Modeling of power generation with thermolytic reverse electrodialysis for low-grade waste heat recovery
Abstract Significant attention has been paid to closed-loop reverse electrodialysis (RED) systems using a thermolytic solution for low-grade waste heat energy recovery. They have several cost benefits when compared with open-loop RED with seawater and river water, such as no need of repetitive pretreatment and removal of locational constraints. This study presents the model of RED using ammonium bicarbonate (NH 4 HCO 3 ), one of the promising solutes for the closed-loop RED, whose ionization has not been clarified. Because of the unclarified electrochemical information of NH 4 HCO 3 electrolyte, the Planck-Henderson equation was used to approximate the membrane potential based on conductivity measurements, and the solution resistance was experimentally computed. Furthermore, the experimentally obtained permselectivity of the membrane was applied for a more precise estimate of the membrane potential. We found that the developed NH 4 HCO 3 -RED model was in good agreement with the experimental results under various operating conditions. We also characterized the net power density, which considers the pumping loss, by using our model. In our system, the maximum net power density of 0.84W/m 2 was obtained with an intermembrane distance of 0.1mm, a flow rate of 3mL/min, and a concentration ratio of 200 (2M/0.01M) as optimum conditions. We expect that this study will improve our understanding of the NH 4 HCO 3 -RED system and contribute to relevant modeling studies, using NH 4 HCO 3 or some other compounds, for generating higher energy densities. Highlights This work is the first attempt to develop an NH 4 HCO 3 -RED model for power generation. The activity and molar conductivity can be described with electrical conductivity. Actual permselectivity plays a critical role to estimate the RED performance. The model fits well to the experimental results with parametric variations. The optimal operating conditions can be achieved by the validated model.
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