Recursive Gaussian Process Regression Model for Adaptive Quality Monitoring in Batch Processes

Le Zhou,Junghui Chen,Zhihuan Song

doi:10.1155/2015/761280

Abstract

In chemical batch processes with slow responses and a long duration, it is time-consuming and expensive to obtain sufficient normal data for statistical analysis. With the persistent accumulation of the newly evolving data, the modelling becomes adequate gradually and the subsequent batches will change slightly owing to the slow time-varying behavior. To efficiently make use of the small amount of initial data and the newly evolving data sets, an adaptive monitoring scheme based on the recursive Gaussian process (RGP) model is designed in this paper. Based on the initial data, a Gaussian process model and the corresponding SPE statistic are constructed at first. When the new batches of data are included, a strategy based on the RGP model is used to choose the proper data for model updating. The performance of the proposed method is finally demonstrated by a penicillin fermentation batch process and the result indicates that the proposed monitoring scheme is effective for adaptive modelling and online monitoring.

Highlights

In modern industries, batch and semibatch processes are of great importance for the production of high-quality and value-added specialty chemicals, such as semiconductors, pharmaceuticals, and biological products
An adaptive quality monitoring scheme is proposed based on the recursive Gaussian process (RGP) model
A recursive Gaussian process (GP) model is used for implementation of updating

Summary

Introduction

Batch and semibatch processes are of great importance for the production of high-quality and value-added specialty chemicals, such as semiconductors, pharmaceuticals, and biological products. It is time-consuming and expensive to obtain data from slow chemical processes, such as emulsion polymerization, fermentation, and pharmaceutical and biotechnical products [15] In this situation, it is not proper to construct monitoring models after all the sufficient data are collected. It is interesting to design a statistical scheme to construct an initial model using limited batch data and adapt the strategy when newly evolving batch data arrive. A recursive Gaussian process (RGP) model is designed for adaptive quality monitoring with limited initial batch data.

GP Regression Model

Adaptive Quality Monitoring Based on RGP in Batch Processes

Conclusion