Simulation Of Reverse Electrodialysis Power Generation Based On Nanochannels

The concentration gradient difference between salt water and fresh water can be collected and converted into electrical energy.Nanochannel reverse electrodialysis（NRED）is the main technology of concentration gradient difference power generation.This paper aims to optimize the power generation performance of the reverse electrodialysis system.By using finite element simulation,the ion transport mechanism of power generation process at nanoscale was simulated from three perspectives:ion species,nanochannel structure and temperature gradient.It provides reasonable and effective design ideas for the development and application of high-performance nanochannel reverse electrodialysis system.The basic theory of reverse electrodialysis power generation process is introduced in detail,and then the theoretical model for simulating reverse electrodialysis power generation system—PNP model is introduced.The basic structural model of the reverse electrodialysis power generation system is designed,and the boundary conditions and related parameters are determined based on the PNP model.At the same time,the post-processing of the simulation is also carried out,and the main parameters involved in the post-processing process are introduced.Based on four different salt solutions（Na Cl,Li Cl,Ca Cl₂and Mg Cl₂）,the influence of ion diffusion coefficient and valence state on the power generation performance of nanochannel reverse electrodialysis was simulated and analyzed.The results show that the cation diffusion under high concentration ratio The smaller the coefficient is,the larger the difference between anion and cation diffusion flux is,the stronger the power generation performance of the NRED system is,and the opposite is true at low concentration ratios.When the concentration ratio is the same,the divalent cation solution has properties such as low cation diffusion coefficient and high anion concentration gradient,and its special relationship with the open circuit voltage makes the power generation performance stronger at high concentration ratios and weaker at low concentration ratios.Based on the straight channel（cylindrical channel）,an asymmetric nanochannel with a straight-conical combination shape was proposed.Based on this channel,the ion transport mode and the geometric structure of the channel（the ratio of straight and cone channels）were simulated and analyzed.The effect of wall charge distribution on its power generation performance.When ions diffuse from the straight channel to the conical channel,the electric double layer（EDL）overlap in the nanochannel is higher,and its overall power generation performance is stronger.When discussing the channel geometry,it is found that the shorter the length of the straight channel and the longer the length of the conical channel,the better the power generation performance.The influence of wall charge distribution is discussed,and it is found that the power generation performance of NRED depends on the wall charge in different parts of the channel under different transport modes,but as the concentration ratio decreases,the power generation performance of the system will be affected by both the straight channel and the conical channel.The effect of temperature gradient in the reverse electrodialysis power generation system is systematically simulated and studied.It was found that the negative temperature gradient produced by the high temperature at the low concentration end can promote the ion mobility,while the positive temperature gradient produced by the high temperature end at the high concentration end can inhibit the ion migration.The power generation performance can be improved by increasing the temperature at the low concentration end,that is,increasing the negative temperature gradient.If the special relationship between KCl solution and temperature is considered,when the concentration ratio reaches a certain value,the maximum power and maximum power efficiency both decrease with the increase of negative temperature gradient.