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Intervention of artificial intelligence to predict the degradation and mineralization of amoxicillin through photocatalytic route using nickel phosphide-titanium dioxide catalyst

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Abstract

This research work aims to assess the efficacy of the lab synthesized catalyst Ni2P–TiO2 (NPT) using Artificial neural network (ANN) for the degradation of Amoxicillin (AMX) in aqueous suspension under UV irradiation. The experiments were conducted at 50 ppm antibiotic concentration, using three different compositions of the synthesized catalyst (1:9, 3:7, 5:5) for 5 h. Of the various catalysts tested, the optimum pH conditions, dose, and time were attained i.e., natural pH, 0.25 g/L, 2 h. The degradation and mineralization emerged highest with the respective percentages of 83.00 and 70.00%. ANN was applied with the Swish activation Function to predict amoxicillin degradation. Chemical oxygen demand (COD) removal was considered the key parameter for determining amoxicillin degradation using a three-layer backpropagation neural network. The results obtained through the ANN were similar to the experimental results, and their correlation coefficient was 0.96. The findings show that all the input variables such as pH, catalyst dose, and irradiation time have an immense effect on the degradation efficiency. The study demonstrates that Neural Network modeling can successfully predict and simulate the degradation process.

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Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

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

    Sheetal Sethi & Amit Dhir

  2. Computer Science & Engineering Department, Thapar Institute of Engineering and Technology, Patiala, India

    Vinay Arora

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  1. Sheetal Sethi
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  2. Amit Dhir
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  3. Vinay Arora
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All authors contributed to the study's conception and design, where SS performed the material preparation, experimentation, analysis, and draft preparation. All authors (AD and VA) commented on the previous version of the manuscript. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Amit Dhir.

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Sethi, S., Dhir, A. & Arora, V. Intervention of artificial intelligence to predict the degradation and mineralization of amoxicillin through photocatalytic route using nickel phosphide-titanium dioxide catalyst. Reac Kinet Mech Cat 136, 549–565 (2023). https://doi.org/10.1007/s11144-023-02360-9

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