Multi-objective optimization based nonlinear model predictive control of seeded cooling crystallization process with application to β form L-glutamic acid

For batch performance optimization of seeded cooling crystallization processes with respect to multiple production objectives, a multi-objective optimization (MOO) based nonlinear model predictive control (NMPC) design is proposed in this paper. By taking into account three important production objectives related to the target crystal size, yield, and batch time, respectively, an MOO program is established with respect to the important operation conditions of seed loading ratio, initial solution supersaturation and cooling temperature profile. To find a good trade-off between these cross-coupled objectives, an enhanced goal attainment method (EGAM) is adopted to acquire the Pareto solution set for the above MOO program, by taking a piece-wise linear cooling profile for implementation. Then a hybrid decision making (HDM) strategy is developed to determine the optimal compromise solution. Based on the optimized objectives and operation conditions, a NMPC scheme is established for batch run of the seeded cooling crystallization process. Meanwhile, a receding-horizon nonlinear Kalman filter (RNKF) is designed to estimate the zero- and third-order moments of crystal population (related to the total number and volume of crystals) during crystallization for control implementation. Moreover, the kinetic model parameters with higher impact on the NMPC scheme are timely updated by moment estimation to improve system performance under time-varying uncertainties. Simulation results and experiments on the seeded cooling crystallization of beta form L-glutamic acid (beta-LGA) are shown to demonstrate the effectiveness and advantage of the proposed optimization and control scheme.