Improving the distillate prediction of a membrane distillation unit in a trigeneration scheme by using artificial neural networks

Abstract

An Artificial Neural Network (ANN) has been developed to predict the distillate produced in a permeate gap membrane distillation (PGMD) module with process operating conditions (temperatures at the condenser and evaporator inlets, and feed seawater flow). Real data obtained from experimental tests were used for the ANN training and further validation and testing. This PGMD module constitutes part of an isolated trigeneration pilot unit fully supplied by solar and wind energy, which also provides power and sanitary hot water (SHW) for a typical single family home. PGMD production was previously estimated with published data from the MD module manufacturer by means of a new type in the framework of Trnsys® simulation within the design of the complete trigeneration scheme. The performance of the ANN model was studied and improved through a parametric study varying the number of neurons in the hidden layer, the number of experimental datasets and by using different activation functions. The ANN obtained can be easily exported to be used in simulation, control or process analysis and optimization. Here, the ANN was finally used to implement a new type to estimate the PGMD production of the unit by using the inlet parameters obtained by the complete simulation model of the trigeneration unit based on Renewable Energy Sources (RES).