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Sono Electro-Chemical Synthesis of Lafeo3nanoparticles for the Removal of Fluoride: Optimization and Modeling Using Rsm, Ann and Ga Tools Publisher



Ahmadi S1 ; Mesbah M2 ; Igwegbe CA3 ; Ezeliora CD4 ; Osagie C5 ; Khan NA6 ; Dotto GL7 ; Salari M8 ; Dehghani MH9, 10
Authors
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Authors Affiliations
  1. 1. Department of Environmental Health, Zabol University of Medical Sciences, Zabol, Iran
  2. 2. Young Researchers and Elite Club, Science and Research Branch, Islamic Azad University, Tehran, Iran
  3. 3. Department of Chemical Engineering, Nnamdi Azikiwe University, Awka, Nigeria
  4. 4. Department of Industrial and Production Engineering, Nnamdi Azikiwe University, Awka, Nigeria
  5. 5. Environmental and Natural Sciences, Brandenburg University of Technology, Cottbus-Senftenberg, Germany
  6. 6. Department of Civil Engineering, Jamia Millia Islamia Central University, New Delhi, 110025, India
  7. 7. Chemical Engineering Department, Federal University of Santa Maria-UFSM, Santa Maria, RS, Brazil
  8. 8. Department of Environmental Health Engineering, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran
  9. 9. Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
  10. 10. Institute for Environmental Research, Center for Solid Waste Research, Tehran University of Medical Sciences, Tehran, Iran

Source: Journal of Environmental Chemical Engineering Published:2021


Abstract

The aim of this work was to model, optimize, and compare fluoride removal by LaFeO3-NPs using the RSM (Response Surface Methodology), ANN (Artificial Neural Network), and GA (Genetic Algorithm) techniques.The input variables considered were pH, time, temperature, LaFeO3-NPs dose, and fluoride. The CCD (central composite design) plan was exercised for the analysis of RSM, and ANN to determine their capabilities of prediction of the response. Their performances were evaluated using the regression coefficient (R2), RMSE, SEP, and the AAD. Also, RSM and GA were used to maximize the response and their optimum conditions evaluated. Both RSM (R2 = 0.9970, AAD = 0.00001, RMSE = 0.0037, SEP = 0.0042) and ANN (R2 = 0.9919, AAD = 0.00044, RMSE = 0.0066, SEP = 0.0074) gave high degree of accuracy. The model equation obtained for the process through RSM was adequate. The GA and RSM gave very close values for the optimization of the fluoride reduction process; RSM gave optimum fluoride removal of 96.35% (at pH 8.6, time = 75.03 min, temperature = 34.9 °C, dose = 0.225 g, and concentration = 23.68 mg L-1) while the GA gave 96.30% (at pH 10, time = 120.39 min, temperature = 28.41 °C, dose = 1.030 g, and concentration = 16.31 mg L-1). But from the confirmation experiments, RSM and GA data gave 96.52% and 96.63%, respectively. RSM, ANN, and GA were capable of modeling and optimizing the elimination of fluoride using LaFeO3-NPs. © 2021 Elsevier Ltd.
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