relation: https://khub.utp.edu.my/scholars/11400/
title: Artificial neural network simulation of the condenser of seawater greenhouse in Oman
creator: Al-Ismaili, A.M.
creator: Ramli, N.M.
creator: Azlan Hussain, M.
creator: Rahman, M.S.
description: 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.
publisher: Taylor and Francis Ltd.
date: 2019
type: Article
type: PeerReviewed
identifier:   Al-Ismaili, A.M. and Ramli, N.M. and Azlan Hussain, M. and Rahman, M.S.  (2019) Artificial neural network simulation of the condenser of seawater greenhouse in Oman.  Chemical Engineering Communications, 206 (8).  pp. 967-985.  ISSN 00986445     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064008302&doi=10.1080%2f00986445.2018.1539710&partnerID=40&md5=d9a46dee4b9d33a2864eb1584f0f447a
relation: 10.1080/00986445.2018.1539710
identifier: 10.1080/00986445.2018.1539710