Combined advanced oxidation dye-wastewater treatment plant: design and development with data-driven predictive performance modeling

• Published in: npj clean water 2024-03, Vol.7 (1), p.15-17, Article 15
• Main Authors: Chauhan, Pankaj Singh, Singh, Kirtiman, Choudhary, Aditya, Brighu, Urmila, Singh, S. K., Bhattacharya, Shantanu
• Format: Article
• Language: English
• Subjects: 639/166/986; 639/166/987; 639/925/357; Aeration; Aquatic Pollution; Artificial neural networks; Dyes; Earth and Environmental Science; Environment; Environmental regulations; Industrial effluents; Industrial wastewater; Inland waters; Machine learning; Mathematical models; Modelling; Nanotechnology; Neural networks; Oxidation; Oxidation process; Parameters; Performance prediction; Prediction models; Regression analysis; Robustness (mathematics); Tertiary treatment; Textile industry wastes; Textile industry wastewaters; Waste Water Technology; Wastewater treatment; Wastewater treatment plants; Water discharge; Water Industry/Water Technologies; Water Management; Water Pollution Control; Water quality; Water Quality/Water Pollution; Zinc oxide
• ISSN: 2059-7037; 2059-7037
• DOI: 10.1038/s41545-024-00308-7

The recalcitrant nature of the industrial dyes poses a significant challenge to existing treatment technologies due to the stringent environmental regulations. This combined with the inefficiency of a single treatment method has led to the implementation of the combination of primary, secondary, and tertiary treatment processes, which fails during complex secondary aeration processes due to variable pH loads of industrial effluent wastewater. This article presents a modified design methodology of a pilot-scale micro-pre-treatment unit using a solar-triggered advanced oxidation process reactor that both effectively controls the influent variability at the source and mitigates textile effluents for making the discharge reusable for different industrial purposes. The proposed modified combination technique of controlled serial processes inclusive of primary, secondary, and tertiary treatment steps with ZnO/ZnO-GO NanoMat-based advanced oxidation process demonstrates complete remediation of industrial grade effluent with effective reuse of the discharge. Further, a reliable prediction model for estimating water quality parameter using machine learning models are proposed. Multi-linear regression and Artificial Neural network modeling provide simple, accurate, and robust prediction capabilities, which are evaluated for the efficiency of the processes. The generated prediction models capture the output parameters within an acceptable level of accuracy ( R a d j 2 > 0.90 ) and allow compliance with the discharge Inland Water Discharge Standards (IWDS).