Toward Faster Operational Optimization of Cascaded MSMPR Crystallizers Using Multiobjective Support Vector Regression

Mixed-suspension mixed-product removal (MSMPR) crystallization process is critical for optimal separation and purification operations in pharmaceutical and fine chemical industries. To achieve this, detailed mathematical model-based optimization is the current practice, which is reported as an extremely time-consuming exercise, prohibiting their online implementation. To facilitate faster optimization, a novel data-driven modeling and optimization algorithm has been proposed in this work. Limited number of high-fidelity data from the computationally expensive model were utilized to build surrogates using support vector regression (SVR). Inputs of MSMPR are assigned different, instead of same, kernel parameters, and multiple kernels were explored to capture the complex dynamics of the crystallization process. Loaded with a sample size estimation algorithm to reduce the computational load on model execution time, the complete formulation is solved using an evolutionary algorithm enabling evolution of optimal SVR-based surrogate models. From the Pareto optimal set of several such models, two instances with differing prediction error were selected and the optimization of MSMPR was performed. The study indicates 3 orders of magnitude faster optimization with the surrogate models and 87.54% savings in the number of expensive function evaluations compared to the physics-based MSMPR model facilitating online optimization of such a process.