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Tetracycline hydrochloride (TC) is an antibiotic frequently detected in water sources globally. Developing effective strategies for the removal of this pharmaceutical contaminant from water and wastewater is crucial. The primary objective of this study is to develop computational models to accurately predict and optimize the TC removal process. Response Surface Methodology (RSM) was used to model the relationship between the removal efficiency (RE) and three key variables: initial TC concentration (TC 0 ), catalyst dosage, and peroxymonosulfate (PMS) dose over treatment time. Finally, Response surface methodology optimization was used to discover the best approach for removing Tetracycline hydrochloride. The findings demonstrated the great accuracy of response surface methodology and artificial neural networks in predicting the RE behavior of TC in an aqueous solution. The artificial neural network’s output MSE, MAE, and R 2 accuracy metrics are 2.02E−02, 7.38E−03, and 9.99E−01, respectively. To solve the worldwide problem of water pollution, the findings of this work might be used to create optimal states and strategies for removing and degrading pharmaceutical contaminants in water using computational artificial intelligence.
Kriaa et al. (Mon,) studied this question.