Inferential Model for Abnormal Event Detection in Industrial Processes
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Solution Overview
Problem
Continuous industrial processes face challenges in detecting abnormal events and predicting future excursions of key operating parameters due to the limitations of direct measurement methods, which are expensive and prone to errors, and existing inferential models struggle to incorporate time dynamics and provide early warnings for process and equipment problems.
Innovation Solution
An inferential model that combines time synchronization with prior art algorithms like PLS, using readily available process measurements to estimate current and future quality values, and provides real-time updates and diagnostic information for abnormal event detection, incorporating data preprocessing, model calculations, and fuzzy net interpretations to summarize and visualize abnormal conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct measurement of process stream quality is used, then measurement accuracy is improved, but cost and maintenance complexity increase
Solution Approach 1:
The patent creates a virtual copy of the quality measurement system through software-based inferential models that replicate the functionality of physical analyzers. These models use readily available process data (temperatures, pressures, flows) to generate virtual quality measurements, eliminating the need for expensive on-line analyzers while maintaining measurement capability. The virtual analyzer continuously calculates quality parameters through mathematical models rather than physical sensing.
Solution Approach 2:
The patent replaces mechanical/physical measurement systems (on-line analyzers, sampling equipment, field shelters) with a computational/software-based system. Instead of using physical instruments to measure quality, the system uses software models that process standard process control data to infer quality parameters. This substitution eliminates the need for dedicated measurement hardware and its associated maintenance requirements.
2Productivity
If on-line analysis equipment is deployed, then continuous quality monitoring is improved, but installation and maintenance cost increase
Solution Approach 1:
The patent makes the process control system multi-functional by enabling it to perform both process control and quality measurement functions. The existing process control infrastructure (sensors, data acquisition, control algorithms) is enhanced to simultaneously provide quality inferential measurements. This universal approach eliminates the need for separate on-line analysis equipment, as the control system itself performs quality monitoring through its computational capabilities.
Solution Approach 2:
The system uses its own existing infrastructure (process sensors, control data, computational resources) to provide quality monitoring services. Rather than requiring external specialized equipment, the process control system serves its own quality measurement needs by leveraging its inherent data collection and processing capabilities. The system is self-sufficient, using its own resources to generate quality information without external dependency.
3Adaptability or versatility
If laboratory analysis is used for quality measurement, then measurement flexibility is improved, but measurement frequency and reliability worsen
Solution Approach 1:
The patent transforms quality measurement from a discontinuous, batch-based laboratory process into a continuous operation. The inferential models run continuously using real-time process data, providing uninterrupted quality information. This continuous measurement approach eliminates the gaps between laboratory samples while maintaining the flexibility to measure any quality parameter that can be inferred from process conditions. The useful action of quality assessment continues without interruption.
4Ease of manufacture
If traditional inferential models are used, then cost is reduced, but ability to detect abnormal events and predict future excursions worsens
Solution Approach 1:
The patent transforms static, slowly-updated inferential models into dynamic, rapidly-responsive models. The system continuously updates model parameters and recalculates quality estimates in real-time as process conditions change. This dynamic approach allows the models to quickly adapt to abnormal events and predict future excursions by responding immediately to changing process conditions rather than relying on slowly updated parameters.
Solution Approach 2:
The system implements continuous feedback loops where quality measurements (from either on-line analyzers or laboratory sources) are used to validate and update the inferential models in real-time. This feedback mechanism allows the models to learn from actual measurements and improve their accuracy continuously. The feedback also enables abnormal event detection by comparing model predictions with actual measurements and triggering alerts when significant deviations occur.
Data Source
AI summary
The present invention is a method and system for detecting an abnormal on-line analysis or laboratory measurement and for predicting an abnormal quality excursion due to an abnormal process condition.