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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.

High purity distillation columns and multi-stream heat exchangers (MSHXs) are critical units in cryogenic air separation plants. This article focuses on modeling approaches for the primary section of a super-staged argon plant. A fullorder stage-wise model for distillation columns in air separation units (ASUs) that considers key process phenomena is presented, followed by a reduced-order model using a collocation approach. The extent of model reduction that can be achieved without losing significant prediction accuracy is demonstrated. A novel moving boundary model is proposed to handle MSHXs with phase change. Simulation results demonstrate the capability of the proposed model for tracking the phase change occurrence along the length of the heat exchanger. Dynamic simulation studies of the integrated plant show that the thermal integration between the feed and product streams captured in the primary heat exchanger is critical to accurately capture the behavior of ASUs.

A terminal iterative learning control (ILC) strategy for batch-to-batch and within-batch control of final product properties, based on empirical partial least squares (PLS) models, is presented. The strategy rejects persistent process disturbances and achieves new final product quality targets using an iterative procedure that works in the reduced space of a latent variable model rather than in the high dimensional space of the manipulated variable trajectories. Complete manipulated variable trajectory reconstruction is then achieved by exploiting the PLS model of the process. The approach is illustrated with a condensation polymerization example for the production of nylon.

The manufacture of emulsion polymers with broad and bimodal particle size distribution (PSD) through in situ particle nucleation in semibatch reactors is difficult because of the sensitivity of particle nucleation phenomena to variations in reactor conditions, impurities, and surfactant and initiator properties. In this paper, we present several control strategies based on the use of readily available online and offline measurements to predict the final PSD and, if necessary, to compute mid-course corrections. Partial least squares (PLS) models are used to extract the necessary information from different sets of measurements, to predict the final PSD, and to define a control region, outside of which mid-course control actions are deemed necessary. Using a simulated styrene emulsion polymerization process as an example, these control strategies are shown to be highly effective and very practical for industrial implementation.

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