Plant Parameter Management for Low-Load Operating Condition Control
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Solution Overview
Problem
Existing plant management systems face challenges in accurately improving operating conditions while minimizing costs associated with additional hardware resources and processing load.
Innovation Solution
A plant management system comprising a control device that sets and transmits manipulation parameters and acquires state parameters, and a management device that determines optimal manipulation parameter settings using a database of measured and predicted state parameters, thereby improving operating conditions without increasing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reinforcement learning is used to simulate complicated processes with various manipulation parameters and state parameters, then the operating condition can be improved, but the cost for additional hardware resources and processing load increases
Solution Approach 1:
The system segments the plant management function into two distinct components: a control device that collects and transmits operational data, and a management device that performs the complex determination of manipulation parameters. This segmentation allows the computationally intensive reinforcement learning processes to be concentrated in the management device, preventing the control device from becoming overloaded and reducing the need for expensive hardware upgrades across the entire system.
Solution Approach 2:
The management device acts as an intermediary between the control device and the plant operation. It receives manipulation parameters and state parameters from the control device, processes them through reinforcement learning algorithms to determine optimal manipulation parameters, and then feeds these back to the control device. This intermediary structure enables complex processing without burdening the control device's hardware resources.
2Adaptability or versatility
If a large number of manipulation parameters are set to control the operation, then the operating condition can be adjusted, but it becomes difficult to understand and predict how various state parameters will change
Solution Approach 1:
The system implements a feedback mechanism where the management device continuously receives state parameters from the control device, processes them through reinforcement learning, and generates recommended manipulation parameters based on the learned relationships. This feedback loop enables the system to understand and predict how changes in manipulation parameters affect state parameters, making the complex multi-parameter control system manageable and interpretable.
Solution Approach 2:
The reinforcement learning model learns the relationships between manipulation parameters and state parameters by analyzing historical data. This allows the system to predict how state parameters will change in response to manipulation parameter changes, transforming the difficulty of understanding complex parameter interactions into a solvable pattern recognition problem.
Data Source
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AI summary
A plant management system 1 includes: a control device 20; and a management device 30. The control device 20 includes: a manipulation parameter setting unit that sets values of a plurality of manipulation parameters; a state parameter acquisition unit that acquires values of state parameters indicating an operating condition of the plant 3; and a transmitter that transmits the values of the manipulation parameters and the values of state parameters to the management device 30. The management device 30 includes: an acquisition unit that acquires the values of the manipulation parameters and the values of state parameters from the control device 20; a database that stores set values of manipulation parameters and actually measured values or predicted values of a plurality of state parameters when the plant 3 is operated based on the set values, corresponding to each other; and a determination unit that determines a set value of a manipulation parameter capable of improving a value of a predetermined state parameter of the plant by referring to the values of the manipulation parameters and the values of the state parameters acquired by the acquisition unit and to correspondence stored in the database between the set values of the plurality of manipulation parameters and the actually measured or predicted values of the plurality of state parameters and that presents the set value thus determined to the control device.