Batch Quality Prediction Using Stage Signatures and ML Feedback
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Solution Overview
Problem
Batch production processes face challenges in predicting end-batch product quality and detecting anomalies in real-time, leading to high rates of 'out-of-spec' products, which are costly and resource-intensive, especially in industries like food processing and special chemicals, where existing methods lack actionable advice and are prone to false alerts.
Innovation Solution
A supervised machine learning approach is implemented to create a predictive model that standardizes historical operating data, partitions it into stages, determines signatures, and trains a machine learning pattern model to predict conformity with operational standards, providing real-time alerts and root-cause analysis based on statistical probability and K-nearest neighbor analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If existing MSPC based models are used for batch monitoring, then anomaly detection capability is provided, but actionable advice and prediction capability are lacking
Solution Approach 1:
The system implements feedback by continuously monitoring batch processes and providing real-time predictions and recommendations. The machine learning model analyzes process data and feeds back actionable insights to operators, creating a closed-loop system that transforms raw anomaly detection into meaningful guidance for process correction and optimization.
Solution Approach 2:
The patent introduces an intermediary machine learning layer between the MSPC model and the operator. This intermediary processes the raw anomaly detection output, interprets it in context of historical data and process knowledge, and translates it into actionable recommendations, thereby bridging the gap between detection and decision-making.
2Productivity
If traditional batch monitoring methods are applied, then process monitoring is achieved, but real-time quality prediction is not possible
Solution Approach 1:
The system performs preliminary action by training machine learning models on historical batch data before actual production. The model learns from past in-spec and out-of-spec batches, enabling it to predict quality outcomes in real-time during ongoing batches. This preliminary training phase allows the system to provide advance quality predictions rather than waiting for batch completion.
Solution Approach 2:
The patent transitions from traditional time-based batch monitoring to a multi-dimensional prediction space by incorporating multiple process variables, historical patterns, and machine learning features. This dimensional transformation enables the system to predict quality outcomes at any point during the batch process, adding the dimension of predictive capability to traditional monitoring.
3Reliability
If batch production processes use existing quality control methods, then monitoring is performed, but the rate of out-of-spec batches remains high at 30-50%
Solution Approach 1:
The system segments the batch production process into multiple stages and analyzes quality risk at each segment. By dividing the continuous batch process into discrete phases and evaluating quality predictions at each segment, the system can identify specific stages where quality deviations are likely to occur, enabling targeted interventions to improve overall specification conformity.
Solution Approach 2:
The patent utilizes parameter changes by training the machine learning model on multiple process parameters and quality metrics. The model analyzes relationships between process parameters and quality outcomes, identifying critical parameters that most influence specification conformity. This enables the system to predict and prevent out-of-spec batches by monitoring and adjusting key parameters in real-time.
Data Source
AI summary
Embodiments control and optimize batch processes. An embodiment obtains and standardizes historical operating data from a plurality of batch production runs of an industrial process. For each batch production run, the standardized operating data corresponding to the batch is partitioned into one or more stages and one or more signature for each stage is determined using the partitioned standardized data. Each determined signature is associated with a class label based upon whether output of a batch run corresponding to the signature conforms or does not conform with operational standards. A model is trained, with at least a subset of the signatures as inputs and associated class labels as outputs, to predict, based on operating data from a real-world batch process, whether output of the process will conform or not conform with the operational standards. Online predictions can be automatically or manually applied to control and optimize a batch production run.