Water distillation tower: Experimental investigation, economic assessment, and performance prediction using optimized machine-learning model
Water distillation tower: Experimental investigation, economic assessment, and performance prediction using optimized machine-learning model
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DOI：
10.1016/j.jclepro.2023.135896
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发表时间：
2023-01
影响因子：
11.1
通讯作者：
Ammar H. Elsheikh;E. El-Said;M. A. Abd Elaziz;Mana Fujii;H. El-Tahan
中科院分区：
文献类型：
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作者：
Ammar H. Elsheikh;E. El-Said;M. A. Abd Elaziz;Mana Fujii;H. El-Tahan
在这里，我们提出了一种新的紧凑型设计的垂直蒸馏塔的基础上太阳能蒸馏器。实验装置包括一个垂直塔，有五个水盘，由一个金属管道支撑，周围有一个玻璃罩。对该塔的产水量、热性能、火用性能和经济性能进行了研究和分析。提出了一种新的性能预测混合模型。将随机向量函数连接（RVFL）神经网络与Runge Kutta优化器（RUN）相结合，对已建塔的产水量和温度进行预测。模型的效率进行了比较，纯RVFL和使用粒子群优化算法（PSO）优化RVFL模型。建议的设计的饮用水产量为2.1 L/m2（考虑托盘面积）和5.3 L/m2（考虑土地使用面积），能量和火用效率分别为1031.7%和103.3%。生产的饮用水的成本约为0.013美元/升。与传统的太阳能蒸馏器设计相比，所开发的系统提供了相当大的改进。所提出的RVFL-RUN模型在预测系统性能方面优于纯RVFL和RVFL-PSO模型。用RVFL-RUN模型预测的产水率和水温与实测值的决定系数分别为0.91和0.97。
Here we present a new compact design of a vertical water distillation tower based on solar stills. The experimental setup consisted of a vertical tower with five water trays, supported by a metal duct and surrounded by a glass enclosure. The water yield and thermal, exergic, and economic features of the tower were investigated and analyzed. A new performance prediction hybrid model was also developed. A powerful artificial intelligence tool called the random vector functional link (RVFL) neural network was integrated with the Runge Kutta optimizer (RUN) to predict the water yield and temperature of the established tower. The model efficiency was compared to that of pure RVFL and an optimized RVFL model using a particle swarm optimizer (PSO). The drinkable water yield of the proposed design was 2.1 L/m2(considering the tray area) and 5.3 L/m2(considering the land use area); energy and exergy efficiencies were ∼31.7% and ∼3.3%, respectively. The cost of the produced drinkable water was approximately $0.013/L. The developed system provides considerable improvement compared with conventional designs of solar stills. The proposed RVFL–RUN model outperformed the pure RVFL and RVFL–PSO models for predicting system performance. The coefficients of determination between the experimental water productivity and water temperature and the predicted values, using the RVFL–RUN model, were 0.91 and 0.97, respectively.