Multi-Stage Process Modeling for Batch Quality Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Batch process control systems face challenges in determining whether a batch is operating within desired quality metrics in real-time, as process variables change over time, making it difficult to identify a 'golden batch' for comparison, and existing on-line analytics are limited in handling multiple products and equipment configurations.
Innovation Solution
The process is divided into stages based on equipment, measurements, and operating conditions, with models developed for each stage using data from multiple runs to predict end-of-stage or end-of-batch quality, and a forgetting factor is applied to adapt to changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single global model is used for the entire batch process, then the model structure is simple, but it cannot accurately capture stage-specific relationships and adapt to changing conditions
Solution Approach 1:
The batch process is divided into multiple stages, and a separate process model is developed for each stage. This segmentation allows each model to capture the specific relationships and dynamics characteristic of its stage, improving prediction accuracy while maintaining manageable model complexity through modular structure.
Solution Approach 2:
The system dynamically selects and switches between different process models based on the current stage of the batch process. This dynamic adaptation enables the system to use the most appropriate model for the current operating conditions, capturing stage-specific behavior without requiring a single overly complex global model.
2Adaptability or versatility
If process variables are monitored continuously, then real-time quality prediction is enabled, but the system cannot adapt to changing manufacturing conditions and equipment configurations
Solution Approach 1:
The system employs dynamic model selection where the appropriate process model is chosen based on current operating conditions, stage, and equipment configuration. This dynamic adaptation maintains reliability by ensuring the selected model is appropriate for the current state, while simultaneously providing adaptability to changing manufacturing conditions.
Solution Approach 2:
The system changes model parameters and structure based on the current stage and operating conditions. By adjusting which model is active and how it is configured, the system adapts to varying manufacturing conditions while maintaining prediction reliability through stage-appropriate modeling.
3Loss of time
If analysis is performed only after batch completion, then comprehensive quality assessment is possible, but corrective actions cannot be taken for current out-of-spec products
Solution Approach 1:
The system performs preliminary quality prediction during intermediate stages of the batch process rather than waiting for completion. By predicting final quality metrics before the batch is finished, the system enables timely detection of potential quality issues and allows corrective actions to be taken while the batch is still in progress, reducing both time loss and improving manufacturing precision.
4Measurement precision
If multiple process stages are modeled separately, then stage-specific accuracy is improved, but data integration and model coordination become complex
Solution Approach 1:
The process is segmented into distinct stages with separate models for each, improving stage-specific prediction accuracy by capturing local dynamics. The segmentation is managed through a coordinated framework that handles data integration systematically, reducing the complexity burden that would otherwise arise from multiple independent models.
Solution Approach 2:
The system uses feedback from each stage's model predictions to inform subsequent stages. This feedback mechanism coordinates the separate stage models, ensuring that predictions from earlier stages are incorporated into later stage modeling, thereby managing data integration complexity through structured information flow while maintaining stage-specific accuracy.
Data Source
AI summary
A process is modeled by resolving the process into a plurality of process stages, including at least a first process stage and a second process stage, and developing a plurality of models, each model corresponding to a respective one of the plurality of process stages, wherein the model corresponding to each process stage is developed using data from one or more runs of that process stage and output quality data relating to the one or more runs of that process stage and wherein the model corresponding to each process stage is adapted to produce an output quality prediction associated with that process stage, and wherein the output quality prediction produced by the model of a first one of the process stages is used to develop the model of a second one of the process stages.