%O cited By 5
%L scholars11400
%J Chemical Engineering Communications
%D 2019
%R 10.1080/00986445.2018.1539710
%N 8
%K Condensers (liquefiers); Greenhouses; Neural networks; Regression analysis; Seawater, Artificial neural network simulation; Coolant temperature; Mean absolute percentage error; Multi-linear regression; Pearson correlation; Production rates; Solar intensities; Statistical criterion, Forecasting
%X The prediction of freshwater production from the condenser of an agricultural seawater greenhouse is important for designing the greenhouse process. Two models, namely, Artificial Neural Network and multilinear regression (denoted as ANN and RA, respectively), were developed and tested to predict the freshwater production rate considering ambient solar intensity, condenser inlet moist-air temperature, humidity ratio and mass flowrate, and inlet coolant temperature. Statistical analysis indicated that all parameters significantly affected the prediction (p < 0.05). The accuracy of the ANN and RA models was then compared to two models previously developed by Yetilmezsoy and Abdul-Wahab and Al-Ismaili et al. (denoted as Yetilmezsoy model and Al-Ismaili model, respectively). The ANN model showed the best prediction when seven statistical criteria were considered. The Pearson correlations for ANN, RA, Yetilmezsoy, and Al-Ismaili models were observed as 1.00, 0.98, 0.88, and 0.96, respectively, while mean absolute percentage errors (MAPE) were 17.84, 79.72, 63.24, and 80.50, respectively. Hence it could be recommended to use ANN model for the prediction of freshwater production rate, however other three simple models could also be used with lower accuracy in the cases of unavailability of the ANN model. © 2019, © 2019 Taylor & Francis Group, LLC.
%P 967-985
%T Artificial neural network simulation of the condenser of seawater greenhouse in Oman
%A A.M. Al-Ismaili
%A N.M. Ramli
%A M. Azlan Hussain
%A M.S. Rahman
%I Taylor and Francis Ltd.
%V 206