Statistical Model for Continued Process Verification
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Solution Overview
Problem
Current process control systems in industries like pharmaceuticals face challenges in implementing analytical/statistical models for continued process verification (CPV) due to the limited number of batches available for historical data, which hinders real-time monitoring and quality prediction, especially in industries with short product life cycles.
Innovation Solution
The method involves generating an analytical/statistical model using a limited number of historical batch datasets, creating a model batch, and generating simulated batch datasets to supplement the historical data, allowing for the prediction of quality and monitoring of subsequent batches through primary component analysis (PCA) and projected latent structure (PLS).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If analytical/statistical models are implemented for continued process verification, then real-time monitoring and quality prediction capability is improved, but the requirement for large amounts of historical batch data creates a problem when only limited batches are available
Solution Approach 1:
The patent creates simulated batch datasets that copy the structure and characteristics of real historical batch data. These synthetic datasets are generated using process knowledge and statistical methods to mimic the distribution and relationships of actual process parameters, thereby augmenting the limited historical data available for model training without requiring additional physical batches
Solution Approach 2:
The patent performs preliminary actions by generating simulated batch datasets before the actual CPV model is deployed. This pre-generation of synthetic data allows the model to be trained in advance with sufficient data, enabling effective real-time monitoring even when historical batches are limited. The simulated data is prepared beforehand to ensure adequate training material is available
2Reliability
If more historical batches are accumulated to improve model accuracy, then product quality prediction reliability is improved, but the time required for data accumulation increases, which is problematic for short product life cycles
Solution Approach 1:
Instead of waiting to accumulate more real historical batches over time, the patent creates copies of existing batch data through simulation. These synthetic batches are generated rapidly using statistical methods and process knowledge, providing sufficient training data immediately without the time-consuming process of waiting for additional physical batches to be produced and recorded
Solution Approach 2:
The patent changes the parameter of data availability by transforming limited historical batch data into a larger dataset through simulation. By adjusting simulation parameters and generating synthetic variations of process data, the system effectively increases the volume of available training data without requiring additional time for physical batch production
3Measurement precision
If simulated batch datasets are generated to supplement historical data, then the effectiveness of real-time monitoring is improved with limited data, but the complexity of the data generation process increases
Solution Approach 1:
The patent uses copying principles to generate simulated batch datasets that replicate the structure, distribution, and relationships of real historical batch data. By creating synthetic copies rather than requiring complex physical experiments or additional batch production, the system achieves enhanced monitoring effectiveness through a relatively streamlined data generation approach
Solution Approach 2:
The patent introduces simulated batch datasets as an intermediary between limited historical data and the CPV model requirements. These synthetic datasets act as a mediator that bridges the gap, allowing the model to be trained effectively without requiring either more physical batches or extremely complex data generation methodologies
Data Source
AI summary
Methods and apparatus for using analytical/statistical modeling to perform continued process verification (CPV) are described. Example methods include determining distribution characteristics for a plurality of parameters based on a first historical batch dataset measured while manufacturing a first batch at a first time, and generating a model batch based on the distribution characteristics of the plurality of parameters. Example methods also include generating a first set of simulated batch datasets corresponding to a first set of simulated batches by, for each one of the first set of simulated batches: generating values for the plurality of parameters based on the model batch, and determining a quality prediction based on the generated values. The example methods also include generating a model based on the first set of simulated batch datasets and the first historical batch dataset. The model is to be implemented to monitor a subsequent manufacture of a second batch.