AI Process Forecasting for Early Transient State Warning
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Solution Overview
Problem
Current automation systems in industrial processes struggle to effectively monitor and control deviations, often requiring human intervention, which can lead to inappropriate actions that exacerbate process issues, especially for inexperienced operators.
Innovation Solution
A method and system for predicting parameter values, plant states, and alarms using AI-based models trained on historical data to forecast steady and transient states, allowing for automated adjustments to manage industrial processes more effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If human operators monitor and control industrial processes manually, then flexibility and adaptability are maintained, but response time increases and errors may occur due to inexperience
Solution Approach 1:
The system performs preliminary actions by continuously analyzing historical data and predicting future parameter values, plant states, and alarms before they actually occur. This allows the system to prepare control actions in advance, reducing response time while maintaining the flexibility of human operators through advance warning and recommendation capabilities.
2Loss of time
If automation systems control industrial processes automatically, then response time decreases, but reliability deteriorates due to inability to handle all deviations appropriately
Solution Approach 1:
The system implements feedback by continuously monitoring actual process parameters, comparing them with forecasted values, and using this information to improve future predictions. The feedback loop ensures that the automation system learns from past performance, maintaining and improving reliability over time while keeping response times fast.
Solution Approach 2:
The predictive analytics system acts as an intermediary between manual operators and automated control systems. It provides advance warnings and control recommendations that assist operators in making informed decisions, combining the speed of automation with the judgment of human expertise to achieve both fast response and high reliability.
3Reliability
If more parameters are monitored to improve process control, then reliability increases, but device complexity increases
Solution Approach 1:
The system extracts and focuses on the most critical parameters and patterns from the large volume of process data using predictive analytics. By identifying and monitoring only the key indicators that significantly impact process reliability, the system achieves comprehensive monitoring coverage without proportionally increasing system complexity.
Solution Approach 2:
The predictive analytics platform serves multiple functions simultaneously: it monitors parameters, predicts future states, generates alarms, provides control recommendations, and continuously learns from data. This multi-functionality allows the system to achieve comprehensive monitoring and control with a single integrated solution rather than multiple separate systems.
Data Source
AI summary
A plurality of tags each identify a corresponding process parameter of an industrial process and historical values for the process parameter. A parameter forecast model is trained for each of the parameters that are identified by the plurality of tags, wherein each of the parameter forecast models is trained based at least in part on the received historical values for at least some of the parameters that are identified by the plurality of tags. A forecasted parameter value is generated for each of the parameters identified by the plurality of tags based at least in part on the corresponding parameter forecast model that corresponds to the respective parameter. A plurality of forecasted steady state periods and a plurality of forecasted transient state periods are predicted for each of one or more of the identified parameters.