ISSN print edition: 0366-6352 ISSN electronic edition: 1336-9075 Registr. No.: MK SR 9/7 Published monthly

Optimization strategies for norfloxacin photocatalytic degradation using response surface methodology and artificial neural network: a review

Thangapandi Chellapandi, S. Sudharsan, and Muthukani Elamathi

Centre for Applied Nanomaterials, Chennai Institute of Technology, Chennai, India

E-mail: t.chellapandi1994@gmail.com

Received: 2 April 2025  Accepted: 9 June 2025

Abstract:

Photocatalytic degradation has emerged as a sustainable as well as effective method for the remediation of water tainted with organic pollutants, including pharmaceuticals, dyes, and industrial waste. This process makes use of semiconductor photocatalysts, which generate reactive species under light irradiation to facilitate the degradation of complex pollutants into environmentally benign by-products. However, the efficiency of photocatalysis is determined by a set of interdependent factors, such as catalyst characteristics such as surface area and band gap energy, pollutant properties such as concentration and chemical structure, and environmental conditions such as pH, light intensity, and presence of competing substances. Optimizing these parameters helps in improving the efficiency of degradation and ensuring scalability. Traditional single-factor optimization methods often fail in capturing of synergistic and antagonistic interactions between the variables, which limits its practical applicability. To overcome this difficulty, Response Surface Methodology (RSM) and Artificial Neural Network (ANN) have been utilized as a robust statistical and neural networking tool in optimizing photocatalytic processes. RSM helps to integrate mathematical modeling with experimental design to make efficient exploration of multi-variable interactions possible, along with the identification of optimal degradation conditions and a quantitative assessment of factor significance. This review synthesizes recent developments in photocatalytic degradation studies using RSM and ANN that have focused on different semiconductor photocatalysts, discussing their performances while degrading norfloxacin and the important optimization strategies, analytical techniques, used in the evaluation of degradation efficiency.

Keywords: Artificial neural networks; Norfloxacin; Response surface methodology; Statistical analysis; Photocatalytic degradation

Full paper is available at www.springerlink.com.

DOI: 10.1007/s11696-025-04187-1

Chemical Papers 79 (9) 5609–5624 (2025)