Summary An adsorption, heat and mass transfer model for the five‐component gas from coal gas (H2/CO2/CH4/CO/N2 = 38/50/1/1/10 vol%) in a layered bed packed with activated carbon and zeolite was established by Aspen Adsorption software. Compared with published experimental results, the hydrogen purification performance by pressure swing adsorption (PSA) in a layered bed was numerically studied. The results show that there is a contradiction between the hydrogen purity and recovery, so the multi‐objective optimization algorithms are needed to optimize the PSA process. Machine learning methods can be used for data analysis and prediction; the polynomial regression (PNR) and artificial neural network (ANN) were used to predict the purification performance of two‐bed six‐step process. Finally, two ANN models combined with sequence quadratic program (SQP) algorithm were used to achieve multi‐objective optimization of hydrogen purification performance. According to the analysis of the optimization results, the ANN models are more suitable for optimizing the purification performance of hydrogen than the PNR model.

A computational tool integrated into a commercial simulation software (CHEMCAD) is developed for the analysis and synthesis of crystallization processes. The tool utilizes the comprehensive thermodynamic models and the rigorous computational algorithms available. The crystallization calculations are formulated as multicomponent multiphase equilibrium phase calculations and support the analysis of various modes of crystallization, such as cooling, evaporative, and the use of mass separating agents such as antisolvents and cosolvents. The tool is demonstrated with applications related to the crystallization of API's and LCD crystals.