an autonomous control AI service for edge controllers^*5, also a world first^*6. In conjunction with this service, the company is offering customers who wish to achieve autonomous plant operations a global consulting service that covers everything from the identification of control issues to the investigation of optimum control methods and the calculation of cost-effectiveness, and includes safety, implementation, maintenance, and operation.

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 Corporation:
“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, decarbonize operations, and enhance competitiveness.”

Takamitsu Matsubara, Professor at the Nara Institute of Science and Technology:
“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:
“I am very grateful to have been able to work alongside our customer to take up the challenge of this globally unparalleled autonomization 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 decarbonization, digital transformation, and autonomization efforts.”

*1	Yokogawa defines autonomous control AI as AI that deduces the optimum method for control independently and has a high level of robustness enabling it to autonomously handle, to a certain extent, situations that it has not previously encountered.
*2	Based on comprehensive secondary research of publicly available resources by IoT Analytics, performed in March 2023.
*3	In a World First, Yokogawa and JSR Use AI to Autonomously Control a Chemical Plant for 35 Consecutive Days - Putting into practical use a next-generation control technology that takes into account quality, yield, energy saving, and sudden disturbances -
*4	In comparison to the amount of steam previously used to maintain the liquid level and the corresponding amount of CO2 emissions.
*5	Yokogawa Launches Autonomous Control AI Service for Use with Edge Controllers - Optimizes control to improve productivity and save energy -
*6	As the world’s first commercially available reinforcement learning AI service for edge controllers. Based on comprehensive secondary research of publicly available resources by IoT Analytics, performed in March 2023.

About ENEOS Materials Corporation

ENEOS Materials is engaged in the research and development, manufacturing, and sales of synthetic rubber, thermoplastic elastomers, latex, and other raw materials for the automobile industry and other industries around the world. Formed on April 1, 2022, through the sale of JSR Corporation’s elastomers business unit to ENEOS Corporation, ENEOS Materials has world-class research & development capabilities and manufacturing technologies. Backed by the extensive procurement, funding, organization, and global network of the ENEOS Group, ENEOS Materials is able to provide a stable supply of high quality and competitive products. In response to changes such as the shift to electric vehicles and the need to achieve the Sustainable Development Goals (SDGs), ENEOS Materials continues to refine its technological capabilities and is promoting innovations that will contribute to society and promise a brighter and more vibrant future for all.

About Yokogawa

Yokogawa provides advanced solutions in the areas of measurement, control, and information to customers across a broad range of industries, including energy, chemicals, materials, pharmaceuticals, and food. Yokogawa addresses customer issues regarding the optimization of production, assets, and the supply chain with the effective application of digital technologies, enabling the transition to autonomous operations. Founded in Tokyo in 1915, Yokogawa continues to work toward a sustainable society through its 17,000+ employees in a global network of 122 companies spanning 61 countries.
For more information, visit www.yokogawa.com

The names of corporations, organizations, products, services and logos herein are either registered trademarks or trademarks of ENEOS Materials Corporation, Yokogawa Electric Corporation, or their respective holders.

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Corporate Planning Department

ENEOS Materials Corporation



PR Section

Integrated Communications Center

Yokogawa Electric Corporation

Yokogawa-pr@cs.jp.yokogawa.com

Source: Yokogawa Electric Corporation

FAQ

What is the significance of the FKDPP AI algorithm for ENEOS Materials and Yokogawa?

The FKDPP AI algorithm represents a world-first adoption of reinforcement learning AI for direct control in a chemical plant, showcasing advancements in operational efficiency.

When did the field test for the AI algorithm take place?

The field test ran from January 17 to February 21, 2022, confirming the AI's ability to control distillation operations.

How much did the AI reduce CO2 emissions by?

The AI led to a 40% reduction in CO2 emissions compared to conventional manual control methods.

What benefits did the AI provide during the field test?

The AI maintained stable liquid levels, optimized waste heat usage, improved product quality, and reduced environmental impact.

What future steps are ENEOS Materials and Yokogawa planning regarding the AI technology?

They plan to explore the application of this AI technology to other processes and continue digital transformation initiatives.