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International Heat Transfer Conference 15

ISSN: 2377-424X (online)
ISSN: 2377-4371 (flashdrive)

Effect of Module Inclination Angle on Air Gap Membrane Distillation

David Warsinger
MIT

Jaichander Swaminathan
Rohsenow Kendall Heat Transfer Laboratory, Department of Mechanical Engineering Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge MA 02139-4307, USA

John H. Lienhard V
Rohsenow Kendall Heat and Mass Transfer Laboratory, Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA

DOI: 10.1615/IHTC15.mtr.009351
pages 5165-5178


KEY WORDS: Condensation, Mass transfer and drying, Membrane Distillation, AGMD, Desalination, tilt angle, thermal efficiency

Abstract

Air gap membrane distillation (AGMD) experiments were performed with varied temperature and varied module inclination angles to characterize the effect of module angle on permeate production and thermal performance. While AGMD is potentially one of the most energy efficient membrane distillation configurations, transport resistances in the air gap typically dominate the thermal performance, resulting in degraded permeate production. Tilting the module away from vertical offers the opportunity to manipulate the condensate layer and its associated thermal resistance. In this study, we report experiments on varying module tilt angle performed with a flat plate AGMD module under fully characterized heat and mass transfer conditions. Numerical modeling is also performed to better understand the experimental results. The tests indicated that the AGMD system behaves as a “permeate gap”, or flooded membrane distillation system for declined and extremely inclined positions. A key finding relevant to all AGMD systems is that at highly negative tilt angles (more than 30 degrees), condensate may fall onto the membrane causing thermal bridging and increased permeate production. Near vertical and positive tilt angles (<15 degrees from vertical) show no significant effect of module tilt on performance, in line with model predictions.

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