A COMPARATIVE STUDY OF DEEP LEARNING METHODS APPLIED FOR WASTEWATER pH NEUTRALIZATION PROCESS MODELLING

In a wastewater treatment plant (WWTP) there are several different, intricate processes with a dynamic and nonlinear behaviour. The fact that these processes are nonlinear, some of them having a high degree of nonlinearity, as is the wastewater pH neutralization process, comes with a number of problems related to their modelling and control. The identification of any method that can be used to simplify the modelling and control of such a high nonlinear process, it is a desideratum to ensure a quality effluent of the plant, because its water quality is affected by the treated wastewater discharged into it. The Deep Learning (DL) and Machine Learning (ML) techniques offer incredible solutions that can be explored in order to find out the optimal tool that can be used for wastewater pH treatment process modelling. In the present paper, seven DL solutions were implemented and tested in order to identify the most appropriate DL method for modelling this type of process, method that ensures the best result. The analysed DL methods are Feedforward Neural Networks (FNNs), Temporal Convolutional Networks (TCNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), General Regression Neural Networks (GRNNs), Time Delay Neural Networks (TDNNs) and Deep Belief Networks (DBNs), being implemented using Python 3.9 software and Tensorflow. The analysis made with the mentioned DL methods, was based on knowing the flowrate of the acid reactant (which was maintained constant), the initial alkaline reactant flowrate in the treated solution, the initial pH level, and the desired pH level, with the final goal of predicting the required quantity of alkaline reactant flowrate necessary to obtain a neutral pH.