Simulation of phenol and chlorophenol removal using combined adsorption and biodegradation: regression analysis and data-mining approach

PANDIAN, P., THEKKUMALAI, M., DAS, A., GOEL, Mukesh, ASTHANA, A. and RAMANAIAH, V. (2022). Simulation of phenol and chlorophenol removal using combined adsorption and biodegradation: regression analysis and data-mining approach. Journal of Hazardous, Toxic, and Radioactive Waste, 26 (3).

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Official URL: https://ascelibrary.org/doi/abs/10.1061/%28ASCE%29...
Link to published version:: https://doi.org/10.1061/(ASCE)HZ.2153-5515.0000701
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    Abstract

    It is widely known that phenols and chlorophenols (CP) are two of the most toxic chemicals and a versatile treatment is imperative to tackle this evil of industrialization. Adsorption using low-cost adsorbents is advantageous and economical; however, it has not turned out to be feasible technology. Biological treatment is much more flexible, useful, and environmentally friendly and a combination of biological treatment and adsorption has yielded much better results compared with using them individually. However, very few works apply statistical methods in elucidating the importance of various options in such a combined study. This work focused on the effect of temperature, initial concentration of chemicals, and adsorbent dosage on the removal of these chemicals. Furthermore, it compares various processes, that is, biological treatment (bio), sequential biological and adsorption (seq), and simultaneous biological and adsorption (sim) methods in treating phenols and chlorophenols. A range of linear regression models was developed to predict the percentage reduction for each of the processes used (bio, sim, and seq), and each of these models was statistically significant as evident from R-square values and the ANOVA table for regression parameters. A data-mining tree-classifier for modeling the phenol and CP removal was also developed. The data mining study indicates the initial concentration of the solvent and temperature to be the primary classifying parameters.

    Item Type: Article
    Uncontrolled Keywords: Environmental Sciences; 0905 Civil Engineering; 0907 Environmental Engineering
    Identification Number: https://doi.org/10.1061/(ASCE)HZ.2153-5515.0000701
    SWORD Depositor: Symplectic Elements
    Depositing User: Symplectic Elements
    Date Deposited: 06 May 2022 10:36
    Last Modified: 06 May 2022 16:37
    URI: http://shura.shu.ac.uk/id/eprint/30198

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