Modular Predictive Control Models for Complex Industrial Plants
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Solution Overview
Problem
Existing methods for creating dynamic models of industrial plants for model predictive control are inefficient, complex, and difficult to automate, leading to specialized models that are hard for regular operators to understand and adapt.
Innovation Solution
A method for generating a dynamic model of an industrial plant by dividing the plant into sub-units, creating dynamic sub-models for each sub-unit, and combining them based on dependencies, allowing for a more transparent and adaptable control system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single comprehensive dynamic model of the entire plant is created, then the model can capture all interdependencies and provide accurate predictions, but the model becomes extremely complex and difficult for regular operators to understand and adapt
Solution Approach 1:
The patent divides the complex plant model into multiple modular sub-models, each representing a specific sub-unit or process section. These sub-models can be independently developed, validated, and understood by operators, while collectively providing comprehensive prediction capabilities through their interconnections.
2Measurement precision
If a comprehensive dynamic model of the entire plant is created, then the model can capture all interdependencies and provide accurate predictions, but the model becomes difficult to automate and requires specialized expertise to create
Solution Approach 1:
By segmenting the model into standardized sub-models with defined interfaces and data requirements, the system enables automated model assembly and configuration. Regular operators can use configuration tools to assemble sub-models based on plant layout and process knowledge, reducing the need for specialized modeling expertise.
Solution Approach 2:
The patent develops universal sub-model templates that can be reused across different plant sections and configurations. These standardized building blocks with consistent interfaces and parameters can be automatically instantiated and configured, facilitating automated model creation while maintaining accuracy.
3Ease of operation
If the model is simplified for ease of understanding by operators, then the model becomes more accessible and easier to adapt, but the model loses accuracy in capturing plant interdependencies
Solution Approach 1:
The modular structure allows operators to focus on understanding and managing individual sub-models relevant to their responsibilities, rather than attempting to comprehend the entire complex system. Each sub-model can be simplified for operational understanding while the collective system maintains comprehensive accuracy through the interconnections of all sub-models.
Data Source
AI summary
A method for generating a dynamic model of an industrial plant having: a plurality of physical processes that are dependent such that an outcome of at least one first process is fed into at least one second process; a plurality of low-level controllers, each controller acting upon at least one physical process such that at least one process variable of the at least one physical process is controlled to match a set-point of the low-level controller; and a plurality of sensors, each sensor measuring at least one process variable of one of the physical processes, and/or of the plant as a whole, the set-points of the low-level controllers and current values of the process variables measured by the sensors being the inputs of the model, and predicted future values of the process variables that are likely to result from applying the set-points to the low-level controllers being the outputs.