Control of an Industrial Distillation Column Using a Hybrid Model with Adaptation of the Range of Validity and an ANN‐based Soft Sensor

An adaptive predictive control scheme is applied to a simulation of an industrial distillation column with varying flowrate and composition of the feed. A hybrid model is used that comprises a basic mechanistic model and a data-based component. An artificial neural network based soft sensor is employed to estimate the unmeasured compositions of the product streams.

Abstract

Advanced control schemes such as model predictive control can be used to minimize the use of resources while guaranteeing the specified product quality. In this paper, we consider an industrial mother liquor distillation column varying flow rate and composition of the feed. There are specifications of the composition for all product streams. To address this challenging control problem, we employ a nonlinear model-predictive controller using a hybrid model, which consists of a simple phenomenological model augmented by a data-based component to compensate the plant-model mismatch. The trustworthiness of the data-based model is addressed using a domain of validity of the data-based model, which is estimated using a one-class support vector machine. During operation, it may turn out that the model is also reliable in a wider range, therefore, data of recently visited operating points is recorded and the domain of validity is extended if the model is sufficiently accurate. To improve the performance of the controller, an artificial neural network model is used to estimate the product composition from available measurements.

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