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Optimizing leachate treatment with titanium oxide-impregnated activated carbon (TiO2@ASC) in a fixed-bed column: characterization, modeling, and prediction study

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Abstract

This research focused on the application of a fixed bed column filled with immobilized titanium oxide-loaded almond shell carbon (TiO2@ASC) for the treatment of leachate. The adsorption performance of synthesized TiO2@ASC in fixed bed column is analyzed using adsorption experiments and modeling study. The characteristics of synthesized materials are determined by several instrumental techniques like BET, XRD, FTIR, and FESEM-EDX. The flow rate, initial concentration of COD and NH3-N, and bed height were optimized to determine the effectiveness of leachate treatment. The linear bed depth service time (BDST) plots equations with a correlation coefficient of greater than 0.98 confirmed the model’s accuracy for COD and NH3-N adsorption in column structure. The adsorption process was found to be well predicted by an artificial neural network (ANN) model with a root mean square error of 0.0172 and 0.0167 for COD and NH3-N reduction, respectively. The immobilized adsorbent was regenerated using HCl and was found to be reusable for up to three cycles, promoting material sustainability. This study is aimed to contribute towards SDG 6 and SDG11 by United Nations Sustainable Development Goals.

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Acknowledgements

The authors would like to express their gratitude to TEQIP-III GJUS&T, Hisar for providing financial assistance with letter number TEQIP-III/2018/1841-1142 to conduct this research. Gaurav is thankful to financial support provided by The Institution of Engineers (India) for carrying out Research & Development work via IEI R&D Grant-in-Aid Scheme (R.5/2/PG/2022-23/RDPG2022003). He is also thankful to the Thapar Institute of Engineering and Technology for providing seed funding (TIET/CS/ER/22-23/220014) and other support.

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Authors and Affiliations

  1. Department of Civil Engineering, MM Engineering College, Maharishi Markandeshwar (Deemed to Be University), Mullana-Ambala, Haryana, India, 133207

    Kulbir Singh

  2. Department of Environmental Science & Engineering, Guru Jambheshwar University of Science and Technology, Hisar, 25001, Haryana, India

    Kulbir Singh & Rajesh Kumar Lohchab

  3. Laboratory of Saharan Natural Resources, Faculty of Sciences and Technology, University of Ahmed Draia Adrar, Adrar, Algeria

    Hakim Aguedal

  4. Higher School of Saharan Agriculture, Adrar, Algeria

    Hakim Aguedal

  5. School of Energy and Environment, Thapar Institute of Engineering and Technology, Patiala, 147004, India

    Gaurav Goel

  6. School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, MK430AL, UK

    Gaurav Goel

  7. Department of Environmental Sciences, J. C. Bose University of Science and Technology, YMCA, Faridabad, 121006, Haryana, India

    Navish Kataria

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  5. Navish Kataria
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Kulbir Singh, Rajesh Kumar Lohchab, Hakim Aguedal, Gaurav Goel, and Navish Kataria. The first draft of the manuscript was written Kulbir Singh, Rajesh Kumar Lohchab, Hakim Aguedal, Gaurav Goel, and Navish Kataria, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Rajesh Kumar Lohchab.

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Highlights

• TiO2@ASC composites were synthesized for leachate treatment in a fixed bed column.

• The fixed bed column parameters including flow rate, concentration, and bed height were optimized.

• Adsorption capacity for COD and NH3-N was found 37.12 mg/g and 5.02 mg/g, respectively.

• BDST and ANN study were carried out to optimize the adsorption performance.

• Regeneration performance of fixed bed column was carried out unto three cycles.

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Singh, K., Lohchab, R.K., Aguedal, H. et al. Optimizing leachate treatment with titanium oxide-impregnated activated carbon (TiO2@ASC) in a fixed-bed column: characterization, modeling, and prediction study. Environ Sci Pollut Res 30, 88450–88462 (2023). https://doi.org/10.1007/s11356-023-28647-x

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