Yokogawa and JSR Use AI to Autonomously Control a Chemical Plant
First time in the world, Yokogawa and JSR used AI to Control a Chemical Plant Autonomously for 35 Days Consecutively.
Putting into practical use a next-generation control technology that takes into account quality, yield, energy-saving, and sudden disturbances
Bengaluru, May 19: 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. It 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.
Control in the process industries spans various 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 the maximum possible use of waste heat as a heat source. In doing so, it stabilised 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 produced met rigorous standards and have since been shipped. Furthermore, as the unit created only good quality products, fuel, labour, time, and other losses 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. It was recognised at an IEEE International Conference on Automation Science and Engineering as the first reinforcement learning-based AI that can be utilised 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). Its strengths include dealing 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 processes 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. It is a common issue at many companies’ plants. Regarding the transition to industrial autonomy, a significant challenge has been instituting autonomous control in situations where until now, the manual intervention has been essential and doing so with as little effort as possible while also ensuring a high level of safety. This test suggests that this collaboration between Yokogawa and JSRhas opened a path forward in resolving this longstanding issue.
Yokogawa welcomes customers who are interested in these initiatives globally. The company aims to swiftly provide products and solutions that lead to the realisation of industrial autonomy.
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 to achieve further productivity improvements. As we advance, the two companies will continue to work together and examine ways of using AI in plants.
Sajiv Nath, MD, Yokogawa India, talking about the innovation, comments, “Yokogawa has always been pioneers of innovation helping companies and societies to become sustainable and adopt and leverage new technology. In one such application and for the first time in the world. Yokogawa and JSR used AI (Artificial Intelligence) to autonomously control a chemical plant for 35 consecutive days. It has never happened before, and we feel we will be revolutionising how manufacturing will evolve in the future. Indian Industry has always been at the forefront of adopting newer ways and means to make the process more and more productive. Companies are extremely concerned about process safety and environmental issues. I see a high level of enthusiasm among Indian customers to understand the usage, benefits, and challenges of adopting Industrial Autonomy. This AI technology enabled by the FKDPP algorithm will change the way Indian manufacturing is in the future. This specific FKDPP-based AI technology, being versatile, can be implemented in a broad range of industry segments. I strongly believe we in Yokogawa will foster a new era in Indian manufacturing and help the Indian economy grow and become sustainable. It will align with the new Indian perspective of helping Indian manufacturing grow in the midterm to the long term and help make AtmaNirbhar Bharat a reality.”
Dr. Hiraoki Kanokogi, General Manager, Yokogawa Products Headquarters, Yokogawa Electric Corporation, shares, “The biggest takeaway from this field test was that we could ensure safe autonomous control with AI that improves productivity and reduces cost and time loss. In the industrial AI sector, the vast majority of AI is what we call “problem analysis AI.” This kind of AI analyses the provided data to detect anomalies for predictive maintenance, predict quality, or determine the cause of issues. It is generally used to support human decision making. In this case with the chemical plant, we are talking about “autonomous control AI,” which actually searches for the optimal control model by itself and then implements that. We are certainly looking to work with customers on field trials for other processes and applications to confirm the versatility and robustness of our AI algorithm FKDPP and demonstrate the value in terms of the profitability and sustainability benefits it can deliver.”
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. Still, technology that can be used in autonomous control and operations optimisation has not been fully ready until now. Yokogawa and NAIST jointly developed the reinforcement learning AI FKDPP algorithm in 2018 to realise 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 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.”