Accelerating the Multi-Objective Optimization of Crystallization Process using High Fidelity Population Balance Model Under NUMBA CUDA Environment

Crystallization is one of the majorly used separation techniques owing to its high efficiency and ease of operation. The optimization of the crystallization process involves the high-fidelity population balance models, thereby significantly increasing the computational complexity. This study, therefore, explores the acceleration of the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) using NUMBA CUDA for multi-objective optimization for crystallization of ortho-amino benzoic acid. By harnessing GPU parallel processing, significant reductions in computation time are achieved, particularly for large populations and complex optimization problems. The GPU-accelerated NSGA-II algorithm in the optimization of ortho amino benzoic acid crystallization demonstrates significant performance boost by reducing the time by 122 times without any reduction in the solution quality. This research, therefore, underscores the potential of GPU acceleration in evolutionary algorithms and sets the stage for further advancements in high-performance parallelized optimization techniques, promising significant improvements in real-time optimization scenarios.