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).
Highlights
Fresh water and energy are closely related issues
To improve first made linear regression model, which clearly were overestimated when compared with experimental data taken from the field tests
Linear regression model, which clearly were overestimated when compared with experimental data taken from the field tests
Summary
Fresh water and energy are closely related issues. Both are critical and mutually dependent resources, especially in dry and/or isolated areas. Desalination of seawater and brackish water is maybe the unique solution to alleviate freshwater scarcity nowadays [3] It is an energy intensive process, since distillation processes such as. Multi-Stage Flash (MSF), Multi-Effect Distillation (MED) and Membrane Distillation could consume about 50–70, 40–60 and 120–1700 kWh of thermal energy per cubic meter of distillate respectively, whereas membrane techniques such as Reverse Osmosis (RO) consume about 3 to 6 kWh of electricity per cubic meter of permeate [4] This close relationship has been dealt with, even in oil rich countries, in which RES are planned to cover the 100% of the energy demand, including desalination [5]. Membrane distillation (MD) can help to reduce the water-energy stress that societies are facing, especially at a reduced scale and when waste heat from other thermal processes or solar energy is freely available
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