Unveiling the role of artificial intelligence in tetracycline antibiotics removal using UV/sulfite/phenol advanced reduction process.

UV/sulfite-based advanced reduction processes (ARP) have attracted increasing attention due to their high capability for removing a wide range of pollutants. Therefore, developing UV/sulfite ARP systems with assisted Artificial Intelligence (AI) models is considered an efficient strategy for sustainable pollutant removal. The present study delves into modeling and optimizing photodegradation of tetracycline (TC) antibiotics under UV/sulfite/рhenol reԁuсtion рroсess (UV/SPAP) using integrаteԁ Artifiсiаl Neurаl Networks (ANN), Suррort Veсtor Regression (SVR), аnԁ Genetiс Algorithm (GA).

Cefixime removal via WO/Co-ZIF nanocomposite using machine learning methods.

In this research, an upgraded and environmentally friendly process involving WO/Co-ZIF nanocomposite was used for the removal of Cefixime from the aqueous solutions. Intelligent decision-making was employed using various models including Support Vector Regression (SVR), Genetic Algorithm (GA), Artificial Neural Network (ANN), Simulation Optimization Language for Visualized Excel Results (SOLVER), and Response Surface Methodology (RSM). SVR, ANN, and RSM models were used for modeling and predicting results, while GA and SOLVER models were employed to achieve the optimal conditions for Cefixime degradation.