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Submitted: 09 Nov 2019
Revision: 09 Dec 2019
Accepted: 18 Dec 2019
ePublished: 31 Dec 2019
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Avicenna J Environ Health Eng. 2019;6(2): 92-99.
doi: 10.34172/ajehe.2019.12
  Abstract View: 140
  PDF Download: 8

Research Article

Predicting the Performance of Gorgan Wastewater Treatment Plant Using ANN-GA, CANFIS, and ANN Models

Maryam Bayat Varkeshi 1 ORCID logo, Kazem Godini 2 ORCID logo, Mohamad ParsiMehr 3* ORCID logo, Maryam Vafaee 1

1 Department of Water Engineering, Faculty of Agriculture, Malayer University, Hamedan, Iran.
2 Environmental Health Research Center, Research Institute for Health Development, Kurdistan University of Medical Sciences, Sanandaj, Iran.
3 Department of Environmental Science, Faculty of Natural Resources and Environment, Malayer University, Malayer, Hamedan, Iran.
*Correspondence to Mohammad ParsiMehr, Department of Environmental Science, Faculty of Natural Resources and Environment, Malayer University, Malayer, Hamedan, Iran, Tel: +989182251004, Email: parsimehr.mohamad@gmail.com, mohamad.parsimehr@stu.malayeru.ac.ir

Abstract

A reliable model for any wastewater treatment plant (WWTP) is essential to predict its performance and form a basis for controlling the operation of the process. This would minimize the operation costs and assess the stability of environmental balance. This study applied artificial neural network-genetic algorithm (ANN-GA) and co-active neuro-fuzzy logic inference system (CANFIS) in comparison with ANN for predicting the performance of WWTP. The result indicated that the GA produces more accurate results than fuzzy logic technique. It was found that GA components increased the ANN ability in predicting WWTP performance. The normalized root mean square error (NRMSE) for ANN-GA in predicting chemical oxygen demand (COD), total suspended solids (TSS) and biochemical oxygen demand (BOD) were 0.15, 0.19 and 0.15, respectively. The corresponding correlation coefficients were 0.891, 0.930 and 0.890, respectively. Comparing these results with other studies showed that despite the slightly lower performance of the current model, its requirement for a lower number of input parameters can save the extra cost of sampling.
Keywords: Artificial neural network, Wastewater, Fuzzy logic, Genetic algorithm,
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