ABSTRACT

Conventional single slope solar still integrated with a parabolic trough collector in addition to packaged glass ball layer was used in water desalination. Experimental work carried and data obtained were used in modeling the influential parameters affecting water desalination using principal component analysis to reduce parameters’ numbers. These parameters were then applied in constructing a Response Surface Model (RSM). System’s Performance has been predicted in terms of temperatures; saline water temperatures (T_w), glass cover temperatures (T_g), dry bulb temperature (T_db), and wet bulb temperature (T_wb) inside the conventional solar still. Along with the above temperatures, ambient air temperature (T_a), Oil inlet temperature (T_oi) and solar intensity (I). The impact of these parameters can highly effect the water desalination yield. The RSM is developed to predict the impact of these parameters. Principal Component Analysis (PCA) reduced and categorized the number of effective parameters to three components containing the parameters (T_w, T_wb, and T_db), (I, T_a, and T_g) and (T_a, T_oi, and I) respectively. These combinations were then tested using the two different RSM optimized models; Modified Reduced Quadratic and Two-Factor Interaction (2FI) model. These models were tested by monitoring nine statistical indices namely: RMSE, P-value, F-value, R², R²_adj, R²_pred, BIC, PRESS, and AICc. Results obtained for this model showed minimum RMSE value and higher values of R², R²_adj and R²_pred as well as lowest values for BIC and AICc. Concluding supremacy of (I, T_a, and T_g) Modified Reduced 2FI model over the others.

摘要

传统的单坡太阳能仍然与抛物线槽集热器和封装玻璃球层相结合，用于海水淡化。进行的实验工作和获得的数据用于模拟影响海水淡化的影响参数，使用主成分分析来减少参数的数量。然后将这些参数应用于构建响应面模型 (RSM)。系统性能已根据温度进行预测；传统太阳能_蒸馏器内的盐水温度 (T _w )、玻璃盖温度 (T _g )、干球温度 (T _db ) 和湿球温度 (T _wb )。除上述温度外，环境空气温度（T _a)、进油温度 (T _oi ) 和太阳强度 (I)。这些参数的影响会极大地影响海水淡化产量。开发 RSM 是为了预测这些参数的影响。主成分分析 (PCA) 将有效参数的数量减少并归类为包含参数（T _w、T _wb和 T _db）、（I、T _a和 T _g）和（T _a、T _oi、和 I) 分别。然后使用两种不同的 RSM 优化模型测试这些组合；改进的减少二次和双因素相互作用 (2FI) 模型。这些模型通过监测九个统计指标进行测试，即：RMSE、P-值、F-值、R ²、R ² _adj、R ² _pred、BIC、PRESS 和 AICc。为该模型获得的结果显示最小RMSE 值和较高的R ²、R ² _adj和R ² _pred值以及BIC 和AICc 的最低值。总结（I、T _a和 T _g）改进的简化 2FI 模型优于其他模型。