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Application of Response Surface Methodology and Artificial Neural Network Modeling to Assess Non-Thermal Plasma Efficiency in Simultaneous Removal of Btex From Waste Gases: Effect of Operating Parameters and Prediction Performance Publisher



Hosseinzadeh A1 ; Najafpoor AA2, 3 ; Jafari AJ4 ; Jazani RK5 ; Baziar M6 ; Bargozin H7 ; Piranloo FG8
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Authors Affiliations
  1. 1. Student Research Committee, Department of Environmental Health Engineering, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran
  2. 2. Social Determinants of Health Research Center, Department of Environmental Health Engineering, School of Health, Mashhad University of Medical Sciences, Mashhad, Iran
  3. 3. Global Center for Environmental Remediation, University of Newcastle, Callagan, 2308, NSW, Australia
  4. 4. Department of Environmental Health Engineering, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
  5. 5. Department of Ergonomics and Industrial Safety, School of Health, Safety and Environment, Shahid Beheshti University of Medical Sciences, Tehran, Iran
  6. 6. Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
  7. 7. Department of Chemical Engineering, University of Zanjan, Zanjan, Iran
  8. 8. Biospher Technology Company, Environmental Laboratory, Abhar, Iran

Source: Process Safety and Environmental Protection Published:2018


Abstract

This study aimed to assess the prediction efficiencies of response surface methodology (RSM) and artificial neural network (ANN)-based models in terms of benzene, toluene, ethylbenzene, and xylenes (BTEX) removal from a polluted airstream using non-thermal plasma (NTP). The effect that key elements of the NTP process, including temperature, BTEX concentration, voltage and flow rate, had on the BTEX elimination efficiency was investigated using a central composite RSM design along with three ANN models including Feed-Forward Back Propagation Neural Network (FFBPNN), Cascade-Forward Back Propagation Neural Network (CFBPNN) and Elman-Forward Back Propagation Neural Network (EFBPNN) with the topology of 4-h-1. The RSM and ANN models were statistically compared using some indicators including Sum of Squared Errors (SSE), adjusted R2, determination coefficient (R2), Root Mean Squared Error (RMSE), Absolute Average Deviation (AAD). According to the RSM output, voltage was the most efficient variable with a coefficient proportion of 8.28. Besides, FFBPNN was the best model among the considered ANN models. Also, the R2 achieved for ANN (FFBPNN) and RSM models were 0.9736 and 0.9656 correspondingly. Therefore, it was concluded that the ANN (FFBPNN) represents a powerful tool for modeling the BTEX removal. © 2018 Institution of Chemical Engineers
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