Predictive modelling of tetracycline removal by I-Bi/Bi2WO6/MWCNTs photocatalyst using RSM and ANN-PSO hybrid machine learning approach.

A visible-light responsive heterostructure photocatalyst (I-Bi/Bi₂WO₆/MWCNTs) was successfully synthesized using an ethylene glycol-assisted hydrothermal method for the photodegradation of the tetracycline (TC) antibiotic. To analyze and streamline the process performance, a hybrid machine learning model that incorporated artificial neural networks (ANN) with particle swarm optimization (PSO) and response surface methodology (RSM) was applied to forecast and optimize key operating parameters, such as solution pH, initial TC concentration, contact time, and photocatalyst dosage. The 4-8-1 topology of ANN-PSO was found to be the optimal network, and the prediction model of TC removal was demonstrated as a matrix of explicit equations. The R² of randomized training (0.98), testing (0.99), and validation (0.96) at the optimized topology confirms the efficiency of the developed ANN-PSO model. The ANN-PSO model showed a better predictive performance in comparison with the RSM model (R² = 0.960, RMSE = 5.379), with the correlation coefficient (0.981) being higher and the RMSE (4.712) lower. A 96.8% TC removal was achieved at solution pH 8, initial TC concentration of 20 mg/L, contact time of 160 min, and photocatalyst dosage of 25 mg.