Home A comparative study and combined application of RSM and ANN in adsorptive removal of diuron using biomass ashes
Article
Licensed
Unlicensed Requires Authentication

A comparative study and combined application of RSM and ANN in adsorptive removal of diuron using biomass ashes

  • Sunil K. Deokar , Nachiket A. Gokhale and Sachin A. Mandavgane EMAIL logo
Published/Copyright: July 27, 2021
Become an author with De Gruyter Brill
Submit Manuscript Author Information
International Journal of Chemical Reactor Engineering
From the journal International Journal of Chemical Reactor Engineering Volume 19 Issue 11

Abstract

Biomass ashes like rice husk ash (RHA), bagasse fly ash (BFA), were used for aqueous phase removal of a pesticide, diuron. Response surface methodology (RSM) and artificial neural network (ANN) were successfully applied to estimate and optimize the conditions for the maximum diuron adsorption using biomass ashes. The effect of operational parameters such as initial concentration (10–30 mg/L); contact time (0.93–16.07 h) and adsorbent dosage (20–308 mg) on adsorption were studied using central composite design (CCD) matrix. Same design was also employed to gain a training set for ANN. The maximum diuron removal of 88.95 and 99.78% was obtained at initial concentration of 15 mg/L, time of 12 h, RHA dosage of 250 mg and at initial concentration of 14 mg/L, time of 13 h, BFA dosage of 60 mg respectively. Estimation of coefficient of determination (R 2) and mean errors obtained for ANN and RSM (R 2 RHA = 0.976, R 2 BFA = 0.943) proved ANN (R 2 RHA = 0.997, R 2 BFA = 0.982) fits better. By employing RSM coupled with ANN model, the qualitative and quantitative activity relationship of experimental data was visualized in three dimensional spaces. The current approach will be instrumental in providing quick preliminary estimations in process and product development.

Keywords: adsorption; artificial neural network; bagasse fly ash; response surface methodology; rice husk ash
Corresponding author: Sachin A. Mandavgane, Chemical Engineering Department, Visvesvaraya National Institute of Technology, South Ambazari Road, Nagpur 440010, India, E-mail:

Funding source: Science and Engineering Research Board, India

Award Identifier / Grant number: SB/S3/CE/077/2013

Acknowledgment

We thank the Science and Engineering Research Board (SERB), India, for providing us a research grant (Grant No. SB/S3/CE/077/2013) to undertake this work. Sophisticated characterization facilities provided by IBM, Nagpur, India, and CSMCRI, Bhavnagar, India, are gratefully acknowledged

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: This research is funded by Science and Engineering Research Board (SERB) under Grant No. SB/S3/CE/077/2013.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

References

Azad, F. N., M. Ghaedi, A. Asfaram, A. Jamshidi, G. Hassani, A. Goudarzi, M. H. A. Azqhandi, and A. Ghaedi. 2016. “Optimization of the Process Parameters for the Adsorption of Ternary Dyes by Ni Doped FeO(OH)-NWs-AC Using Response Surface Methodology and an Artificial Neural Network.” RSC Advances 6: 19768–79. https://doi.org/10.1039/c5ra26036a.Search in Google Scholar

Bingöl, D., M. Hercan, S. Elevli, and E. Kiliç. 2012. “Comparison of the Results of Response Surface Methodology and Artificial Neural Network for the Biosorption of Lead Using Black Cumin.” Bioresource Technology 112: 111–5. https://doi.org/10.1016/j.biortech.2012.02.084.Search in Google Scholar PubMed

Deokar, S. K., D. Singh, S. Modak, S. A. Mandavgane, and B. D. Kulkarni. 2016a. “Adsorptive Removal of Diuron on Biomass Ashes: A Comparative Study Using Rice Husk Ash and Bagasse Fly Ash as Adsorbents.” Desalination and Water Treatment 57 (47): 22378–91. https://doi.org/10.1080/19443994.2015.1132394.Search in Google Scholar

Deokar, S. K., S. A. Mandavgane, and B. D. Kulkarni. 2016b. “Comparative Evaluation of Packed-Bed Performance of Biomass Ashes as Adsorbents for Removal of Diuron from Aqueous Solution.” Desalination and Water Treatment 57 (59): 28831–46. https://doi.org/10.1080/19443994.2016.1196391.Search in Google Scholar

