Advanced filtration in greywater treatment: a modelling approach with water reuse perspectives

ABSTRACT

This study presents a data-driven system analysis approach intended to improve greywater reuse at the household/community level using a case study of ¨Granular Activated Carbon-Membrane Integrated-Multigrade Effluent¨(GAC-MI-ME) treatment system. It consists of a matrix of advanced filtration units and provides multi-grade water reuse options. Using experimental data with varying greywater strengths, artificial neural networks were applied to develop effluent prediction models for water quality parameters including turbidity, total dissolved solids, five-day biochemical oxidation demand, oxidation redox potential and pH. The results show that the GAC-MI-ME can improve water quality with terminal removal efficiency of 99% and above for turbidity and BOD. At the same time, the model simulations can effectively predict effluent quality, which can be applied as decision-making tools in selecting and the treatment trains of GAC-MI-ME. The concept may help broaden the greywater reuse by ensuring it meets the requirements of diverse water reuse regulations.

KEYWORDS:

• Greywater
• water quality
• artificial neural network
• water reuse
• advanced filtration

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported in part by the Qatar National Research Fund (QNRF) and by the USDA National Institute of Food and Agriculture, Hatch project 1024709.