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🛢️🧠ENEOS and PFN Begin Continuous Operation of AI-Based Autonomous Petrochemical Plant System
ENEOS Corporation (ENEOS) and Preferred Networks, Inc. (PFN) announced today that their artificial intelligence (AI) system, which they have been continuously operating since January 2023 for a butadiene extraction unit in ENEOS Kawasaki Refinery’s petrochemical plant, has achieved higher economy and efficiency than manual operations.
Jointly developed by ENEOS and PFN, the AI system is designed to automate large-scale, complex operations of oil refineries and petrochemical plants that currently require operators with years of experience. The new AI system is one of the world’s largest for petrochemical plant operation according to PFN’s research, with a total of 363 sensors for prediction and 13 controlled elements. The companies co-developed the system to improve safety and stability of plant operations by reducing dependence on technicians’ varying skill levels.
Smart Digital Reality for autonomous industrial facilities
Hexagon addresses the barriers to digital transformation with its Smart Digital Reality for autonomous industrial facilities, elevating the digital twin and digital thread by infusing it with intelligence to automate processes and analytics, increasingly removing human intervention on the journey to a fully autonomous future.
In a World First, Yokogawa and JSR Use AI to Autonomously Control a Chemical Plant for 35 Consecutive Days
Yokogawa Electric Corporation (TOKYO: 6841) and JSR Corporation (JSR, TOKYO: 4185) 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 (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.
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.
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. 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.
Industrial Autonomy on the Horizon
Automation systems in continuous-process plants are constantly evolving due to competitive industry pressures, customer demands, external events, and security requirements. Like it or not, most existing systems have changed as a result of numerous small actions taken over the years. A control system originally installed 25 years ago may include a patchwork of small additions made over time, leading to a system that is difficult to maintain because of all its unique quirks. Only some system owners take a strategic lifecycle approach to their control systems. Others are typically reactive, making changes only as needed to correct problems.
The trends are unmistakable: Autonomy is a critical technology that will lead process industry operations into the future. As technology moves beyond automation, autonomy and autonomous systems will bring improvements in many areas. The latest developments around industrial autonomy provide a timely response to several key industry trends, including the desire for post-COVID-19 preparedness and resilience, growing operational complexity, the aging industrial workforce and upskilling needs.
Advancing from industrial automation to industrial autonomous operations
Layered in along the way to autonomous operations, robotics provide an increased amount of functionality for inspection and measurement, system integration and fleet management, and physical operations with arm manipulation capabilities, said Penny Chen, Ph.D, senior principal technology strategist, Yokogawa. Robotic application examples include visual inspection, thermal inspection, auditory inspection, gas detection, object detection and 3D mapping. Yokogawa software aggregates information and platforms to manage many systems, she explained. See Figure 3. The software helps resolve three basic needs: 1) Robot management, 2) data orchestration and 3) integrating with existing control and asset management systems. Security and safety also are very important, she said.
These autonomous factories on satellites will produce materials in space that can’t be made on Earth
Bacon and cofounder-CEO Joshua Western want to take advantage of the unique conditions in space—the very low gravity and the fact that it’s an almost perfect vacuum—to make materials that can’t be made on Earth. Some new materials have already been produced on the International Space Station. A new type of fiber-optic cable, for example, is cloudy when it’s made on Earth because of gravity and impurities in the air, but crystal clear when made in space.
In space, it’s possible to manufacture new alloys that can be used to make bigger, stronger, turbines on aircraft, so planes use less fuel. On electric planes, new materials can make the electronic connections between batteries and the propeller motor more efficient, so the planes need less cooling equipment and can carry more passengers. Space factories are also well-suited to make better batteries for electric planes or cars. Wind turbines, for example, are more efficient the larger they are, but have to be made in pieces so they can be transported to a site for installation, and then held together with bolts. By making bolts that are stronger than what can be manufactured on Earth, it’s possible to develop a larger, more efficient wind turbine that can create more energy.
AI in Manufacturing: How It's Used and Why It's Important for Future Factories
The fully autonomous factory has always been a provocative vision, much used in speculative fiction. It’s a place that’s nearly unmanned and run entirely by artificial intelligence (AI) systems directing robotic production lines. But this is unlikely to be the way AI will be employed in manufacturing within the practical planning horizon.
The realistic conception of AI in manufacturing looks more like a collection of applications for compact, discrete systems that manage specific manufacturing processes. They will operate more or less autonomously and respond to external events in increasingly intelligent and even humanlike ways—events ranging from a tool wearing out, a system outage, or a fire or natural disaster.