Non-intrusive Data Analytics for Batch Process Quality Prediction
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Solution Overview
Problem
Existing process control systems face challenges in performing on-line data analytics within batch and continuous processes, particularly in determining whether a batch process is operating within desired specifications to produce products with desired quality metrics, as off-line analytics are not timely enough to correct issues in real-time.
Innovation Solution
An on-line data analytics system is introduced as a standalone device that operates non-intrusively with the process control system, using a data analytics engine coupled with a logic engine to perform predictive modeling and analysis, allowing for real-time monitoring and prediction of process variables and product quality without requiring reconfiguration or recertification of the existing control system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If off-line data analytics are performed after batch completion, then comprehensive quality analysis is achieved, but real-time corrective action capability is lost
Solution Approach 1:
The system performs preliminary data analytics during batch processing before completion, enabling real-time quality assessment and corrective actions. The analytics engine continuously monitors batch parameters and predicts quality outcomes, allowing operators to intervene while the batch is still in progress rather than waiting for off-line analysis after completion.
2Loss of time
If on-line data analytics are implemented within existing control systems, then real-time monitoring capability is improved, but system complexity and recertification requirements increase
Solution Approach 1:
The system introduces an intermediary standalone data analytics machine that interfaces with the existing control system through standard communication protocols. This intermediary performs on-line analytics without requiring modifications to the core control system, thereby maintaining real-time monitoring capability while avoiding system recertification requirements.
Solution Approach 2:
The analytics functionality is segmented into a separate standalone device rather than being integrated into the existing control system. This segmentation allows the analytics engine to operate independently, providing real-time monitoring capabilities without increasing the complexity or recertification burden of the original control system.
3Measurement precision
If batch processes are monitored at multiple stages, then quality prediction accuracy is improved, but data collection and processing complexity increases
Solution Approach 1:
The standalone data analytics machine performs multiple functions including data collection from various batch stages, real-time analytics, quality prediction, and corrective action recommendation. This multi-functional approach consolidates what would otherwise require multiple separate systems, improving quality prediction accuracy without proportionally increasing overall system complexity.
Data Source
AI summary
An on-line data analytics device can be installed in a process control system as a standalone device that operates in parallel with, but non-intrusively with respect to, the on-line control system to perform on-line analytics for a process without requiring the process control system to be reconfigured or recertified. The data analytics device includes a data analytics engine coupled to a logic engine that receives process data collected from the process control system in a non-intrusive manner. The logic engine operates to determine further process variable values not generated within the process control system and provides the collected process variable data and the further process variable values to the data analytics engine. The data analytics engine executes statistically based process models, such as batch models, stage models, and phase models, to produce a predicted process variable, such as an end of stage or end of batch quality variable for use in analyzing the operation of the on-line process.