Enhancing the Prediction of Influent Total Nitrogen in Wastewater Treatment Plant Using Adaptive Neuro-Fuzzy Inference System–Gradient-Based Optimization Algorithm

For the accurate estimation of daily influent total nitrogen of sewage plants, a novel hybrid approach is proposed in this study, where a gradient-based optimization (GBO) algorithm is employed to adjust the hyper-parameters of an adaptive neuro-fuzzy system (ANFIS). Several benchmark methods for op...

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Veröffentlicht in:Water (Basel) Jg. 16; H. 21; S. 3038
Hauptverfasser: Ikram, Misbah, Liu, Hongbo, Al-Janabi, Ahmed Mohammed Sami, Kisi, Ozgur, Mo, Wang, Ali, Muhammad, Adnan, Rana Muhammad
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.11.2024
Schlagworte:
Algorithms
Artificial intelligence
Chemical oxygen demand
Efficiency
Effluents
Energy consumption
Environment
Environmental aspects
Environmental impact
Fuzzy algorithms
Fuzzy logic
Fuzzy systems
Generalized linear models
Genetic algorithms
Neural networks
Nitrogen
Optimization algorithms
Pollutants
Public health
Purification
Sewage
Stream flow
Support vector machines
Water quality
Water treatment
Water treatment plants
China
ISSN:2073-4441, 2073-4441
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Abstract For the accurate estimation of daily influent total nitrogen of sewage plants, a novel hybrid approach is proposed in this study, where a gradient-based optimization (GBO) algorithm is employed to adjust the hyper-parameters of an adaptive neuro-fuzzy system (ANFIS). Several benchmark methods for optimizing ANFIS parameters are compared, which include particle swarm optimization (PSO), gray wolf optimization (GWO), and gradient-based optimization (GBO). The prediction accuracy of the ANFIS-GBO model is evaluated against other models using four statistical measures: root-mean-squared error (RMSE), mean absolute error (MAE), and Nash–Sutcliffe efficiency (NSE), and coefficient of determination (R2). Test results show that the suggested ANFIS-GBO outperforms the standalone ANFIS, hybrid ANFIS-PSO and ANFIS-GWO methods in daily influent total nitrogen prediction from the sewage treatment plant. The ANFIS, ANFIS-PSO, ANFIS-GWO, and ANFIS-GBO models are evaluated using seven distinct input combinations to predict daily TNinf. The results from both the testing and training periods demonstrate that these models, namely ANFIS, ANFIS-PSO, ANFIS-GWO, and ANFIS-GBO, exhibit the highest level of accuracy for the seventh input combination (Qw, pH, SS, TP, NH3-N, COD, and BOD5). ANFS-GBO-7 reduced the RMSE in the prediction of ANFIS-7, ANFIS-PSO-7, and ANFIS-GWO-7 by 21.77, 10.73, and 6.81%, respectively, in the test stage. Results from testing and training further demonstrate that increasing the number of parameters (NH3-N, COD, and BOD) as input improves the models’ ability to make predictions. The outcomes show that the ANFIS-GBO model can potentially be suggested for the daily prediction of influent total nitrogen (TNinf) in full-scale wastewater treatment plants.
