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学者姓名：谷静连
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Abstract ：
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 (R2, 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.
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| GB/T 7714 | Shi, Fengming , Lu, Shang , Gu, Jinglian et al. Modeling and Evaluation of the Permeate Flux in Forward Osmosis Process with Machine Learning [J]. | INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH , 2022 . |
| MLA | Shi, Fengming et al. "Modeling and Evaluation of the Permeate Flux in Forward Osmosis Process with Machine Learning" . | INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH (2022) . |
| APA | Shi, Fengming , Lu, Shang , Gu, Jinglian , Lin, Jiuyang , Zhao, Chengxi , You, Xinqiang et al. Modeling and Evaluation of the Permeate Flux in Forward Osmosis Process with Machine Learning . | INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH , 2022 . |
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Abstract ：
An improved model for calculating physical properties and phase behavior of confined fluids (oil and gas resources in shale reservoirs) by considering adsorption film theory was developed based on the square-well chain-like fluid with variable well-width range (SWCF-VR) equation of state. The accuracy of the improved model is greatly increased via comparing the experimental data of argon in cylindrical pores at 87.3 K. The physical properties of pure component hydrocarbons, mixture hydrocarbons, and real Bakken oils in nanopores were predicted and analyzed. The results show that the properties of confined fluids are very different from those of bulk-phase fluids, where confinement decreases the gas-liquid phase equilibrium constant (K-value), bubble point pressure, and interfacial tension of the fluid. The presence of the adsorption film further decreases the K-value and bubble point and increases the capillary pressure, and these properties change more significantly in pore radius with only a few nanometers. The results demonstrate the importance of improving the accuracy in calculating the properties and phase behaviors of confined fluids and also draw the necessity of considering the adsorption film theory.
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| GB/T 7714 | Fang, Fei , Gu, Jinglian , You, Xinqiang . Improved Model for Calculating Physical Properties of Confined Fluid by Considering Adsorption Film Theory Based on the SWCF-VR Equation of State [J]. | INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH , 2021 , 60 (35) : 13094-13106 . |
| MLA | Fang, Fei et al. "Improved Model for Calculating Physical Properties of Confined Fluid by Considering Adsorption Film Theory Based on the SWCF-VR Equation of State" . | INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH 60 . 35 (2021) : 13094-13106 . |
| APA | Fang, Fei , Gu, Jinglian , You, Xinqiang . Improved Model for Calculating Physical Properties of Confined Fluid by Considering Adsorption Film Theory Based on the SWCF-VR Equation of State . | INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH , 2021 , 60 (35) , 13094-13106 . |
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