MPC Operator Support for Irregular Process Conditions
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Solution Overview
Problem
Model predictive controllers (MPC) in industrial process control systems often require complex configuration and can disable applications during upset conditions, leading to operator uncertainty and loss of confidence due to their black box nature and unpredictable reactions.
Innovation Solution
A method and apparatus for real-time model predictive control operator support using function blocks that detect irregular operating conditions, analyze case-specific scenarios, and provide recommendations to operators for correcting issues, thereby enhancing operator confidence and system stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If MPC controllers are configured as complex black box technology to handle various process conditions, then adaptability and control capability are improved, but operator confidence and system reliability deteriorate due to unpredictable reactions during upset conditions
Solution Approach 1:
An operator support tool is introduced as an intermediary between the MPC controller and the operator. This tool monitors controller behavior, detects irregular operating conditions, analyzes case-specific scenarios, and provides recommendations to operators. The intermediary translates the complex black box operations into understandable information, maintaining both the adaptability of the MPC and the confidence of the operator.
Solution Approach 2:
The system implements feedback by continuously monitoring the MPC controller's performance and providing information back to the operator. The operator support tool detects when the controller behaves unexpectedly or disables applications, analyzes the specific operating conditions, and feeds this information to the operator in a comprehensible format, enabling informed decision-making while maintaining controller adaptability.
2Productivity
If MPC applications are configured to handle complex process conditions, then control effectiveness is improved, but device complexity increases making the system harder to operate and troubleshoot
Solution Approach 1:
The operator support tool serves as an intermediary that manages the complexity of the MPC configuration. It automatically monitors controller behavior, detects irregular conditions, and provides diagnostic information without requiring the operator to understand the complex internal configuration of the MPC controller.
Solution Approach 2:
The operator support tool performs self-service by automatically detecting irregular operating conditions, analyzing case-specific scenarios, and generating recommendations without requiring manual configuration or deep technical knowledge from the operator. The system monitors itself and provides self-diagnostic capabilities.
3Object-affected harmful factors
If MPC controllers automatically disable applications during upset conditions to maintain stability, then system protection is improved, but operator confidence and continuous operation capability deteriorate
Solution Approach 1:
The operator support tool implements continuous feedback monitoring of the MPC controller's behavior. When the controller disables applications or exhibits unexpected behavior, the tool detects this, analyzes the specific operating conditions that triggered it, and provides feedback to the operator with recommendations for maintaining continuous operation while protecting the system.
Solution Approach 2:
The operator support tool acts as an intermediary that bridges the automatic protection mechanisms of the MPC and the operator's decision-making process. It translates the controller's protective actions into understandable information, enabling operators to make informed decisions about whether to maintain or shut down applications, thereby improving continuous operation capability while preserving system protection.
Data Source
AI summary
A method includes obtaining measurements associated with a plurality of controlled variables (CVs) and a plurality of manipulated variables (MVs). The method also includes detecting an irregular operating condition of a CV or an MV. The method also includes analyzing case specific operating conditions based on the irregular operating condition. The method further includes removing the irregular operating condition based on analyzing the case specific operating conditions.