Executive Summary This report summarizes the main achievements of the second year’s effort of development of new crystallization technologies for improved crystal size and shape control during the crystallization process. The successful crystallization process and system design requires an interdisciplinary effort, which ranges from population balance model (PBM) development of the system concept, through efficient implementation of model equations to soft-sensor development, which is required for the model predictive control (MPC) design as well. This report gives a deeper insight into these interdisciplinary development efforts, which also highlights the achievable improvements enabled by the combination of process modeling, high performance process simulation and optimization. In the first report we showed that the application of wet-milling during crystallization can significantly improve the process flexibility and attainable crystal size domain. The GPU acceleration halved the simulation time, which enabled faster optimization. In this year, we applied the same principles, but we investigate the effect of milling in addition to crystal size also on the crystal shape. Since the computational cost associated to numerically solve the model equations is a power-law function of number of dimensions, the application of 2D PBMs for this purpose is not feasible without GPU acceleration, which in this case brought ~1.5 order of magnitude speedup. The first, full PBM based nonlinear model predictive control (NMPC) was implemented for the batch crystallization of L-ascorbic acid. The NMPC showed good control behavior, produced significantly better crystals than the direct nucleation control under considerably shorter batch time. The novel aspect of this NMPC was that it applied the fast, approximate CSDCLD transformation, that was presented in the previous report. The development work of the analogue control system for simultaneous size and shape control has been started. A major challenge, associated to the calculation time was solved. Realized deliverables 1. Model development, simulation and preliminary optimization of an integrated batch crystallizer-wet mill system for bivariate crystal size distribution control 2. Implementation of a full PBM based NMPC for crystal size distribution control 3. Preliminary development work of a real-time feasible full 2D PBM based adaptive predictive simultaneous crystal size and shape control algorithm
A Holistic Approach for the Model-Based Control of Crystal Size, Shape and Purity in Integrated Batch and Continuous Crystallization-Wet Milling Systems
Publication Reference:
ARR-21-08Report Type:
ARR - Annual ReportResearch Area:
Systems EngineeringPublication Year:
2018Publication Month:
12Country:
United States