Deokar, S. K., P. G. Theng, and S. A. Mandavgane. 2020. “Batch and Packed Bed Techniques for Adsorptive Aqueous Phase Removal of Selected Phenoxyacetic Acid Herbicide Using Sugar Industry Waste Ash.” International Journal of Chemical Reactor Engineering. https://doi.org/10.1515/ijcre-2020-0084.Search in Google Scholar

Dimopoulos, I., J. Chronopoulos, A. C. Sereli, and S. Lek. 1999. “Neural Network Models to Study Relationships between Lead Concentration in Grasses and Permanent Urban Descriptors in Athens City (Greece).” Ecological Modelling 120: 157–65. https://doi.org/10.1016/s0304-3800(99)00099-x.Search in Google Scholar

Garg, U. K., M. P. Kaur, V. K. Garg, and D. Sud. 2008. “Removal of Nickel(II) from Aqueous Solution by Adsorption on Agricultural Waste Biomass Using a Response Surface Methodological Approach.” Bioresource Technology 99: 1325–31. https://doi.org/10.1016/j.biortech.2007.02.011.Search in Google Scholar PubMed

Geyikçi, F., E. Kiliç, S. Çoruh, and S. Elevli. 2012. “Modelling of Lead Adsorption from Industrial Sludge Leachate on Red Mud by Using RSM and ANN.” Chemical Engineering Journal 183: 53–9. https://doi.org/10.1016/j.cej.2011.12.019.Search in Google Scholar

Ghaedi, M., A. Daneshfar, A. Ahmadi, and M. S. Momeni. 2015a. “Artificial Neural Network-Genetic Algorithm Based Optimization for the Adsorption of Phenol Red onto Gold and Titanium Dioxide Nanoparticles Loaded on Activated Carbon.” Journal of Industrial and Engineering Chemistry 21: 587–98. https://doi.org/10.1016/j.jiec.2014.03.024.Search in Google Scholar

Ghaedi, A. M., M. Ghaedi, A. Vafaei, N. Iravani, M. Keshavarz, M. Rad, I. Tyagi, S. Agarwal, and V. K. Gupta. 2015b. “Adsorption of Copper (II) Using Modified Activated Carbon Prepared from Pomegranate Wood: Optimization by Bee Algorithm and Response Surface Methodology.” Journal of Molecular Liquids 206: 195–206. https://doi.org/10.1016/j.molliq.2015.02.029.Search in Google Scholar

Ghaedi, M., S. Hajjati, Z. Mahmudi, I. Tyagi, S. Agarwal, A. Maity, and V. K. Gupta. 2015c. “Modeling of Competitive Ultrasonic Assisted Removal of the Dyes -Methylene Blue and Safranin-O Using Fe3O4 Nanoparticles.” Chemical Engineering Journal 268: 28–37. https://doi.org/10.1016/j.cej.2014.12.090.Search in Google Scholar

Hafizi, A., A. Ahmadpour, M. K. Salooki, M. M. Heravi, and F. F. Bamoharram. 2013. “Comparison of RSM and ANN for the Investigation of Linear Alkylbenzene Synthesis over H14[NaP5W30O110]/SiO2 Catalyst.” Journal of Industrial and Engineering Chemistry 19: 1981–9. https://doi.org/10.1016/j.jiec.2013.03.007.Search in Google Scholar

Harbi, S., F. Guesmi, D. Tabassi, C. Hannachi, and B. Hamrouni. 2016. “Application of Response Surface Methodology and Artificial Neural Network: Modeling and Optimization of Cr (VI) Adsorption Process Using Dowex 1X8 Anion Exchange Resin.” Water Science and Technology 73: 2402–12. https://doi.org/10.2166/wst.2016.091.Search in Google Scholar PubMed

Huovinen, M., J. Loikkanen, J. Naarala, and K. Vähäkangas. 2015. “Toxicity of Diuron in Human Cancer Cells.” Toxicology in Vitro 29: 1577–86. https://doi.org/10.1016/j.tiv.2015.06.013.Search in Google Scholar PubMed

Igwegbe, C. A., L. Mohmmadi, S. Ahmadi, A. Rahdar, D. Khadkhodaiy, R. Dehghani, and S. Rahdar. 2019. “Modeling of Adsorption of Methylene Blue Dye on Ho-CaWO4 Nanoparticles Using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) Techniques.” MethodsX 6: 1779–97. https://doi.org/10.1016/j.mex.2019.07.016.Search in Google Scholar PubMed PubMed Central

