Plant Control Model With Iterative Prediction and Rapid Updates
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Solution Overview
Problem
Existing control systems for plants rely heavily on operator skill, leading to increased operational load and inefficiencies due to the lack of skilled personnel, and require burdensome preparation of step response time series for model prediction control.
Innovation Solution
A control system utilizing a calculation model that includes a prediction model and a decision model to iteratively calculate control target and operation data based on observation data, reducing the need for operator skill and enabling autonomous or assisted operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If model prediction control is implemented using conventional methods, then control accuracy is improved, but the preparation burden of step response time series increases significantly
Solution Approach 1:
The patent uses process simulation to generate virtual step response time series data instead of requiring actual physical experiments. The simulation creates copies of the real process behavior, allowing operators to obtain accurate control models without the burden of extensive field testing and data collection.
Solution Approach 2:
The system performs preliminary process simulation and step response calculation before actual control implementation. By pre-computing the step response time series through simulation, the system eliminates the need for time-consuming on-site experimentation and preparation, readying the control model in advance.
2Productivity
If operator skill dependency is reduced, then operational efficiency is improved, but system complexity increases due to advanced control algorithms
Solution Approach 1:
The control system performs self-diagnosis and self-optimization by automatically generating step response data through process simulation and autonomously determining optimal control parameters. The system serves itself by compensating for operator skill deficiencies through automated algorithms, reducing dependency on human expertise while maintaining operational efficiency.
Solution Approach 2:
The system continuously monitors actual process outcomes and uses this feedback to refine control predictions and adjust operation data. By incorporating real-time feedback loops, the system automatically learns and adapts, reducing the need for skilled operators to manually tune control parameters while managing complexity through structured feedback mechanisms.
Data Source
AI summary
A control system includes at least one processor and at least one memory. The at least one processor is configured to determine operation data by repeating a process of calculating control target data indicating a predicted value of a control target in a plant and the operation data indicating an operation value of a control device of the plant by a given calculation model based on observation data indicating an actual value of the plant.