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Computational Modeling and Empirical Analysis of a Biomass-Powered Drinking Water Pasteurization Technology

Author

Listed:
  • Grace Burleson

    (School of Mechanical, Industrial, and Manufacturing Engineering, Oregon State University, Corvallis, OR 97331, USA)

  • Daniel Caplan

    (School of Mechanical, Industrial, and Manufacturing Engineering, Oregon State University, Corvallis, OR 97331, USA)

  • Catherine Mays

    (School of Chemical, Biological, and Environmental Engineering, Oregon State University, Corvallis, OR 97331, USA)

  • Nicholas Moses

    (School of Mechanical, Industrial, and Manufacturing Engineering, Oregon State University, Corvallis, OR 97331, USA)

  • Tala Navab-Daneshmand

    (School of Chemical, Biological, and Environmental Engineering, Oregon State University, Corvallis, OR 97331, USA)

  • Kendra Sharp

    (School of Mechanical, Industrial, and Manufacturing Engineering, Oregon State University, Corvallis, OR 97331, USA)

  • Nordica MacCarty

    (School of Mechanical, Industrial, and Manufacturing Engineering, Oregon State University, Corvallis, OR 97331, USA)

Abstract

While filtration, chlorination, and UV drinking water treatments are commonplace, globally an estimated 1.2 billion people continue to boil their drinking water over inefficient biomass fires instead because it allows them to use available resources paired with a time-tested and trusted method. Although boiling water is culturally well-established, there is vast potential to improve human health, environmental impact, and efficiency by leveraging the fact that a significant reduction in pathogenic microorganisms occurs at temperatures well below boiling through a process known as pasteurization. This paper presents the evaluation of a community-scale, biomass-powered, flow-through water pasteurization system that was designed to heat water to the temperature required for pasteurization to occur before recuperating heat while cooling treated water down to a safe-to-handle temperature. The system is then compared to other common thermal treatment methods including batch-boiling over open fires and improved cookstoves. Results from computational modeling and empirical analysis show that the water pasteurizer significantly increases the overall water treatment capacity (from 7.9 to 411 L/h, adjusted for one hour of treatment via household boiling and operation of the water pasteurizer at steady-state, respectively) and uses far less biomass fuel (from 22 to 5.5 g/L, adjusted for treatment of 1 L of water via household boiling and operation of the water pasteurizer at steady-state, respectively). Notable comparisons to the batch-boiling of water over institutional-sized traditional and improved cookstoves are also demonstrated. Further, the results of fecal indicator reduction through the system (8 log and 6 log reduction of E. coli and bacteriophage MS2, respectively) suggest compliance with US-EPA (6 log and 4 log reduction of E. coli and bacteriophage MS2, respectively) and WHO requirements (effluent concentrations below the detection limit, specified as <1 E. coli CFU/100 mL and <10 bacteriophage MS2 PFU/mL) for the reduction in and effluent concentration of E. coli and bacteriophage for water treatment processes. It is recommended that engineers continue to explore the use of heat transfer and microorganism reduction theory to design technologies that increase the capacity and efficiency for thermal water purification that uses locally-available biomass resources.

Suggested Citation

  • Grace Burleson & Daniel Caplan & Catherine Mays & Nicholas Moses & Tala Navab-Daneshmand & Kendra Sharp & Nordica MacCarty, 2020. "Computational Modeling and Empirical Analysis of a Biomass-Powered Drinking Water Pasteurization Technology," Energies, MDPI, vol. 13(4), pages 1-24, February.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:4:p:936-:d:322754
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    References listed on IDEAS

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    1. Thirugnanasambandam, Mirunalini & Iniyan, S. & Goic, Ranko, 2010. "A review of solar thermal technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(1), pages 312-322, January.
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