Swee-Teng Chin

Profile-driven Features for Offline Quality Prediction in Batch Processes

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.

Managing Uncertainty Information for Improved Data-Driven Modelling

Abstract Obtaining uncertainty information and using it in an optimized way in the context of the various PSE tasks, is still a challenge in the modern Chemical Processing Industry (CPI). Among the variables most affected by measurement uncertainty, one typically finds the process outputs. Examples include stream concentrations (main product and by-products) and measurements of quality properties (mechanical, chemical, etc.). With the increasing flexibility of processing units, these quantities can easily span different orders of magnitude and present rather different uncertainties associated with their measurements. Therefore, heteroscedasticity in the process outputs (Y’s) is a prevalent feature in CPI. In this article we address two related and complementary aspects of this problem with practical relevancy: i) how to estimate measurement uncertainty in heteroscedastic contexts? ii) how to take advantage of the availability of measurement uncertainty information for optimal model development?

A Study of Experimental Design Optimality for Wiener Systems

In the context of nonlinear dynamic system identification for Hammerstein systems, Rollins et al. (2003a) studied the information efficiency of the following two competing experimental design approaches: statistical design of experiments (SDOE) and pseudo-random sequences design (PRSD). The focus of this study is the Wiener system and evaluates SDOE against PRS under D-optimal efficiency. Three cases are evaluated and the results strongly support SDOE as the better approach.