Statistical Model Adaptation for Quality Prediction in Varying Process States
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Solution Overview
Problem
Existing process control systems struggle to perform quality prediction and fault detection in continuous and batch processes with varying throughput and product grades, as traditional statistical models fail to adapt to changing process states, leading to complex and processor-intensive predictive models that are difficult to implement and maintain in real-time systems.
Innovation Solution
A single statistical model, such as PLS, PCA, or MLR, is developed from historical data and adapted for quality prediction or fault detection across different process states by updating means and standard deviations, allowing for on-line quality prediction and fault detection without the need to change or recreate models for each state, with sensitivity adjustments for robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional statistical models are used for quality prediction in processes with varying throughput and product grades, then the models become complex and processor-intensive, but they can still achieve quality prediction and fault detection
Solution Approach 1:
The patent segments the process data into multiple states based on throughput levels and product grades. Instead of using a single complex model for all conditions, separate statistical models are developed for each process state. This segmentation allows each model to be simpler and more specialized, reducing overall system complexity while maintaining prediction accuracy across varying process conditions.
Solution Approach 2:
The patent implements dynamic model selection where the appropriate statistical model is automatically selected based on the current process state. The system dynamically adapts to changing throughput and product grade conditions by switching between pre-developed state-specific models, rather than using a static complex model for all scenarios. This dynamic approach reduces computational burden while maintaining reliability.
2Measurement precision
If separate models are developed for each process state, then quality prediction accuracy improves, but the number of models to maintain increases
Solution Approach 1:
The patent creates a universal framework that handles multiple process states through a common methodology. All state-specific models follow the same development process, use the same statistical techniques, and are maintained through a unified system. This universal approach allows the same team and processes to manage multiple models, significantly easing maintenance burden despite having multiple state-specific models.
Solution Approach 2:
The patent performs preliminary action by developing and validating all state-specific models during the offline model development phase. Once developed, these models are stored and can be automatically selected during online operation without requiring real-time model creation or modification. This preliminary development work reduces the complexity of real-time maintenance and operation.
3Ease of manufacture
If a single model is used for all process states, then model maintenance is simpler, but the model becomes less accurate for specific states
Solution Approach 1:
The patent segments the process data into multiple states based on throughput levels and product grades. Instead of using a single statistical model for all conditions, separate statistical models are developed for each process state. This segmentation allows each model to be simpler and more specialized, reducing overall system complexity while maintaining prediction accuracy across varying process conditions.
4Productivity
If real-time quality prediction is performed in continuous processes with varying throughput, then on-line prediction capability is achieved, but computational resources are heavily consumed
Solution Approach 1:
The patent performs preliminary action by developing and validating all state-specific models during the offline model development phase. Once developed, these models are stored and can be automatically selected during online operation without requiring real-time model creation or modification. This preliminary development work reduces the complexity of real-time maintenance and operation.
Solution Approach 2:
The patent implements dynamic model selection where the appropriate statistical model is automatically selected based on the current process state. The system dynamically adapts to changing throughput and product grade conditions by switching between pre-developed state-specific models, rather than using a static complex model for all scenarios. This dynamic approach reduces computational burden while maintaining reliability.
Data Source
AI summary
A process modeling technique uses a single statistical model, such as a PLS, PRC, MLR, etc. model, developed from historical data for a typical process and uses this model to perform quality prediction or fault detection for various different process states of a process. The modeling technique determines means (and possibly standard deviations) of process parameters for each of a set of product grades, throughputs, etc., compares on-line process parameter measurements to these means and uses these comparisons in a single process model to perform quality prediction or fault detection across the various states of the process. Because only the means and standard deviations of the process parameters of the process model are updated, a single process model can be used to perform quality prediction or fault detection while the process is operating in any of the defined process stages or states. Moreover, the sensitivity (robustness) of the process model may be manually or automatically adjusted for each process parameter to tune or adapt the model over time.