Modeling and optimization of solar thermal-photovoltaic vacuum membrane distillation system by response surface methodology. (1st January 2020)

• Record Type: Journal Article
• Authors: Deng, Hongling; Yang, Xiaohong; Tian, Rui; Hu, Junhu; Zhang, Bo; Cui, Fangda; Guo, Guangyu
• Is Part Of: Solar energy. Volume 195(2020)
• Page Start: 230
• Page End: 238
• Publication Date: 2020-01-01
• Subjects: Solar thermal-photovoltaic system -- Membrane distillation -- Response surface methodology -- Regression models -- Permeate flux -- Energy consumption; Solar energy -- Periodicals; Solar engines -- Periodicals; 621.47
• DOI: 10.1016/j.solener.2019.11.006
• Languages: English
• ISSNs: 0038-092X

Abstract

Highlights: Designed the solar thermal-photovoltaic vacuum fiber membrane distillation system. Applied the response surface methodology to optimize the operation parameters in solar thermal-photovoltaic membrane distillation system. Applied the ANOVA to analyze and verify the RSM mathematical model. Obtained the optimal operation conditions, the corresponding permeate flux and energy consumption in RSM model and the experiments. Abstract: Response surface methodology is used in this study to model and optimize a solar thermal-photovoltaic vacuum membrane distillation (STPVMD) system with PVDF hollow fiber membrane. Regression models have been developed to forecast the effect of various operation factors on the permeate flux and the energy consumption. The operation factors in the model include the feed inlet temperature, the feed flow rate and the vacuum pressure. Analysis of variance is used to statistically validate the regression models. With permeate flux as the objective function of optimization, the optimal operation parameters are found to be 63 °C for the feed inlet temperature, 237 L / h for the feed flow rate, and 750 kPa for the vacuum pressure. Experimental tests show that the resulting permeate flux is 6.26 L / m 2 · h, which is slightly higher than the predicted value of 6.05 L / m 2 · h . The corresponding energy consumption is predicted to be 12.52 L / kW · h, demonstrating the effectiveness and reliability of the models.