Optimization of Bi2O3/TS-1 preparation and photocatalytic reaction conditions for low concentration Erythromycin wastewater treatment based on artificial neural network

At present, it is a challenge to degrade antibiotics of low concentrations in wastewater environment, highly effective and eco-friendly photocatalytic processes were considered promising technologies for this degradation. In this work, Bi2O3-loaded titanium silicalite-1 molecular sieve (Bi2O3/TS-1) composites were prepared and used to degrade low-concentration erythromycin (ERM) in wastewater. The content of active components and the dose of photocatalyst are key operating parameters with major effects on photo catalytic efficiency. The optimal parameters (Bi content and photocatalyst dose) were determined through artificial neural network simulating the relationship between key operating parameters and removal efficiency (RE). The maximum RE (98.02%) was measured under optimal operating parameters (Bi % = 5.5%, catalyst dosage = 0.6 g/L). The effects of water quality parameters (such as pH and ERM concentration) were also studied under optimal experimental conditions. Artificial intelligence was used in this work to achieve optimal control of catalyst preparation and photocatalytic reaction conditions. This study provides a useful strategy for the preparation of nanocatalysts and the practical application of high-efficiency photocatalytic reactions. (C) 2021 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

Automated summary

In this study, Bi2O3-loaded titanium silicalite-1 molecular sieve (Bi2O3/TS-1) composites were prepared and used for degrading low-concentration erythromycin (ERM) in wastewater. The optimal operating parameters were determined through artificial neural network simulation, achieving a maximum removal efficiency of 98.02% under optimal conditions. Water quality parameters were also studied under optimal experimental conditions.