Photocatalytic degradation of ofloxacin antibiotic wastewater using TS-1/C3N4 composite photocatalyst: Reaction performance optimisation and estimation of wastewater component synergistic effect by artificial neural network and genetic algorithm

Abstract

Antibiotics are used worldwide and their residues have been detected in multiple aquatic environments, which threaten both the human health and ecological environment. An efficient process is necessary for the thorough degradation of antibiotic contaminants in wastewater. Photocatalytic degradation of organic pollutants has the advantages of environmental friendliness, safety and thoroughness. In this study, titanium silicon molecular sieve-loaded carbon nitride (TS-1/C3N4) composite photocatalysts were synthesised and used for the photocatalytic degradation of ofloxacin (OFX) wastewater. An artificial neural network (ANN) was proposed based on the experimental data, and combined with genetic algorithm (GA) to optimise the experimental parameters for the favourable reaction performance. The maximum removal efficiency (RE) at 82.92% was measured under the optimal experimental parameters (1.55 g/L catalyst, 58.60 % TS-1 loading and 49.38 mW/cm2 luminous power density). Moreover, the influence of wastewater constituents on the RE was investigated both experimentally and through modelling via the ANN model. The experimental results revealed that the adsorption of OFX on the photocatalyst and the RE were reduced in the presence of wastewater constituents, probably due to their competitive adsorption and the subsequent reaction with the reactive species and light-shielding effect. The model gave an absolute relative deviation (ARD %) for the synergistic effect of cations, metal ions and anions at 6.88 %, 1.04 % and 1.77 %, respectively, thus showing a good ability to predict the synergistic interplay of wastewater components. This work may provide important insights and solutions for the photocatalytic treatment of antibiotic wastewater.