Analysis of direct contact membrane distillation based on a lumped-parameter dynamic predictive model
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Biological and Environmental Sciences and Engineering (BESE) Division
Water Desalination and Reuse Research Center (WDRC)
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AbstractMembrane distillation (MD) is an emerging technology that has a great potential for sustainable water desalination. In order to pave the way for successful commercialization of MD-based water desalination techniques, adequate and accurate dynamical models of the process are essential. This paper presents the predictive capabilities of a lumped-parameter dynamic model for direct contact membrane distillation (DCMD) and discusses the results under wide range of steady-state and dynamic conditions. Unlike previous studies, the proposed model captures the time response of the spacial temperature distribution along the flow direction. It also directly solves for the local temperatures at the membrane interfaces, which allows to accurately model and calculate local flux values along with other intrinsic variables of great influence on the process, like the temperature polarization coefficient (TPC). The proposed model is based on energy and mass conservation principles and analogy between thermal and electrical systems. Experimental data was collected to validated the steady-state and dynamic responses of the model. The obtained results shows great agreement with the experimental data. The paper discusses the results of several simulations under various conditions to optimize the DCMD process efficiency and analyze its response. This demonstrates some potential applications of the proposed model to carry out scale up and design studies. © 2016
CitationKaram AM, Alsaadi AS, Ghaffour N, Laleg-Kirati TM (2017) Analysis of direct contact membrane distillation based on a lumped-parameter dynamic predictive model. Desalination 402: 50–61. Available: http://dx.doi.org/10.1016/j.desal.2016.09.002.
SponsorsResearch reported in this publication was supported by King Abdullah University of Science and Technology (KAUST).