Industrial Plant Controller Training for Rare Event Response
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Solution Overview
Problem
Industrial plant control systems face challenges in effectively training controllers to respond to a wide range of events, such as equipment failures, due to limited real-world data and the rarity of certain operational scenarios, which can lead to inadequate operator preparedness and potential safety issues.
Innovation Solution
A method using reinforcement learning and simulation models to generate large quantities of training data by simulating various events, allowing the industrial plant controller to explore possible control actions and their consequences, thereby training the controller to respond effectively to diverse scenarios without actual real-world exposure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional training methods using limited real-world data are used, then the controller is trained with actual operational data, but the controller cannot effectively respond to rare events such as equipment failures
Solution Approach 1:
The patent creates virtual copies of the industrial plant through simulation models that replicate real plant behavior. These digital twins generate synthetic training data that mirrors real-world operational scenarios, including rare events like equipment failures. The simulation model processes state vectors and control actions to produce subsequent state vectors and reward signals, creating comprehensive training datasets without requiring actual physical experimentation or extensive real-world data collection.
2Adaptability or versatility
If the controller is exposed to diverse scenarios through simulation, then the controller responds better to rare events, but the training process becomes more complex
Solution Approach 1:
The patent introduces a simulation model as an intermediary between the controller and the real industrial plant. This intermediary environment allows the controller to be trained on diverse scenarios including rare events without directly exposing the actual plant to experimental conditions. The simulation model acts as a safe intermediary that can generate unlimited training scenarios while maintaining computational tractability through structured state representations and reward functions.
Solution Approach 2:
The patent employs reinforcement learning to dynamically adjust controller parameters based on simulated experiences. The controller processes state vectors and generates control actions, receiving reward signals that guide parameter optimization. This parameter adjustment mechanism enables the controller to adapt to diverse scenarios automatically, reducing the need for manual configuration complexity while improving versatility.
3Quantity of substance
If reinforcement learning with simulation data is used, then large quantities of training scenarios can be generated, but the training process requires significant computational resources
Solution Approach 1:
The patent segments the training process into discrete time steps with structured state vectors and control actions. Each training iteration processes individual state transitions rather than entire trajectories, enabling parallel computation and efficient resource utilization. The simulation model evaluates individual control actions independently, allowing computational tasks to be divided and processed in manageable units that reduce overall energy consumption while maintaining large-scale training capabilities.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an industrial plant controller that controls operation of an industrial plant. In one aspect, a method comprises generating training data using an industrial plant simulation model that simulates operation of the industrial plant. The industrial plant controller is trained by a reinforcement learning technique using the training data. The industrial plant controller is configured to process an input comprising a state vector characterizing a state of the industrial plant in accordance with a plurality of industrial plant controller parameters to generate an action selection policy output that defines a control action to be performed to control the operation of the industrial plant.