Adaptive Process Control via Dynamic Model Switching
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Solution Overview
Problem
Existing process control systems face complexity and inefficiency due to the need for multiple process models and automatic regeneration of controllers to match changing process conditions, leading to increased operational costs and reduced performance as equipment degrades.
Innovation Solution
A system architecture that integrates automatic and independent process model identification and monitoring within the native control system, using function block plug-ins and model identification modules to gather data, create models, and adapt control strategies without user intervention, enabling continuous adaptive control and intelligent monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple process models are used to match changing process conditions, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system dynamically selects and switches between multiple process models based on current operating conditions. The controller automatically identifies which model best represents the current process state and uses that model for control calculations, enabling the system to adapt to changing conditions without manual intervention while managing complexity through automated model selection
Solution Approach 2:
The system changes parameters by switching between different process models that represent different operating regimes. Each model contains specific parameters optimized for particular process conditions, and the system automatically transitions between models as process conditions change, maintaining optimal control performance across varying operating states
2Adaptability or versatility
If automatic regeneration of controllers is implemented, then adaptability is improved, but device complexity increases
Solution Approach 1:
The controller performs automatic regeneration of control routines using embedded process models without requiring external intervention. The system self-updates by automatically selecting appropriate models, regenerating control parameters, and implementing new control strategies, thereby reducing the need for manual controller maintenance and regeneration while improving adaptability
3Manufacturing precision
If model-based control techniques are used, then control precision is improved, but device complexity increases
Solution Approach 1:
The system implements model-based control selectively for specific critical control loops where precision is most needed, rather than applying complex model-based techniques to all loops. This partial implementation achieves improved control precision for key processes while limiting the overall system complexity increase to manageable levels
Data Source
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AI summary
A controller includes a control module to control operation of a process in response to control data, a plug-in module coupled to the control module as a non-layered, integrated extension thereof, and a model identification engine. The plug-in detects a change in the control data, and a collects the control data and data in connection with a condition of the process in response to the detected change. The model identification engine executes a plurality of model parameter identification cycles. Each cycle includes simulations of the process each having different simulation parameter values and each using the control data as an input, an estimation error calculation for each simulation based on an output of the simulation and based on the operating condition data, and a calculation of a model parameter value based on the estimation errors and simulation parameter values used in the simulation corresponding to each of the estimation errors.