Click on “Download PDF” for the PDF version or on the title for the HTML version.

If you are not an ASABE member or if your employer has not arranged for access to the full-text, Click here for options.

Greywater Treatment System Modeling: An approach Using Simulated Greywater

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  Paper number  131620367,  2013 Kansas City, Missouri, July 21 - July 24, 2013. (doi: @2013
Authors:   Shashi Kant, Fouad H Jaber, Raghupathy Karthikeyan
Keywords:   Greywater Water Quality Water Treatment Water Reuse Artificial Neural Network.

Abstract: Greywater reuse is an innovative approach towards environmental sustainability in urban areas. Several system-design approaches have been developed to treat greywater in specific conditions. However, the physical, chemical, and biological characteristic of greywater varies with the source and the location. The uncertainty in the water quality poses challenges for the selection of the appropriate system. This research focuses on the development of a decision-support tool for design and selection of a portable greywater treatment system using synthetic-greywater. The laboratory-simulated greywater in the experiment represents the overall characteristics greywater from very high strength to low strength in terms of water quality parameters. The prioritized parameters of interest in the experiment includes; BOD, N-NO3, P-PO4, Turbidity, TDS (Total Dissolved Solids), DO (Dissolved Oxygen), and TC (Total Coliform). A decentralized water treatment system has been developed which includes four serial filtrations from 50 to 1 micron followed by hollow-fiber membrane (0.02 micron), GAC (Granular Activated Carbon), UV radiation and eventually RO (Reverse Osmosis) system. Water quality parameters were evaluated at each treatment stage by using multiple run of varying strength synthetic-greywater through treatment system. The empirical results were used as training-dataset for creating ANN (Artificial Neural Network) based prediction models. Design and preliminary results will be presented in this paper.

(Download PDF)    (Export to EndNotes)