Artificial Intelligence Predictions of Biomass Power of an Installed Waste Water Treatment Plant

Abstract

In the current study, the power generations obtained from gas turbines of an installed waste water treatment plant were predicted, utilizing artificial intelligence method consisting of artificial neural network (ANN). In this regards, a cumulative of 445 data, found in the power generation data cluster and found in the physical and chemical data clusters has been used in the predictions based on the artificial intelligence association method. Each instant data of these total 445 data corresponds to daily average power generation (P) obtained from gas turbines of the facility and corresponds to physical and chemical parameters including the temperature (T), degree of acidity (pH), conductivity (σ), as well as the daily total volumetric flow of the waste gas to be burned at the gas generator (Q). Accordingly, the best prediction obtained by ANN approach was concluded to generate the statistical accuracy results corresponding to 6.1279% mean absolute percentage error (MAPE), 2.1540 MWh/day root mean square error (RMSE), and 0.9730 correlation coefficient (R) for power generation parameter.