Journal
JOURNAL OF CLEANER PRODUCTION
Volume 170, Issue -, Pages 147-159Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2017.09.092
Keywords
Solar still; Solar desalination; Productivity; ANN; Prediction; Modelling; Performance evaluation
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Funding
- Universiti Kebangsaan Malaysia [GUP-2016-020]
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This paper presents a cascaded forward neural network model for predicting the productivity of a developed inclined stepped solar still system. The actual recorded data of the developed inclined stepped solar still system is used to develop the proposed model. The results of the predicted productivity are compared with that obtained from regression and linear models. In this study, three statistical error terms are used to evaluate the proposed model: root mean square error (RMSE), mean absolute percentage error (MAPE) and mean bias error (MBE). The results show that the proposedcascaded forward neural network (CFNN) model more accurately predicts the productivity of the system than the other modelsmentioned. The RMSE, MAPE and MBE values of the proposed model are 22.48%, 18.51% and -26.46%, respectively. Therefore, the CFNN model provides benefits for modelling the solar still. (C) 2017 Elsevier Ltd. All rights reserved.
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