Comparison of adaptive neuro-fuzzy inference system and multiple nonlinear regression for the productivity prediction of inclined passive solar still

• Published in: Journal of water supply : research and technology - AQUA Vol. 68; no. 2; p. 98
• Main Authors: Mashaly, Ahmed F, Alazba, A A
• Format: Journal Article
• Language: English
• Published: Oxford IWA Publishing 01.03.2019
• Subjects: Adaptive systems; Aluminum; Artificial neural networks; Brines; Construction; Correlation coefficient; Correlation coefficients; Desalination; Energy; Feeds; Flow rates; Flow velocity; Fuzzy logic; Fuzzy systems; Galvanized steel; Inference; Model accuracy; Modelling; Nonlinear systems; Performance evaluation; Predictions; Productivity; Project management; Project planning; Relative humidity; Root-mean-square errors; Saline water; Seawater; Solar radiation; Total dissolved solids; Water desalting; Saudi Arabia; United Kingdom > UK; Australia; United States > US
• ISSN: 1606-9935; 1605-3974
• DOI: 10.2166/aqua.2019.058

Solar still productivity (SSP) is of vital importance in solar desalination project planning and management. In this investigation, the applicability of adaptive neuro-fuzzy inference systems (ANFIS) and multiple nonlinear regression (MNLR) in modeling SSP is investigated. Eight different membership functions (MFs) were used with ANFIS approach. Solar radiation, relative humidity, feed flow rate, total dissolved solids of feed, and brine are used as inputs to the models. The outcomes of the ANFIS are compared with those of the MNLR with respect to correlation coefficient (CC), root mean square error (RMSE), overall index of model performance (OI), and mean absolute error (MAE). Comparison results illustrate the generalized bell MF with ANFIS model has better accuracy than the other seven MFs in modeling SSP. Performance evaluation criteria show the predictive abilities of ANFIS and MNLR models were very similar and can be suggested to predict SSP effectively. Using the ANFIS model, the average value of CC, RMSE, OI, and MAE was 0.96, 0.05 L/m2/h, 0.91, and 0.04 L/m2/h, respectively. The corresponding values for the MNLR model were CC = 0.97, RMSE = 0.06 L/m2/h, OI = 0.93, and MAE = 0.05 L/m2/h. One of the advantages of MNLR model is using explicit equations.