Iqbal, M., N. Iqbal, I. A. Bhatti, N. Ahmad, and M. Zahid. 2016. “Response Surface Methodology Application in Optimization of Cadmium Adsorption by Shoe Waste: A Good Option of Waste Mitigation by Waste.” Ecological Engineering 88: 265–75. https://doi.org/10.1016/j.ecoleng.2015.12.041.Search in Google Scholar

Jensen, L. C., J. R. Becerra, J. P. Rivero, M. Escudey, L. Barrientos, and V. C. Castillo. 2013. “Sorption Kinetics of Diuron on Volcanic Ash Derived Soils.” Journal of Hazardous Materials 261: 602–13. https://doi.org/10.1016/j.jhazmat.2013.07.073.Search in Google Scholar PubMed

Karimi, H., and M. Ghaedi. 2014. “Application of Artificial Neural Network and Genetic Algorithm to Modeling and Optimization of Removal of Methylene Blue Using Activated Carbon.” Journal of Industrial and Engineering Chemistry 20: 2471–6. https://doi.org/10.1016/j.jiec.2013.10.028.Search in Google Scholar

Kumar, K. V., K. Porkodi, R. L. A. Rondon, and F. Rocha. 2008. “Neural Network Modeling and Simulation of the Solid/Liquid Activated Carbon Adsorption Process.” Journal of Industrial and Engineering Chemistry 47: 486–90. https://doi.org/10.1021/ie071134p.Search in Google Scholar

Li, W., S. Wei, W. Jiao, and G. Qi. 2016. “Chemical Engineering Research and Design Modelling of Adsorption in Rotating Packed Bed Using Artificial Neural Networks (ANN).” Journal of Industrial and Engineering Chemistry 114: 89–95. https://doi.org/10.1016/j.cherd.2016.08.013.Search in Google Scholar

Liu, Y., Z. Xu, X. Wu, W. Gui, and G. Zhu. 2010. “Adsorption and Desorption Behavior of Herbicide Diuron on Various Chinese Cultivated Soils.” Journal of Hazardous Materials 178: 462–8. https://doi.org/10.1016/j.jhazmat.2010.01.105.Search in Google Scholar PubMed

López, M. E., E. R. Rene, Z. Boger, M. C. Veiga, and C. Kennes. 2015. “Modelling the Removal of Volatile Pollutants under Transient Conditions in a Two-Stage Bioreactor Using Artificial Neural Networks.” Journal of Hazardous Materials 324: 100–9. https://doi.org/10.1016/j.jhazmat.2016.03.018.Search in Google Scholar PubMed

Ohale, P. E., C. F. Uzoh, and O. D. Onukwuli. 2017. “Optimal Factor Evaluation for the Dissolution of Alumina from Azaraegbelu Clay in Acid Solution Using RSM and ANN Comparative Analysis.” South African Journal of Chemical Engineering 24: 43–54. https://doi.org/10.1016/j.sajce.2017.06.003.Search in Google Scholar

Podstawczyk, D., A. W. Krowiak, A. Dawiec, and A. Bhatnagar. 2015. “Biosorption of Copper (II) Ions by Flax meal:Empirical Modeling and Process Optimization by Response Surface Methodology (RSM) and Artificial Neural Network (ANN) Simulation.” Ecological Engineering 83: 364–79. https://doi.org/10.1016/j.ecoleng.2015.07.004.Search in Google Scholar

Ranjan, D., D. Mishra, and S. H. Hasan. 2011. “Bioadsorption of Arsenic: An Artificial Neural Networks and Response Surface Methodological Approach.” Journal of Industrial and Engineering Chemistry 50: 9852–63. https://doi.org/10.1021/ie200612f.Search in Google Scholar

Sangwichien, C., G. L. Aranovich, and M. D. Donohue. 2002. “Density Functional Theory Predictions of Adsorption Isotherms with Hysteresis Loops.” Colloids and Surfaces A: Physicochemical and Engineering Aspects 206: 313–20. https://doi.org/10.1016/s0927-7757(02)00048-1.Search in Google Scholar

Shojaeimehr, T., F. Rahimpour, M. A. Khadivi, and M. Sadeghi. 2014. “A Modeling Study by Response Surface Methodology (RSM) and Artificial Neural Network (ANN) on Cu2+ Adsorption Optimization Using Light Expended Clay Aggregate (LECA).” Journal of Industrial and Engineering Chemistry 20: 870–80. https://doi.org/10.1016/j.jiec.2013.06.017.Search in Google Scholar

