Profile-driven Features for Offline Quality Prediction in Batch Processes

Abstract

Abstract The present paper addresses the task of quality prediction in batch processes, where measurements from process variables are used to predict one or more quality variables of interest. The majority of current methods for batch quality prediction are based on complete time profiles for all variables, requiring synchronization before batch-wise unfolding (BWU). Synchronization is complex to implement and requires trained personnel whereas BWU leads to a matrix with thousands of pseudo-variables, increasing the potential for model overfitting. In this context, the development and validation of reliable data-driven predictive models is challenging and time consuming. On the other hand, low complexity approaches for batch processes remain vastly unexplored and only a few examples are available in the literature. Therefore, in this work we present a new methodology called profile-driven features (PdF) for offline quality prediction. PdF presents low modelling and implementation complexity, is able to cope with the dynamics presented by batch process variables and generate useful features for building predictive models. In order to test the proposed method, datasets from two simulated batch processes were obtained and partial least squares models were developed to predict end-of-batch quality parameters. Upon comparison with the benchmark method based on BWU and other feature-oriented approaches, PdF presented similar or superior prediction performances under independent testing conditions, despite its lower complexity.