Within-Batch and Batch-to-Batch Inferential-Adaptive Control of Semibatch Reactors:  A Partial Least Squares Approach

Abstract

An inferential control strategy that combines within-batch information from process variable trajectories and information from prior batches to control multivariate product quality properties in semibatch reactors is presented. The approach extends mid-course correction (MCC) strategies by including batch-to-batch information in the controllers and an adaptive partial least squares (PLS) approach to update the models from batch to batch. As with other MCC approaches, the scheme retains the “no-control region” concept where control is taken at various stages during the batch only if the projected error in the final quality is deemed to be statistically significant. Only data on readily available process measurements (e.g., temperatures) throughout the batch, plus a measurement on a variable related to quality (e.g., particle size) at one or more discrete times during the batch, are required to achieve very precise control of the final product quality (e.g., particle-size distribution, PSD). Latent variable models based on PLS are a key element in the approach. They are able to extract information efficiently from the large number of highly correlated measurements on the process variable trajectories and relate it to high-dimensional output measurements on product quality (e.g., PSD) by projecting this information into low-dimensional latent variable spaces. The methodology is applied to the control of PSD in emulsion polymerization. The problem of regulation about a fixed set-point PSD in the face of disturbances and the problem of achieving new set-point PSDs are both illustrated.

Application of latent variable methods to process control and multivariate statistical process control in industry

“…Empirical models for the control of batch product quality where the control action was restricted to only a few movements in the manipulated variables (additional reactant injections) have appeared in the literature and applied in industrial problems [30,73,74]. Obviously these approaches can be used in situations where few adjustments are enough to compensate for disturbances and bring the desired end product quality within specifications.…”

Control and Optimization of Batch Chemical Processes

“…When the detailed dynamic model (1) is not available, it is possible to use partial least-squares (PLS) regression models identified from historical data and a few additional experi-ments 38 …”

Advanced Process Decision Making Using Multivariate Latent Variable Methods

“…The effectiveness of LVMs in such activities as process understanding, process monitoring and troubleshooting [16], process control [37][38][39][40], process design [41][42][43], product design [44], and optimization [45] has been proved in several industrial sectors.…”