Immune-Inspired Process Control for Dynamic Plant Optimization
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Solution Overview
Problem
Current process control techniques in energy and power generation plants rely on historical or theoretical models that are inadequate for precise control, as they fail to account for changing conditions and unmeasured variables, requiring extensive testing and engineering resources, and are not robust enough to handle complex, dynamic processes.
Innovation Solution
An integrated optimization and control technique inspired by biological immune systems that collects and stores process control states during on-line operation, allowing for stochastic optimization without the need for pre-created models, using stored data to determine optimal control inputs and adapt to changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If historical or theoretical models are used for process control, then control can be implemented, but the control precision is insufficient and cannot account for changing conditions and unmeasured variables
Solution Approach 1:
The system performs preliminary actions by collecting and storing process control states during normal operation before optimization is needed. This stored data serves as a knowledge base that enables rapid response to changing conditions without requiring real-time modeling or extensive testing, thus improving both control precision and adaptability
Solution Approach 2:
The system enables self-service by using its own stored process control states to determine optimal control inputs. The controller learns from its own operational history and uses this self-acquired knowledge to adapt to changing conditions, eliminating the need for external historical or theoretical models and improving control precision autonomously
2Measurement precision
If extensive modeling and testing are performed to improve control accuracy, then control precision improves, but costs and time consumption increase
Solution Approach 1:
The system performs preliminary data collection and storage during normal operation, building a knowledge base in advance. This eliminates the need for extensive real-time modeling and testing, as the stored process control states provide the necessary information for accurate control decisions, thus improving control accuracy without significant time loss
Solution Approach 2:
The system creates copies of process control states from actual operation and stores them for later use. These copied states serve as a repository of real-world operational data that can be referenced to determine optimal control inputs, providing accurate control without requiring time-consuming theoretical modeling or extensive testing
3Reliability
If model-based control is used, then control can be implemented, but the system is not robust enough to handle complex, dynamic processes
Solution Approach 1:
The system implements dynamics by continuously adapting to changing conditions using stored process control states. Rather than relying on static theoretical models, the controller dynamically selects optimal control inputs based on real operational data that reflects actual process behavior under varying conditions, thereby improving robustness for complex, dynamic processes
Solution Approach 2:
The system achieves self-service by using its own stored operational experience to handle complex, dynamic processes. The controller autonomously adapts to changing conditions by referencing its stored process control states, eliminating the need for external model updates or reconfiguration, thus improving robustness and adaptability simultaneously
Data Source
AI summary
An integrated optimization and control technique performs process control and optimization using stochastic optimization similar to the manner in which biological immune systems work, and thus without the use of historical process models that must be created prior to placing the control and optimization routine in operation within a plant. An integrated optimization and control technique collects various indications of process control states during the on-line operation of the process, and attempts to optimize the process operation by developing a series of sets of process control inputs to be provided to the process, wherein the control inputs may be developed from the stored process control states using an objective function that defines a particular optimality criteria to be used in optimizing the operation of the process. The technique responds to a significant change in the current process state by determining anew set of process control inputs to be provided to the process based on one or more of the stored process control states.