Process Control Learning Switch to Cut Energy Loss and Wear

Solution Overview

Problem

Self-optimizing control strategies, such as extremum seeking control, often result in loss and equipment wear due to continuous variation of signals to find optimum operating conditions, making it challenging to develop robust process control systems.

Innovation Solution

A system that uses a processing circuit to learn a steady-state relationship between a manipulated variable and an output variable using a self-optimizing control strategy and then switches to a model-based control strategy, which operates based on the learned relationship, minimizing energy consumption and reducing equipment wear.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If self-optimizing control strategy is used to find optimum operating conditions, then system performance is improved, but energy loss and equipment wear increase

Engineering Contradiction:

Improvesystem performanceVSAvoidenergy loss

Core Design Contradiction:

ProductivityVSLoss of energy

Solution Approach 1:

The system performs preliminary action by using the self-optimizing controller during a training period to learn and store the steady-state relationship between manipulated and controlled variables. This pre-acquired knowledge is then used by the model-based controller during normal operation, eliminating the need for continuous oscillation and signal variation, thereby reducing energy loss while maintaining optimal performance.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The invention creates a copy of the optimal operating characteristics by storing the steady-state relationship data acquired during training. The model-based controller uses this stored copy to replicate optimal performance without requiring the original self-optimizing search process, thus avoiding continuous energy-consuming oscillations and equipment wear.

Inventive Principle:

Principle #26Copying

2Productivity

If self-optimizing control strategy is used to find optimum operating conditions, then system performance is improved, but equipment wear increases

Engineering Contradiction:

Improvesystem performanceVSAvoidequipment wear

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system performs preliminary action by using the self-optimizing controller during a training period to learn and store the steady-state relationship between manipulated and controlled variables. This pre-acquired knowledge is then used by the model-based controller during normal operation, eliminating the need for continuous oscillation and signal variation, thereby reducing energy loss while maintaining optimal performance.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The invention creates a copy of the optimal operating characteristics by storing the steady-state relationship data acquired during training. The model-based controller uses this stored copy to replicate optimal performance without requiring the original self-optimizing search process, thus avoiding continuous energy-consuming oscillations and equipment wear.

Inventive Principle:

Principle #26Copying

3Productivity

If self-optimizing control strategy is used continuously, then optimal performance is maintained, but energy consumption increases

Engineering Contradiction:

Improveoptimal performanceVSAvoidenergy consumption

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary action by using the self-optimizing controller during a training period to learn and store the steady-state relationship between manipulated and controlled variables. This pre-acquired knowledge is then used by the model-based controller during normal operation, eliminating the need for continuous oscillation and signal variation, thereby reducing energy loss while maintaining optimal performance.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The invention creates a copy of the optimal operating characteristics by storing the steady-state relationship data acquired during training. The model-based controller uses this stored copy to replicate optimal performance without requiring the original self-optimizing search process, thus avoiding continuous energy-consuming oscillations and equipment wear.

Inventive Principle:

Principle #26Copying

Data Source

PatentUS8412357B2Process control systems and methods having learning features

Publication Date: 2013.04.02 TYCO FIRE & SECURITY GMBH

AI summary

A system for operating a process includes a processing circuit that uses a self-optimizing control strategy to learn a steady-state relationship between an input and an output. The processing circuit is configured to switch from using the self-optimizing control strategy to using a different control strategy that operates based on the learned steady-state relationship.