Plant Control Proposal Model for Fast Abnormal State Response
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing plant control systems rely heavily on expert know-how and knowledge to address abnormal states, making it difficult to quickly and accurately respond to inefficiencies or abnormalities.
Innovation Solution
A plant control support system that utilizes a control model trained through machine learning to generate proposed control content based on plant state information, enabling automated and informed decision-making without relying on expert knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expert know-how and manuals are used to control abnormal plant states, then control accuracy can be maintained, but response time increases and efficiency decreases
Solution Approach 1:
The system performs preliminary action by pre-training the machine learning model using historical operation data and expert knowledge before actual abnormal state occurrences. The model learns from past cases including abnormal states and their resolutions, so when a new abnormal state occurs, the system can immediately apply pre-learned control strategies without waiting for expert analysis, thus reducing response time while maintaining control accuracy.
2Adaptability or versatility
If manual operation based on expert knowledge is used, then control decisions can be made with understanding, but the system cannot handle diverse abnormal states efficiently
Solution Approach 1:
The system achieves universality by designing a machine learning model that can handle multiple types of abnormal states across different plant operations. The model is trained on diverse historical data including various abnormal conditions and their resolutions, enabling it to generalize and adapt to new abnormal states it has not explicitly seen before. This multi-functional capability allows the system to efficiently handle diverse abnormal states without requiring separate manual procedures for each type.
3Reliability
If process control is used for normal plant operation, then stable operation can be maintained, but the system cannot handle abnormal states that fall outside normal control parameters
Solution Approach 1:
The system introduces an intermediary machine learning model that bridges the gap between traditional process control and expert manual intervention. The ML model acts as a mediator by continuously analyzing operation data and providing suggested control actions for both normal and abnormal states. For normal operations, it works alongside process control to maintain stability, while for abnormal states, it provides data-driven recommendations that complement expert knowledge, enabling the system to handle situations beyond traditional control parameters.
Data Source
AI summary
This plant control support system is provided with an acquisition unit, a generation unit, and an output unit, in order to provide a technique for appropriately supporting plant control without relying on the know-how and knowledge of experts and manuals. The acquisition unit acquires state information regarding the state of a plant. The generation unit uses a control model, and on the basis of the state information, generates control contents according to the state of the plant as proposed control contents. The control model is a model obtained by training the relationship between the control contents for controlling the plant and the control result obtained by executing the control of the control contents on the plant. The output unit outputs the proposed control contents.