Abstract Global estimates of electricity generation from coastal salinity gradient energy resources rely on the underlying assumption that these gradients are spatially and temporally stable. Refining these estimates requires a better understanding of coastal variations in water properties and their impact on power production. This study investigated power output in reverse electrodialysis (RED) cells by coupling seawater samples collected from three different sites along coastal North Carolina at five different sampling dates between 2016 and 2017 with wastewater effluent from a wastewater treatment facility as the dilute solution. We found that power density did not vary substantially across the sampling dates except for one notable drop in power for a sample collected during an approaching hurricane. For all sites, power output peaked during the summer season. Using our experimental results, we developed a semi-empirical predictive model of RED power output as a function of temperature and conductivity. The model was able to predict power density within approximately 20% of the experimental power densities for the seawater samples used in this study and others in the literature. Combining our modeling approach with temporal conductivity and temperature data may help identify promising sites for coastal salinity gradient energy installations.

中文翻译：

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使用北卡罗来纳州沿海水域通过反向电渗析发电的时间变化及其与温度和电导率的相关性

摘要 沿海盐度梯度能源发电的全球估计依赖于这些梯度在空间和时间上是稳定的基本假设。完善这些估计需要更好地了解沿海水特性的变化及其对电力生产的影响。本研究通过将从 2016 年至 2017 年之间五个不同采样日期从北卡罗来纳州沿海三个不同地点收集的海水样本与作为稀释溶液的废水处理设施的废水相耦合来研究反向电渗析 (RED) 电池的功率输出。我们发现，除了在飓风逼近期间收集的样本的功率显着下降之外，功率密度在采样日期之间没有显着变化。对于所有站点，电力输出在夏季达到峰值。使用我们的实验结果，我们开发了 RED 功率输出作为温度和电导率函数的半经验预测模型。对于本研究中使用的海水样本和文献中的其他样本，该模型能够在大约 20% 的实验功率密度范围内预测功率密度。将我们的建模方法与时间电导率和温度数据相结合，可能有助于确定沿海盐度梯度能量装置的有希望的地点。对于本研究中使用的海水样本和文献中的其他样本，该模型能够在大约 20% 的实验功率密度范围内预测功率密度。将我们的建模方法与时间电导率和温度数据相结合，可能有助于确定沿海盐度梯度能量装置的有希望的地点。对于本研究中使用的海水样本和文献中的其他样本，该模型能够在大约 20% 的实验功率密度范围内预测功率密度。将我们的建模方法与时间电导率和温度数据相结合，可能有助于确定沿海盐度梯度能量装置的有希望的地点。