Volume 18, Issue 2 (7-2022)
J Health Syst Res 2022, 18(2): 83-94 |
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Mousavikia N, Mohammadi F, Hasheminejad H. Prediction and Optimization of Ultrasound-Assisted Removal of Estrogen Hormones from Municipal Wastewater Using Artificial Neural Network and Genetic Algorithm: A Review Approach. J Health Syst Res 2022; 18 (2) :83-94
URL: http://hsr.mui.ac.ir/article-1-1312-en.html
URL: http://hsr.mui.ac.ir/article-1-1312-en.html
1- MSc Student, Department of Civil Engineering, Isfahan University of Technology, Isfahan, Iran
2- Assistant Professor, Department of Environmental Health Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran
3- Assistant Professor, Department of Civil Engineering, Isfahan University of Technology, Isfahan, Iran
2- Assistant Professor, Department of Environmental Health Engineering, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran
3- Assistant Professor, Department of Civil Engineering, Isfahan University of Technology, Isfahan, Iran
Abstract: (1745 Views)
Background: Estrogens are one of the micropollutants in the wastewater which have detrimental effects on water living organisms. There are many reports documenting the adverse effects of estrogen hormones, such as feminization of fish, in the environment. One of the major sources of these compounds is municipal wastewater effluents. The biological processes at municipal wastewater treatment plants cannot completely remove these compounds. Therefore, a method for the treatment of hormones is needed. The ultrasonic method is an effective process for elimination of micropollutants. This study aimed to model and optimize the removal of two hormones [estrone (E1) and 17 beta-estradiol (E2)] from the wastewater by ultrasound method using artificial neural network (ANN) with genetic algorithm (GA) approach.
Methods: A literature review was performed from years 2000 to 2021 and the results of related studies were applied for modeling. A two-layer Feed-Forward Back-Propagation Neural Network (FFBPNN) model was designed. Various training algorithms were evaluated and the Levenberg Marquardt (LM) algorithm was selected as the best one.
Findings: Existence of 12 neurons in the hidden layer led to the highest correlation coefficient (r) and the lowest mean squared error (MSE (and mean absolute error (MAE). The results of the GA determined the optimum performance conditions. Therefore, increasing in pH and power density increased the efficiency of removing hormones from the wastewater.
Conclusion: Finally, a sensitivity analysis was performed using ANN-GA and Spearman correlation, and the results were completely compatible.
Methods: A literature review was performed from years 2000 to 2021 and the results of related studies were applied for modeling. A two-layer Feed-Forward Back-Propagation Neural Network (FFBPNN) model was designed. Various training algorithms were evaluated and the Levenberg Marquardt (LM) algorithm was selected as the best one.
Findings: Existence of 12 neurons in the hidden layer led to the highest correlation coefficient (r) and the lowest mean squared error (MSE (and mean absolute error (MAE). The results of the GA determined the optimum performance conditions. Therefore, increasing in pH and power density increased the efficiency of removing hormones from the wastewater.
Conclusion: Finally, a sensitivity analysis was performed using ANN-GA and Spearman correlation, and the results were completely compatible.
Type of Study: Applicable |
Subject:
Environmental Health Engineering
Received: 2021/08/28 | Accepted: 2022/04/9 | Published: 2022/07/6
Received: 2021/08/28 | Accepted: 2022/04/9 | Published: 2022/07/6
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