Abstract
Biomass ashes are used for adsorption of herbicides from aqueous solution. A relationship between physicochemical properties of biomass ashes such as carbon–hydrogen–nitrogen content (CHN analysis), silica content and BET surface area with their adsorption capacity was established and modeled using artificial neural network. 2,4-Dichlorophenoxyacetic acid (2,4-D) a commonly used herbicide is chosen a representative for this study. The artificial neural network model was trained, validated and tested using 35 data sets and was equipped with nine neuron hidden layers having tansig (tangent sigmoid) transfer function and an output layer with purelin (purely linear) transfer function. This model can be used to predict 2,4-D removal efficacy of any biomass ash by knowing its physicochemical properties like C, H, N, Si and BET surface area.
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Acknowledgements
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.
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Gokhale, N.A., Trivedi, N.S., Mandavgane, S.A. et al. Biomass ashes as potent adsorbent for pesticide: prediction of adsorption capacity by artificial neural network. Int. J. Environ. Sci. Technol. 17, 3209–3216 (2020). https://doi.org/10.1007/s13762-020-02645-9
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DOI: https://doi.org/10.1007/s13762-020-02645-9