AbstractList For the accurate estimation of daily influent total nitrogen of sewage plants, a novel hybrid approach is proposed in this study, where a gradient-based optimization (GBO) algorithm is employed to adjust the hyper-parameters of an adaptive neuro-fuzzy system (ANFIS). Several benchmark methods for optimizing ANFIS parameters are compared, which include particle swarm optimization (PSO), gray wolf optimization (GWO), and gradient-based optimization (GBO). The prediction accuracy of the ANFIS-GBO model is evaluated against other models using four statistical measures: root-mean-squared error (RMSE), mean absolute error (MAE), and Nash–Sutcliffe efficiency (NSE), and coefficient of determination (R2). Test results show that the suggested ANFIS-GBO outperforms the standalone ANFIS, hybrid ANFIS-PSO and ANFIS-GWO methods in daily influent total nitrogen prediction from the sewage treatment plant. The ANFIS, ANFIS-PSO, ANFIS-GWO, and ANFIS-GBO models are evaluated using seven distinct input combinations to predict daily TNinf. The results from both the testing and training periods demonstrate that these models, namely ANFIS, ANFIS-PSO, ANFIS-GWO, and ANFIS-GBO, exhibit the highest level of accuracy for the seventh input combination (Qw, pH, SS, TP, NH3-N, COD, and BOD5). ANFS-GBO-7 reduced the RMSE in the prediction of ANFIS-7, ANFIS-PSO-7, and ANFIS-GWO-7 by 21.77, 10.73, and 6.81%, respectively, in the test stage. Results from testing and training further demonstrate that increasing the number of parameters (NH3-N, COD, and BOD) as input improves the models’ ability to make predictions. The outcomes show that the ANFIS-GBO model can potentially be suggested for the daily prediction of influent total nitrogen (TNinf) in full-scale wastewater treatment plants.
For the accurate estimation of daily influent total nitrogen of sewage plants, a novel hybrid approach is proposed in this study, where a gradient-based optimization (GBO) algorithm is employed to adjust the hyper-parameters of an adaptive neuro-fuzzy system (ANFIS). Several benchmark methods for optimizing ANFIS parameters are compared, which include particle swarm optimization (PSO), gray wolf optimization (GWO), and gradient-based optimization (GBO). The prediction accuracy of the ANFIS-GBO model is evaluated against other models using four statistical measures: root-mean-squared error (RMSE), mean absolute error (MAE), and Nash–Sutcliffe efficiency (NSE), and coefficient of determination (R[sup.2] ). Test results show that the suggested ANFIS-GBO outperforms the standalone ANFIS, hybrid ANFIS-PSO and ANFIS-GWO methods in daily influent total nitrogen prediction from the sewage treatment plant. The ANFIS, ANFIS-PSO, ANFIS-GWO, and ANFIS-GBO models are evaluated using seven distinct input combinations to predict daily TN[sub.inf] . The results from both the testing and training periods demonstrate that these models, namely ANFIS, ANFIS-PSO, ANFIS-GWO, and ANFIS-GBO, exhibit the highest level of accuracy for the seventh input combination (Q[sub.w] , pH, SS, TP, NH[sub.3] -N, COD, and BOD5). ANFS-GBO-7 reduced the RMSE in the prediction of ANFIS-7, ANFIS-PSO-7, and ANFIS-GWO-7 by 21.77, 10.73, and 6.81%, respectively, in the test stage. Results from testing and training further demonstrate that increasing the number of parameters (NH[sub.3] -N, COD, and BOD) as input improves the models’ ability to make predictions. The outcomes show that the ANFIS-GBO model can potentially be suggested for the daily prediction of influent total nitrogen (TN[sub.inf] ) in full-scale wastewater treatment plants.
Audience Academic
Author Al-Janabi, Ahmed Mohammed Sami
Ali, Muhammad
Adnan, Rana Muhammad
Mo, Wang
Kisi, Ozgur
Ikram, Misbah
Liu, Hongbo
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Snippet For the accurate estimation of daily influent total nitrogen of sewage plants, a novel hybrid approach is proposed in this study, where a gradient-based...
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SubjectTerms Algorithms
Artificial intelligence
Chemical oxygen demand
Efficiency
Effluents
Energy consumption
Environment
Environmental aspects
Environmental impact
Fuzzy algorithms
Fuzzy logic
Fuzzy systems
Generalized linear models
Genetic algorithms
Neural networks
Nitrogen
Optimization algorithms
Pollutants
Public health
Purification
Sewage
Stream flow
Support vector machines
Water quality
Water treatment
Water treatment plants
Title Enhancing the Prediction of Influent Total Nitrogen in Wastewater Treatment Plant Using Adaptive Neuro-Fuzzy Inference System–Gradient-Based Optimization Algorithm
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Volume 16
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