Nonlinear Model-Based Control of a Semi-Industrial Batch Crystallizer Using a Population Balance Modeling Framework


This paper presents an output feedback nonlinear model-based control approach for optimal operation of industrial batch crystallizers. A full population balance model is utilised because the cornerstone of the control approach. The modeling framework permits us to explain the dynamics of a wide selection of business batch crystallizers. In addition, it facilitates the employment of performance objectives expressed in terms of crystal size distribution. The core element of the management approach is an optimal control downside, that is solved by the direct multiple shooting strategy. To ensure the effectiveness of the optimal operating policies in the presence of model imperfections and method uncertainties, the model predictions are adapted on the basis of online measurements employing a moving horizon state estimator. The nonlinear model-primarily based management approach is applied to a semi-industrial crystallizer. The simulation results counsel that the feasibility of real-time management of the crystallizer is largely enthusiastic about the discretization coarseness of the population balance model. The control performance will be greatly deteriorated because of inadequate discretization of the population balance equation. This results from structural model imperfection, which is effectively compensated for by using the net measurements to confer an integrating action to the dynamic optimizer. The important-time feasibility of the output feedback management approach is experimentally corroborated for fed-batch evaporative crystallization of ammonium sulphate. It's observed that the use of the management approach leads to a substantial increase, i.e., up to 15percent, in the batch crystal content as the product quality is sustained.

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