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

• Published in: Reaction kinetics, mechanisms and catalysis mechanisms and catalysis, 2023-02, Vol.136 (1), p.549-565
• Main Authors: Sethi, Sheetal, Dhir, Amit, Arora, Vinay
• Format: Article
• Language: English
• Subjects: Catalysis; Chemistry; Chemistry and Materials Science; Industrial Chemistry/Chemical Engineering; Physical Chemistry
• ISSN: 1878-5190; 1878-5204
• DOI: 10.1007/s11144-023-02360-9

This research work aims to assess the efficacy of the lab synthesized catalyst Ni 2 P–TiO 2 (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.