Prediction of temperature elevation for seawater in multi-stage flash desalination plants using radial basis function neural network

In this paper, a radial basis function (RBF) neural network model was developed for estimating temperature elevation (TE) in multi-stage flash (MSF) desalination processes. The constructed artificial neural network (ANN) model use as input variables the boiling point temperature (BPT) and salinity. The developed RBF neural network was found to be precise in predicting TE from the input variables. The performance of the ANN model was analyzed by mean squared error (MSE). The developed RBF neural network was found to be highly precise in predicting TE for the new input data, which are kept unaware of the trained network showing its applicability to estimate the TE for seawater in MSF desalination plants better than the empirical correlations, thermodynamic models and MLP neural network.