Artificial neural networks applied to manage the variable operation of a simple seawater reverse osmosis plant

It is concluded that the ANN models that were used after training and validation were able to successfully manage the random and widely varying available electrical power. The statistical hypothesis testing that was also performed showed no significant statistical differences (at 5% level) between the errors (both MAE and MAPE) committed when adapting power consumption of the plant to the available electrical power in the various operational tests using different feedwater characteristics.