Convolutional Neural Network Applied in Anaerobic Reactors of Domestic Sewage

Abstract

Abstract The need for sustainability within the ETES (sewage treatment stations), to take advantage of the residue and turn it into raw materials, depends on an important step which is the care with the anaerobic reactor variables. Only with care at the beginning of the sewage treatment process will it be possible to ensure a waste that can be used as a raw material for commercial use or in the station itself. To this end, based on monitoring data obtained in scientific work, convolutional neural networks were trained to model and indicate that the use of artificial intelligence in domestic sewage treatment stations was feasible. Data obtained from two scientific papers were grouped in the same ETE, in the same temporal lapse, the data were refined and normalized. In this article, RNA was applied to predict the methane flow (CH4), the model has good estimates for the data sets. The results obtained show that the developed model provides reliable estimates with response variables and shows that the most effective architecture of forecasts was using convolutional networks with a hidden layer with 8 neurons and sigmoid activation function, this configuration was obtained in the greatest convergence in Prediction of methane flow.