Predicting the Performance of Gorgan Wastewater Treatment Plant Using ANN-GA, CANFIS, and ANN Models
Publish Year: 1398
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_AJEHE-6-2_004
تاریخ نمایه سازی: 30 مرداد 1401
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 ۰.۱۵, ۰.۱۹ and ۰.۱۵, respectively. The corresponding correlation coefficients were ۰.۸۹۱, ۰.۹۳۰ and ۰.۸۹۰, 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.
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Authors
Maryam Bayat Varkeshi
Department of Water Engineering, Faculty of Agriculture, Malayer University, Hamedan, Iran
Kazem Godini
Environmental Health Research Center, Research Institute for Health Development, Kurdistan University of Medical Sciences, Sanandaj, Iran
Mohammad ParsiMehr
Department of Environmental Science, Faculty of Natural Resources and Environment, Malayer University, Malayer, Hamedan, Iran
Maryam Vafaee
Department of Water Engineering, Faculty of Agriculture, Malayer University, Hamedan, Iran