How many of your plant processes still depend on operator tribal knowledge? Any of those operators retiring soon? Yokogawa's e-RT3 Plus utilizes Factorial Kernel Dynamic Policy Programming (FKDPP) for accelerated machine learning. Jointly developed by Yokogawa and the Nara Institute of Science and Technology (NAIST), FKDPP is an autonomous control AI protocol that makes use of reinforcement learning technology. It can control and autonomize areas of plant operations that have been beyond the capabilities of existing control methods and have up to now necessitated manual operation. Yep…that’s right…operator tribal knowledge.
More on FKDPP
FKDPP is a new control technology that is different from PID control and APC. In March 2022, it was announced that Yokogawa and JSR Corporation's elastomer business unit (now owned by ENEOS Materials) had successfully concluded a 35-day field test in which AI was used to autonomously control a facility in a chemical plant that could not be controlled using existing control methods and had necessitated the manual operation of control valves based on the judgements of plant personnel. A world first, this was accomplished despite the presence of factors such as weather conditions that could have significantly disrupted the control state.
With the new service that Yokogawa announced, customers can create AI control models using the FKDPP algorithm and install them on edge controllers. This service has the following features and merits:
FKDPP Features
- Thanks to simplification of the AI model creation process, even non-AI experts can create an autonomous control AI model and install it on an e-RT3 edge controller.
- Retrofit of edge controllers with the installation of the autonomous control AI can be performed while other facilities remain in use.
- Supports control cycles as short as 0.01 seconds and is optimal for device control applications that require a quick response.
FKDPP Merits
Enables autonomous control where only manual control was possible
By applying autonomous control AI in areas that are beyond the capabilities of PID control and APC, both autonomy and optimal control can be achieved. It enables stable control that is less susceptible to external disturbances and increases productivity.
Suppresses overshoot
Although this will vary depending on the control targets, FKDPP suppresses overshoot. The reduction of overshoot (a condition where a set value is exceeded) is expected to extend, for example, the lifetime of furnaces and other heating facilities by reducing unnecessary overheating.
Significantly reduces settling time
FKDPP significantly reduces the settling time compared with PID control, saving energy and improving productivity.
Ability to achieve the right balance between conflicting requirements
Although this will depend on the control targets, FKDPP is able to resolve conflicting requirements and, for example, achieve the right balance between the need to reduce energy use while maintaining product quality.
Yokogawa put FKDPP into practical use by utilizing the company's know-how regarding plant operation and control, and successfully autonomized manual operations of a distillation column during a field test at a chemical plant.
Video: Automation to Autonomy - Industrial AI Platform e-RT3 Plus Controller
Case Study: How Yokogawa Took a Plant from Semi-Automated to Autonomous
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