Industrial Process Control Using Historical Deviation Correction
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Solution Overview
Problem
Industrial facilities face challenges in promptly identifying and addressing deviations in operating parameters of equipment, leading to downtime and reduced productivity due to the lack of effective techniques to leverage past instances of similar deviations and corresponding corrective actions.
Innovation Solution
A method and system that monitor real-time operating parameters, query a dataset of past deviations and corrective actions, and select an appropriate corrective action based on suitability scores to address current deviations, thereby enabling operators to make informed decisions and minimize disruptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If operators manually monitor and diagnose deviations in real-time, then they can address issues as they arise, but the response time is delayed and productivity is reduced due to the complex nature of industrial processes
Solution Approach 1:
The system pre-processes and stores historical deviation data and corrective actions in a structured dataset before deviations occur. When a deviation is detected, the system can immediately query this pre-prepared dataset for matching cases and recommended actions, eliminating the need for operators to manually search through historical records during critical response moments.
Solution Approach 2:
The patent replaces manual operator analysis and decision-making with an automated computer-based system that uses algorithms to query historical data, identify matching deviation cases, and recommend corrective actions. This substitution of mechanical human analysis with automated computational processing significantly reduces response time while maintaining or improving detection reliability.
2Reliability
If operators manually analyze historical data to identify corrective actions, then they can learn from past experiences, but the complexity of analyzing multiple past instances reduces ease of operation
Solution Approach 1:
The system performs self-service by automatically querying the historical dataset, identifying matching deviation cases, and generating corrective action recommendations without requiring operator intervention in the data analysis process. The computer system serves itself by autonomously completing the complex task of historical data analysis, freeing operators from this burdensome work while ensuring consistent and accurate analysis based on the structured dataset.
Solution Approach 2:
The system incorporates feedback mechanisms where the structured dataset of historical deviations and corrective actions continuously informs and improves the recommendation process. Each query to the dataset provides feedback from past experiences, allowing the system to learn and refine its corrective action recommendations while reducing the cognitive burden on operators who no longer need to manually analyze this feedback information.
3Reliability
If a comprehensive dataset of past deviations and corrective actions is maintained, then better informed decisions can be made, but the device complexity increases due to data storage and querying requirements
Solution Approach 1:
The patent segments the comprehensive dataset into structured, organized collections of historical deviation cases and their corresponding corrective actions. By dividing the large volume of historical data into manageable, indexed segments that can be efficiently queried and matched against current deviations, the system reduces the computational complexity of data storage and retrieval while preserving the complete information needed for informed decision-making.
Data Source
AI summary
Example techniques to control operations in an industrial facility is described. In an operation, operating parameters of an equipment from amongst a plurality of equipments installed in the industrial facility are monitored. A range of values is predefined for each of the operating parameters of the equipment. An operating parameter of the equipment that deviates from the corresponding predefined range of values is identified. A dataset is queried to identify past instances of deviation in the operating parameter that are within a specified range of deviation to the deviation in the operating parameter of the equipment. A corrective action is selected from amongst at least one corrective action in the dataset. The corrective action is implemented to correct the deviation in the operating parameter of the equipment.