Affiliations 

  • 1 School of Engineering, Monash University Malaysia, 47500, Bandar Sunway, Selangor, Malaysia
  • 2 Micro/Nanophysics Research Laboratory, RMIT University, Melbourne, VIC, 3001, Australia
  • 3 School of Engineering, Monash University Malaysia, 47500, Bandar Sunway, Selangor, Malaysia. Electronic address: tan.ming.kwang@monash.edu
Water Res, 2020 Feb 01;169:115187.
PMID: 31671294 DOI: 10.1016/j.watres.2019.115187

Abstract

There is a pressing need for efficient biological treatment systems for the removal of organic compounds in greywater given the rapid increase in household wastewater produced as a consequence of rapid urbanisation. Moreover, proper treatment of greywater allows its reuse that can significantly reduce the demand for freshwater supplies. Herein, we demonstrate the possibility of enhancing the removal efficiency of solid contaminants from greywater using MHz-order surface acoustic waves (SAWs). A key distinction of the use of these high frequency surface acoustic waves, compared to previous work on its lower frequency (kHz order) bulk ultrasound counterpart for wastewater treatment, is the absence of cavitation, which can inflict considerable damage on bacteria, thus limiting the intensity and duration, and hence the efficiency enhancement, associated with the acoustic exposure. In particular, we show that up to fivefold improvement in the removal efficiency can be obtained, primarily due to the ability of the acoustic pressure field in homogenizing and reducing the size of bacterial clusters in the sample, therefore providing a larger surface area that promotes greater bacteria digestion. Alternatively, the SAW exposure allows the reduction in the treatment duration to achieve a given level of removal efficiency, thus facilitating higher treatment rates and hence processing throughput. Given the low-cost of the miniature chipscale platform, these promising results highlight its possibility for portable greywater treatment for domestic use or for large-scale industrial wastewater processing through massive parallelization.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.