مجله آب و فاضلاب

مجله آب و فاضلاب

Predicting Effluent Quality Parameters Using Ensemble Models, Artificial Neural Networks and Naked Mole-Rat Algorithm

نوع مقاله : مقاله پژوهشی

نویسندگان
الهام قنبری عدیوی 1
علی رئیسی 2
1 دانشیار، گروه مهندسی آب، دانشکده کشاورزی، دانشگاه شهرکرد، شهرکرد، ایران
2 استادیار، گروه مهندسی آب، دانشکده کشاورزی، دانشگاه شهرکرد، شهرکرد، ایران
10.22093/wwj.2024.454419.3414
چکیده
A reliable simulation of wastewater effluent parameters is important for reducing the operational costs of a wastewater treatment plant. In this study, optimized artificial neural networks and inclusive multiple models are used to predict the effluent biochemical oxygen demand, chemical oxygen demand and total suspended solids of a WWTPs in Shahrekord basin, Iran. The influent quality parameters (CODinf, BODinf, TSSinf and PHinf) are used as inputs to the models. The naked mole-rat algorithm is used to tune ANN parameters. This investigation compares the capabilities of ANN-NMRA with those of ANN-firefly, ANN-sine cosine algorithm, ANN-genetic algorithm and ANN models. The output of hybrid and standalone models is incorporated into an ANN model as an IMM model. Several individual models are used in an IMM model for predicting outputs. Hence, an IMM model increases the accuracy of individual models. In this study, ANN models are modified by using a goodness factor to reduce computational time. This paper presents a new preprocessing method for selecting the best input combination and analyzes the uncertainty of model and input parameters. IMM, ANN-NMRA, ANN-SCA, ANN-FFA, ANN-GA and ANN models achieve MAEs of 0.789, 0.998, 1.19, 1.26, 1.34 and 1.40 mg/L for predicting BODeff in the testing stage, respectively. The IMM model has the highest accuracy for predicting CODeff and TSSeff. A good factor reduces the computational time of ANN models by removing redundant hidden neurons. The uncertainty analysis results show that model parameters provide higher uncertainty than input parameters.
کلیدواژه‌ها
Effluent Quality Parameters
Artificial Neural Network Models
Uncertainty
Optimization Algorithms
Wastewater Treatment Plant

عنوان مقاله English

Predicting Effluent Quality Parameters Using Ensemble Models, Artificial Neural Networks and Naked Mole-Rat Algorithm

نویسندگان English

Elham Ghanbari Adivi 1
Ali Raeisi 2
1 Assoc. Prof., Dept. of Water Engineering, Faculty of Agriculture, Shahrekord University, Iran
2 Assist. Prof., Dept. of Water Engineering, Faculty of Agriculture, Shahrekord University, Iran
چکیده English

A reliable simulation of wastewater effluent parameters is important for reducing the operational costs of a wastewater treatment plant. In this study, optimized artificial neural networks and inclusive multiple models are used to predict the effluent biochemical oxygen demand, chemical oxygen demand and total suspended solids of a WWTPs in Shahrekord basin, Iran. The influent quality parameters (CODinf, BODinf, TSSinf and PHinf) are used as inputs to the models. The naked mole-rat algorithm is used to tune ANN parameters. This investigation compares the capabilities of ANN-NMRA with those of ANN-firefly, ANN-sine cosine algorithm, ANN-genetic algorithm and ANN models. The output of hybrid and standalone models is incorporated into an ANN model as an IMM model. Several individual models are used in an IMM model for predicting outputs. Hence, an IMM model increases the accuracy of individual models. In this study, ANN models are modified by using a goodness factor to reduce computational time. This paper presents a new preprocessing method for selecting the best input combination and analyzes the uncertainty of model and input parameters. IMM, ANN-NMRA, ANN-SCA, ANN-FFA, ANN-GA and ANN models achieve MAEs of 0.789, 0.998, 1.19, 1.26, 1.34 and 1.40 mg/L for predicting BODeff in the testing stage, respectively. The IMM model has the highest accuracy for predicting CODeff and TSSeff. A good factor reduces the computational time of ANN models by removing redundant hidden neurons. The uncertainty analysis results show that model parameters provide higher uncertainty than input parameters.

کلیدواژه‌ها English

Effluent Quality Parameters
Artificial Neural Network Models
Uncertainty
Optimization Algorithms
Wastewater Treatment Plant
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مجله آب و فاضلاب
دوره 34، شماره 6 - شماره پیاپی 149
بهمن و اسفند 1402
صفحه 55-73