(729h) Development of a Mathematical Model for Microbial Desalination Cells

Conventional waste water treatment processes, especially the aeration process, typical require a lot of energy input. In particular, wastewater treatment consumes 2% of global power capacity at an annual cost of $40 billion worldwide. Microbial fuel cells (MFCs) can convert the organic compounds in the waste water to electrons and protons by microorganisms growing on the anode surface. The electrons can be used by external electrical circuit as power supply, while the protons move to the cathode through the ion exchange membrane and interact with oxygen to produce water on the cathode surface. Since MFCs can produce power from the organic compounds in waste water, MFCs may provide a sustainable way for water treatment. Microbial desalination cells (MDCs) derive from MFCs by adding a third compartment between the anode and the cathode, separated by anion or cation exchange membranes for a new function of desalination. Given complex desalination processes and strong interactions between biological, electrochemical, and engineering factors in MDCs, a proper mathematic model will be essential for the optimization and the scaling up of MDCs. While several mathematical models exist for MFCs or microbial electrolysis cells (MECs) [1-8], no model has been developed for MDCs. Based on an existing MFC model [4], a mathematic MDC model was developed in this work to quantify MDC desalination performance under dynamic loadings (for both substrate and saline water) and different external resistance.

The developed MDC model considers the desalination process driven by both the electric potential between an anode and a cathode electrodes and the salt diffusion process due to a concentration gradient across the ion exchange membranes. The Nernst-Monod equations were used to quantify substrate consumption and bacterial growth. The model was calibrated using experimental data obtained from a lab-scale MDC upon the change of substrate flowrates, and validated by the data from the experimental conditions with different substrate concentrations, salt concentrations, and external electrical resistance. The validated model was then used to predict the performance of the MDC affected by either single or multiple operating parameters. Optimal operation parameters such as influent acetate feed concentration and flow-rate, influent salt feed concentration and flow-rate, external electrical resistance, and the ratio of the volumes of the anode and the salt compartments, were determined from the simulations. To the best of our knowledge, this is the first mathematic model for MDCs.

 

References:

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