Intelligent System for the Predictive Analysis of an Industrial Wastewater Treatment Process

Abstract

Considering the exponential growth of today’s industry and the wastewater results of its processes, it needs to have an optimal treatment system for such effluent waters to mitigate the environmental impact generated by its discharges and comply with the environmental regulatory standards that are progressively increasing their demand. This leads to the need to innovate in the control and management information systems of the systems responsible to treat these residual waters in search of improvement. This paper proposes the development of an intelligent system that uses the data from the process and makes a prediction of its behavior to provide support in decision making related to the operation of the wastewater treatment plant (WWTP). To carry out the development of this system, a multilayer perceptron neural network with 2 hidden layers and 22 neurons each is implemented, together with process variable analysis, time-series decomposition, correlation and autocorrelation techniques; it is possible to predict the chemical oxygen demand (COD) at the input of the bioreactor with a one-day window and a mean absolute percentage error (MAPE) of 10.8%, which places this work between the adequate ranges proposed in the literature.

Adsorptive removal of dairy industrial pollutants by chitosan zinc –oxide nanoadsorbent- A comparitive study of artificial neural network and quadratic Box-Behnken design

“…The performance of the network in training phase increased with increasing the number of neurons. At the same time, the performance of the network in testing data phase lead to optimum value at an optimal number of hidden neurons (Arismendy et al, 2020). In the present study, 4-layer feed forward back propagation neural network (4-3-1) was designed by changing the four process variables of batch adsorption experiments.…”

A Prescriptive Intelligent System for an Industrial Wastewater Treatment Process: Analyzing pH as a First Approach

“…The first reason lies in taking advantage of the conclusions generated by N predictive models to analyze how the characterized variables will impact the future of the process. According to the literature review, these models are usually advanced computing techniques because of their accuracy [30]. As shown in the Related Works section, some advanced computing techniques are ANNs, Bayesian belief networks, ARIMA models, and self-organizing deep belief networks.…”

Machine Learning Approach to Predict Quality Parameters for Bacterial Consortium-Treated Hospital Wastewater and Phytotoxicity Assessment on Radish, Cauliflower, Hot Pepper, Rice and Wheat Crops

“…Hence, the phytotoxicity assessment on crop plants was carried out further to see the impact of treatment efficiency as predicted by our RF classifier. In light of the standard methods for wastewater quality analysis, five wastewater quality predicting parameters have been used in machine learning, i.e., BOD [14], COD [41,[54][55][56], TSS [41,52,56], TDS [41] and turbidity [57,58]. The present RF classification successfully used artificial intelligence to predict twelve quality parameters between wastewater that was treated with the bacterial consortium versus untreated wastewater.…”