Operation Model Evaluation for Adaptive Facility Control
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Solution Overview
Problem
Current systems for controlling and optimizing the operation of complex facilities, such as industrial plants, face challenges in efficiently adjusting control parameters to meet target performance metrics like energy efficiency and product quality, often requiring extensive trial-and-error and lacking adaptive learning capabilities.
Innovation Solution
An apparatus and method that utilize an operation model and a target setting model, integrated with learning processing units, to adjust control parameters based on real-time data and predefined reward functions, enabling adaptive optimization of facility operations to achieve target performance metrics through reinforcement learning and supervised learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional trial-and-error methods are used to adjust control parameters, then the system can eventually find optimal settings, but the process requires extensive time and manual intervention
Solution Approach 1:
The system employs reinforcement learning models that automatically adjust control parameters through self-learning from environmental feedback. The operation model and target setting model continuously optimize facility operations without human intervention, allowing the system to serve itself in the optimization process while significantly reducing adjustment time
Solution Approach 2:
The system implements closed-loop feedback mechanisms where the reinforcement learning model receives reward signals based on performance metrics (energy efficiency, product quality). This feedback drives continuous optimization of control parameters, enabling the system to learn from outcomes and improve over time without manual trial-and-error
2Adaptability or versatility
If traditional control systems are used, then the system structure remains simple, but the system lacks adaptive learning capabilities
Solution Approach 1:
The system introduces an operation model as an intermediary layer between the control system and the facility. This model acts as a mediator that processes state data, generates control parameters through reinforcement learning, and translates high-level objectives into actionable control commands, enabling adaptive learning without overwhelming system complexity
Solution Approach 2:
The patent replaces traditional mechanical trial-and-error adjustment mechanisms with intelligent software-based reinforcement learning models. The operation model and target setting model use algorithms to automatically optimize control parameters, substituting physical experimentation with computational learning and optimization
3Manufacturing precision
If manual intervention is used for parameter adjustment, then the system maintains simplicity, but energy efficiency and product quality optimization are insufficient
Solution Approach 1:
The reinforcement learning model autonomously optimizes control parameters to maximize energy efficiency and product quality without manual intervention. The operation model continuously learns from operational data and automatically adjusts facility operations, enabling the system to self-optimize manufacturing precision while minimizing human involvement
Solution Approach 2:
The system dynamically changes control parameters based on learned patterns and real-time state data. The operation model adjusts multiple parameters simultaneously (temperature, pressure, flow rates) to optimize both energy efficiency and product quality, achieving superior manufacturing precision through automated parameter optimization
4Reliability
If extensive trial-and-error is performed, then comprehensive optimization can be achieved, but operational variability increases
Solution Approach 1:
The reinforcement learning model uses continuous feedback from performance metrics to guide optimization, reducing operational variability. By learning from reward signals and systematically adjusting parameters based on observed outcomes, the model achieves comprehensive optimization while maintaining stable and predictable operations compared to random trial-and-error approaches
Data Source
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AI summary
Provided is an apparatus including a supply unit configured to supply a value of a state parameter to an operation model configured to output a recommendation value of a control parameter of a piece of equipment in response to a value of a state parameter relating to the piece of equipment being input; a control parameter acquisition unit configured to acquire a recommendation value of a control parameter that is output from the operation model in response to the supply unit supplying a value of a state parameter to the operation model; an acquisition unit configured to acquire a model evaluation value corresponding to a result of having operated the piece of equipment according to the recommendation value acquired by the control parameter acquisition unit; and an evaluation unit configured to evaluate the operation model, based on the model evaluation value and a reference evaluation value corresponding to a result of having operated the piece of equipment through a manipulation by a human.