Process Plant AI Control With Operator Availability Validation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing control systems for process plants, particularly those using AI, fail to consider the expertise of control room operators and often lack timely feedback, leading to suboptimal training of AI models and potential inaccuracies in control operations, especially when inexperienced operators provide feedback.
Innovation Solution
A distributed control system (DCS) that incorporates AI models to recommend control operations, utilizes imaging units to capture sensory parameters of operators, and validates these recommendations through queries to available experienced operators, allowing for autonomous control and improved AI model training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI models are used to automate control room operations, then productivity is improved, but the models lack training data from experienced operators leading to reduced reliability
Solution Approach 1:
The system implements a feedback mechanism where AI recommendations are presented to control room operators for validation. Operators confirm or reject recommendations, and this feedback is used to continuously retrain and improve the AI model, ensuring it learns from actual operator expertise while maintaining high productivity
Solution Approach 2:
The system uses an intermediary validation process where operators act as a bridge between AI recommendations and final control actions. The AI provides recommendations, operators validate them during available periods, and this intermediate step ensures reliability while preserving automation benefits
2Manufacturing precision
If constant feedback is requested from control room operators to train AI models, then manufacturing precision is improved, but operator availability decreases leading to loss of time
Solution Approach 1:
The system implements periodic feedback collection by detecting operator availability and requesting validation only during idle periods. Instead of continuous interruption, the system periodically checks for operator availability and collects feedback when operators are not actively controlling processes, minimizing time loss while maintaining training accuracy
Solution Approach 2:
The system prepares AI recommendations in advance and presents them to operators during their available periods. By anticipating operator availability and preparing recommendations beforehand, the system maximizes feedback collection opportunities without disrupting ongoing control operations
3Productivity
If feedback is collected from inexperienced operators, then productivity is improved through more data, but manufacturing precision deteriorates due to variable quality of feedback
Solution Approach 1:
The system applies different quality standards to different feedback sources. Feedback from experienced operators is given higher weight and used for critical training, while feedback from inexperienced operators is used supplementally. This local differentiation of feedback quality ensures high manufacturing precision while still utilizing available data from all operators
Data Source
Figure 1~2
Figure 3
Figure 4~5
AI summary
The present invention relates to a method and a system for controlling a process of a process plant. Hie system is configured to control a process in the process plant by recommending control operations and perform the control operations. The system obtains the control operations and detects availability of control room operators using historical data related to actions performed by the control room operators, sensory parameters of the control room operators and a status associated with the process. The system provides queries related to the control operations, the plant parameters, status of the process to the control room operators upon detecting the availability. Hie queries are validated by the control room operators. The system thereafter autonomously controls tire control room based on the validated data.