Sing, K. S. W. 1998. “Adsorption Methods for the Characterization of Porous Materials.” Advances in Colloid and Interface Science 76–77: 3–11. https://doi.org/10.1016/s0001-8686(98)00038-4.Search in Google Scholar

Wang, J., and X. Guo. 2020a. “Adsorption Isotherm Models: Classification, Physical Meaning, Application and Solving Method.” Chemosphere 258: 127279. https://doi.org/10.1016/j.chemosphere.2020.127279.Search in Google Scholar PubMed

Wang, J., and X. Guoa. 2020b. “Adsorption Kinetic Models: Physical Meanings, Applications, and Solving Methods.” Journal of Hazardous Materials 390: 122156. https://doi.org/10.1016/j.jhazmat.2020.122156.Search in Google Scholar PubMed

Yetilmezsoy, K., and S. Demirel. 2008. “Artificial Neural Network (ANN) Approach for Modeling of Pb(II) Adsorption from Aqueous Solution by Antep Pistachio (Pistacia Vera L.) Shells.” Journal of Hazardous Materials 153: 1288–300. https://doi.org/10.1016/j.jhazmat.2007.09.092.Search in Google Scholar PubMed

Supplementary Material

The online version of this article offers supplementary material (https://doi.org/10.1515/ijcre-2020-0227).

Received: 2020-11-16
Accepted: 2021-07-12
Published Online: 2021-07-27

© 2021 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Review
  3. Book Review: Industrial green chemistry, Editors: Serge Kaliaguine and Jean-Luc Dubois
  4. Articles
  5. Experimental characterization, TDDFT-DFT, and spin effect on [PEG/H2O–ZrO2/TiO2]h hybrid nanofluid 3D flow as potential ceramic industry application
  6. A simulation study of nonideal mixing effect on the dynamic response of an exothermic CSTR with Cholette’s model
  7. CO2 utilization by dry reforming of CH4 over mesoporous Ni/KIT-6 catalyst
  8. CFD-based simulation to reduce greenhouse gas emissions from industrial plants
  9. Beneficiation of phosphate-siliceous slates via acetic acid
  10. Artificial neural network (ANN) approach for prediction and modeling of breakthrough curve analysis of fixed-bed adsorption of iron ions from aqueous solution by activated carbon from Limonia acidissima shell
  11. A comparative study and combined application of RSM and ANN in adsorptive removal of diuron using biomass ashes
  12. Removal of a reactive dye from simulated textile wastewater by environmentally friendly oxidant calcium peroxide
https://doi.org/10.1515/ijcre-2020-0227
Keywords for this article
adsorption; artificial neural network; bagasse fly ash; response surface methodology; rice husk ash

Articles in the same Issue

  1. Frontmatter
  2. Review
  3. Book Review: Industrial green chemistry, Editors: Serge Kaliaguine and Jean-Luc Dubois
  4. Articles
  5. Experimental characterization, TDDFT-DFT, and spin effect on [PEG/H2O–ZrO2/TiO2]h hybrid nanofluid 3D flow as potential ceramic industry application
  6. A simulation study of nonideal mixing effect on the dynamic response of an exothermic CSTR with Cholette’s model
  7. CO2 utilization by dry reforming of CH4 over mesoporous Ni/KIT-6 catalyst
  8. CFD-based simulation to reduce greenhouse gas emissions from industrial plants
  9. Beneficiation of phosphate-siliceous slates via acetic acid
  10. Artificial neural network (ANN) approach for prediction and modeling of breakthrough curve analysis of fixed-bed adsorption of iron ions from aqueous solution by activated carbon from Limonia acidissima shell
  11. A comparative study and combined application of RSM and ANN in adsorptive removal of diuron using biomass ashes
  12. Removal of a reactive dye from simulated textile wastewater by environmentally friendly oxidant calcium peroxide
Sign up now to receive a 20% welcome discount
Institutional Access
How does access work?
Have an idea on how to improve our website?
Please write us.
© 2025 De Gruyter Brill
Downloaded on 10.9.2025 from https://www.degruyterbrill.com/document/doi/10.1515/ijcre-2020-0227/html
Scroll to top button