Next-Gen Control Technology AI Autonomously Runs Chemical Plant for 35 Days

Source: Press release

In a field test, a chemical plant in Japan ran autonomously for 35 days with the assistance of an artificial intelligence (AI) solution developed by Yokogawa and the Nara Institute of Science and Technology. The next-generation control technology is capable of taking into account numerous factors such as quality, yield, energy saving, and sudden disturbances.

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Distillation columns at the JSR chemical plant.
Distillation columns at the JSR chemical plant.
(Source: JSR Corporation)

Tokyo/Japan – Yokogawa Electric Corporation and JSR Corporation have recently announced 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 (PID control/APC) 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.

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, 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, labor, time, and other losses that occur when off-spec products are produced were all eliminated. Safe operations were ensured through a three-step process.

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.

Through initiatives including the successful conduct of a control training system experiment in 2019, and an experiment in April 2020 that used a simulator to recreate an entire plant, Yokogawa has confirmed the potential of this autonomous control AI 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.

Given the numerous complex physical and chemical phenomena that impact operations in actual plants, there are still many situations where veteran operators must step in and exercise control. Even when operations are automated using PID control and APC, highly-experienced operators have to halt automated control and change configuration and output values when, for example, a sudden change occurs in atmospheric temperature due to rainfall or some other weather event. This is a common issue at many companies’ plants.

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Regarding the transition to industrial autonomy, a very significant challenge has been instituting autonomous control in situations where until now manual intervention has been essential, and doing so with as little effort as possible while also ensuring a high level of safety. The results of this test suggest that this collaboration between Yokogawa and JSR has opened a path forward in resolving this longstanding issue.

JSR believes that this demonstration shows AI’s potential for addressing challenges that previously could not be resolved at chemical plants, and will investigate its application to other processes and plants with the aim of achieving further improvements in productivity.

Going forward, the two companies will continue to work together and investigate ways of using AI in plants.

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.

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.”

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Takamitsu Matsubara, associate professor at NAIST, remarked, “I am very glad to hear that this field test was successful. Data analysis and machine learning are now being applied to chemical plant operations, but technology that can be used in autonomous control and the optimization of operations has not been fully ready until now. The reinforcement learning AI FKDPP algorithm was jointly developed by Yokogawa and NAIST in 2018 to realize autonomous control in chemical plants.

Despite having to refer to a large number of sensors and control valves, the AI can generate a robust control policy in a limited number of learning trials. These features helped to improve the efficiency of the development process, and led to the achievement of autonomous control for a long period of 840 hours during the field test. I think this very difficult achievement of autonomous control in an actual distillation column and the fact that the level of practical application has been raised to the point where the entire production process and safety are integrated into one system have great significance for the entire industry. I look forward to seeing what happens next with this technology.”

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