Isfahan University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share this content! On (X network) By
Prediction the Performance of Full Scale Wastewater Treatment Plant With A-B Process Using Artificial Neural Network and Genetic Algorithm Publisher



Mohammadi F1 ; Yavari Z2 ; Mohammadi F1 ; Rahimi S4
Authors
Show Affiliations
Authors Affiliations
  1. 1. Department of Environmental Health Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran
  2. 2. Department of Environmental Health Engineering, School of Abarkouh Paramedical, Genetic and Environmental Adventures Research Center, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
  3. 3. Department of Textile Engineering, Isfahan University of Technology, Isfahan, Iran
  4. 4. Department of Environmental Health, Firoozabad Branch, Islamic Azad University, Firoozabad, Iran

Source: International Journal of Environmental Health Engineering Published:2022


Abstract

Aim: A reliable model for wastewater treatment plants (WWTPs) is essential to provide a tool for predicting their performance and to form a basis for controlling the operation of the process also; this would minimize the operation costs. In recent years, computer-based methods have been applied to many areas of environmental issues. Artificial neural networks (ANNs) and genetic algorithm (GA) techniques could be applied for modeling WWTP processes, owing to their high accuracy, adequacy, and quite promising applications in engineering. Materials and Methods: This study applied multilayer feed forward back propagation neural network and GA to predict and optimize the performance of the second phase of the Isfahan North WWTP. Experimental results, which demonstrated the performance of the plant over 6 years were applied for modeling. Results: A three-layer neural network was developed as a predictive model and the network was trained with Levenberg–Marquardt algorithm. The chemical oxygen demand (COD), biochemical oxygen demand (BOD), total suspended solids (TSS), total kjeldahl nitrogen (TKN), and total phosphorus (TP) were introduced as the model input and output. Neural network performance was evaluated with correlation coefficient ® and least mean square error. Proposed model demonstrated the high consistency of the results of modeling and experiments. GA achieved the best value of input parameters as 324.36, 457.37, 359.11, 60.09, and 14.15 mg/l for BOD, COD, TSS, TKN, TP, respectively. Conclusion: ANN and GA combination provides powerful analysis tool for modeling and optimization of nonlinear relationships between the parameters in WWTPs and could be used for proper design and operation of the WWTPs. © 2022 International Journal of Environmental Health Engineering.
Related Docs
View other Related Docs
1. Modelling and Optimizing Pyrene Removal From the Soil by Phytoremediation Using Response Surface Methodology, Artificial Neural Networks, and Genetic Algorithm, Chemosphere (2019)
2. Modeling and Sensitivity Analysis of the Alkylphenols Removal Via Moving Bed Biofilm Reactor Using Artificial Neural Networks: Comparison of Levenberg Marquardt and Particle Swarm Optimization Training Algorithms, Biochemical Engineering Journal (2020)
3. Modeling and Optimization Approach for Phytoremediation of Formaldehyde From Polluted Indoor Air by Nephrolepis Obliterata Plant, Environmental Science and Pollution Research (2023)
4. Prediction and Optimization of Steroid Hormone Removal Parameters From Municipal Wastewater by Ultrasound Probe Using Artificial Neural Network and Genetic Algorithm: A Review, Desalination and Water Treatment (2022)
5. Artificial Intelligence Modeling to Predict Transmembrane Pressure in Anaerobic Membrane Bioreactor-Sequencing Batch Reactor During Biohydrogen Production, Journal of Environmental Management (2021)
6. Machine Learning Model Optimization for Removal of Steroid Hormones From Wastewater, Chemosphere (2023)
7. Biosorption of 4-Chlorophenol by Dried Anaerobic Digested Sludge: Artificial Neural Network Modeling, Equilibrium Isotherm, and Kinetic Study, International Journal of Environmental Science and Technology (2017)
8. Artificial Neural Network and Logistic Regression Modelling to Characterize Covid-19 Infected Patients in Local Areas of Iran, Biomedical Journal (2021)
Experts (# of related papers)
View all Related Experts
Farzaneh Mohammadi (15)
Hamid Reza Pourzamani (3)
Other Related Docs
9. Improvement of the Rhizoremediation Efficiency of Pahs Contaminated Soil Under Cysteine Treatment Along With Modeling, Environmental Nanotechnology, Monitoring and Management (2021)
10. Optimization of Wastewater Treatment Plant by Activated Sludge Method Using Gps-X Software, Journal of Health System Research (2023)
11. Assessment of Alkylphenols Migration From Packaging to Fruit Juices: Influential Factors and Health Risks, Sustainable Chemistry and Pharmacy (2023)
12. Prediction and Optimization of Ultrasound-Assisted Removal of Estrogen Hormones From Municipal Wastewater Using Artificial Neural Network and Genetic Algorithm: A Review Approach, Journal of Health System Research (2022)
13. Investigation of Photocatalytic Activity of Synthesized Zinc Stannate for Tetracycline Antibiotic Degradation: Modelling and Optimization Through Rsm, Ann and Genetic Algorithm, Desalination and Water Treatment (2019)
14. The Lasting Effects of Wastewater Irrigation: Evaluating Alkylphenols Accumulation in Soil and Potential Health Risks for Farmers and Local Communities, Results in Engineering (2024)
15. Determination of Design Parameters of Urban Wastewater Treatment Plants in the Cold Regions of Iran, International Journal of Environmental Health Engineering (2012)
16. Biological 2,4,6-Trinitrotoluene Removal by Extended Aeration Activated Sludge: Optimization Using Artificial Neural Network, Scientific Reports (2023)
17. Sensitivity Analysis With the Monte Carlo Method and Prediction of Atenolol Removal Using Modified Multiwalled Carbon Nanotubes Based on the Response Surface Method: Isotherm and Kinetics Studies, International Journal of Chemical Engineering (2022)
18. Modelling the Phytoremediation of Formaldehyde From Indoor Air by Chamaedorea Elegans Using Artificial Intelligence, Genetic Algorithm and Response Surface Methodology, Journal of Environmental Chemical Engineering (2020)
19. Preparation of Agar Nanospheres: Comparison of Response Surface and Artificial Neural Network Modeling by a Genetic Algorithm Approach, Carbohydrate Polymers (2015)
20. Artificial Neural Network and Genetic Algorithm for Modeling and Optimization of Photocatalytic Removal of Aquatic Dye by G-C3 N4 /N-Tio2 Nanoparticles, Desalination and Water Treatment (2020)