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Support vector regression-based model for phenol adsorption in rotating packed bed adsorber.

Rameez Ahmad Aftab1, Sadaf Zaidi2, Mohd Danish1

  • 1Department of Chemical Engineering, Zakir Hussain College of Engineering and Technology, Aligarh Muslim University, Aligarh, Uttar Pradesh, 202002, India.

Environmental Science and Pollution Research International
|June 25, 2021
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Summary

This study predicts phenol adsorption in rotating packed beds (RPB) using support vector regression (SVR) and artificial neural networks (ANN). These soft computing models accurately forecast phenol removal from industrial wastewater, outperforming multiple regression.

Keywords:
Artificial neural networkMultiple regressionPhenol adsorptionResponsible editor: Tito Roberto Cadaval JrRotating packed bedSupport vector regression

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Area of Science:

  • Environmental Engineering
  • Chemical Engineering
  • Computational Chemistry

Background:

  • Phenol in industrial wastewater poses significant environmental risks.
  • Rotating packed bed (RPB) technology offers efficient phenol adsorption by enhancing mass transfer.
  • Accurate prediction of adsorption is crucial for optimizing wastewater treatment processes.

Purpose of the Study:

  • To apply and compare soft computing models, specifically Support Vector Regression (SVR) and Artificial Neural Network (ANN), for predicting phenol adsorption in an RPB.
  • To evaluate the predictive performance of SVR and ANN against traditional Multiple Regression (MR) models.
  • To assess the generalization capability of the developed models for phenol adsorption prediction.

Main Methods:

  • Phenol adsorption data from an RPB system was collected.
  • Independent parameters including spray density, gravity factor, concentration, and contact time were utilized.
  • Support Vector Regression (SVR), Artificial Neural Network (ANN), and Multiple Regression (MR) models were constructed using randomized and normalized data.
  • Model performance was evaluated using Coefficient of Determination (R²) and Root Mean Square Error (RMSE).

Main Results:

  • SVR and ANN models demonstrated high predictive accuracy, with R² values of 0.996 and 0.998, respectively.
  • ANN models slightly outperformed SVR models, showing a lower RMSE of 0.027 compared to SVR's 0.045.
  • Multiple Regression (MR) models showed lower predictive performance with R² of 0.934 and RMSE of 0.149.
  • Both SVR and ANN models exhibited excellent generalization capabilities.

Conclusions:

  • Soft computing models, particularly SVR and ANN, are highly effective for predicting phenol adsorption in RPB systems.
  • These models offer a robust and accurate approach to optimize phenol removal from industrial wastewater.
  • The findings highlight the potential of computational methods in advancing environmental remediation technologies.