TY - JOUR ID - 35666 TI - Salty Water Desalination Modeling Using Electrodialysis Process and Neural Network Technique JO - Nashrieh Shimi va Mohandesi Shimi Iran JA - NSMSI LA - en SN - 1022-7768 AU - Behroozi, Amir Hossein AU - Zourmand, Zahra AU - Kasiri, Norollah AD - The School of Chemical, Petroleum and Gas Engineering, Iran University of Science and Technology (IUST),, Tehran, I.R. IRAN Y1 - 2021 PY - 2021 VL - 39 IS - 4 SP - 185 EP - 192 KW - Electrodialysis KW - Modeling KW - Desalination KW - Neural network DO - N2 - In this study, a model is presented using the black box neural network technique. In this model, the effects of four basic parameters, including temperature, voltage consumption, flow rate, and feed concentration on salt separation percentage from salty water, have been investigated. With the help of trial and error, the training method, transfer function, and the optimal number of neurons on each layer are selected to provide the best network performance. Multi-layer neural network, backpropagation, and Levenberg-Marquardt algorithm are utilized as the optimum options. In this study, 135 data were used, allocating 60% of them (81 data) to network training, 20% (27 data) to training data evaluation, and the remaining 20% (27 data) to assess the network generalizability as test data. Finally, a comparison of the model results with independent laboratory data indicates that the optimal network arrangement is 4:5:8:1, and the model with an error of less than 1% can predict the process behavior. UR - https://www.nsmsi.ir/article_35666.html L1 - https://www.nsmsi.ir/article_35666_4f85c6172120e5626ebf1b48e0004be1.pdf ER -