Predicting the permeate flux is critical for evaluating and optimizing the performance of the forward osmosis (FO) process. However, the solution diffusion models have poor applicability in accessing the FO process. Recently, the data-driven eXtreme Gradient Boosting (XGBoost) algorithm has been proven to be effective in processing structure data in engineering problems and has not been utilized to assess the FO process. Herein, a combination of the XGBoost model with a genetic algorithm (GA) was first proposed to predict the permeate flux, highlighting its superiority in the FO process through comparison of the support vector regression (SVR) model, the artificial neural network (ANN), and the multiple linear regression (MLR). Moreover, the performance of these models was optimized by tuning hyperparameters with a genetic algorithm (GA) and compared via Taylor Diagram. Among these machine learning (ML) models, the GA-based XGBoost model is superior to the other three models in terms of mean square error (MSE, 2.7326) and coefficient of determination (R 2, 0.9721) on the test data, and its prediction power was compared to that of the solution diffusion (SD) model in the literature. Finally, further insight into the feature importance that affects the permeate flux in the FO process was examined by utilizing the SHapley Additive exPlanations (SHAP) to estimate the contribution value of various variables. The results demonstrated that the XGBoost model could predict the permeate flux in the FO system with high accuracy and good generalization ability for the given data set and even on the unseen data. Furthermore, the findings of the SHAP method show that the osmotic pressure difference, the osmotic pressure difference of draw solution and FS solution, the crossflow velocity of the feed solution and draw solution, and the water permeability coefficient have a significant impact on water flux.

A systematic procedure is proposed for the design of a pressure-swing distillation (PSD) process for a ternary mixture. This procedure involves thermodynamic insight, global optimization, and heat integration. The aim of thermodynamic insight is to analyze the process feasibility and find all of the possible separation sequences via residue curve maps and the distillation boundaries. The global optimization for a total of 16 variables in three columns is conducted by multiobjective genetic algorithm (MOGA) with economy and environmental impact as objectives. In heat integration, a four-step heat integration MOGA method was first proposed to optimize the heat-integrated PSD process and the optimally integrated heat duty was achieved. The separation of a methanol/tetrahydrofuran/ water ternary mixture is employed to elucidate the effectiveness of the proposed procedure. An energy-saving heat-integrated PSD process is obtained, and the total annual cost and CO 2 emissions are reduced by 22.5 and 30.4% compared with the conventional PSD process.