永磁沙袋增强太阳能蒸馏器人工神经网络模型的实验评价与开发

Rishika Chauhan, Pankaj Dumka, Dhananjay R. Mishra
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引用次数: 4

摘要

由于全球人口的快速增长和无计划的工业化,饮用水的供应日益减少。虽然人类无法想象在没有水的情况下生存,但全球领导人仍然无法在现实中履行他们的协议。太阳能蒸馏器是从受污染的水中提取饮用水的主要方法之一。本文报道了用填沙棉袋和铁氧体环形永磁体增强的单盆太阳能蒸馏器的实验评价和开发的人工神经网络模型。均方根误差(RMSE)、效率系数(E)、模型性能综合指数(OI)和剩余质量系数(CRM)值与所提出的人工神经网络模型吻合较好。所提出的人工神经网络模型可用于预测所报告的改进蒸馏器的馏出物产量,其变化幅度为5%。CSS、mss -1和mss - 2的总体相关系数分别为0.98171、0.9867和0.99542。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Experimental Evaluation and Development of Artificial Neural Network Model for the Solar Stills Augmented with the Permanent Magnet and Sandbag
The availability of potable water is reducing day by day due to rapid growth in the human population and un-planned industrialization around the globe. Although human beings cannot think of survival in the absence of water, the global leadership can still not implement their pacts in reality. Solar still is one of the prominent ways of getting potable water from contaminated water. This manuscript reports the experimental evaluation and developed ANN model for the single basin solar stills having augmentations with the sand-filled cotton bags and ferrite ring permanent magnets. Root mean square error (RMSE), efficiency coefficient (E), the overall index of model performance (OI), and coefficient of residual mass (CRM) values are in good agreement with the proposed developed model of ANN. The proposed ANN model can be utilized to predict distillate yield with a variation of 5% for the reported modified stills. Overall correlation coefficient of CSS, MSS-1&2 are 0.98171, 0.9867, and 0.99542, respectively.
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