非極性流體表面張力、飽和蒸汽壓和飽和密度之一般化類神經網路模式

The Generalized Neural Network Model of Nonpolar Fluid Surface Tension, Saturated Vapor Pressure and Saturated Density

黃美惠(Mei-Huei Huang)

指導教授 ： 林顯聖

國立臺北科技大學/工程學院/化學工程研究所/碩士(2007年)

https://doi.org/10.6841/NTUT.2007.00300

摘要

在各種不同化工領域的應用上，常需要很多熱力學性質的數據，但往往因為文獻數據資料收集不易，造成應用上的不便。在利用電腦進行輔助設計時，將收集的熱力學數據變成方程式，是比較方便應用的。一般作法，都是將每個不同物質的熱力學性質分別用不同關聯方程式來描述，如果能將不同物質合併使用一條一般化關聯方程式來描述，將可以增加使用上的便利性。
本研究利用前饋式類神經網路對二十種非極性流體之表面張力、飽和蒸汽壓和飽和密度做數據之擬合，希望找到適合這三種熱力學性質之一般化關聯式。本研究所求得非極性流體之表面張力的一般化類神經網路模式的平均絕對誤差為0.43085%，飽和蒸汽壓為0.4361%，飽和密度為0.10093%。由模擬比較發現，本研究所得一般化類神經網路模式具有相當之實用性。

關鍵字

前饋式類神經網路 ； 非極性流體 ； 表面張力 ； 飽和蒸汽壓 ； 飽和密度

並列摘要

Thermodynamic data are required in the application of different chemical fields. Because the data is difficult to find so there is a few inconvenient for use. In the computer aided design field, thermodynamic data is collected and fitted into some equations. Those fitting equations are more convenient for use. In general, the thermodynamic data of different chemical substance are fitted into different correlation equations. If one generalized correlation equation can be used to describe all substances, it can increase the convenience for use.

In our research, feedforward neural network is utilized to fit the surface tension, saturated vapor pressure and saturated densities data of twenty nonpolar fluids. Three individual generalized correlation equations are determined for these three kinds of thermodynamic qualities. The absolute average deviation of the generalized neural network of nonpolar fluid surface tension is 0.43085%. The absolute average deviation of the generalized neural network of nonpolar fluid saturated vapor pressure is 0.4361%. The absolute average deviation of the generalized neural network of nonpolar fluid saturated densities is 0.10093%. In result, the generalized artificial neural network of the research is practicable.

並列關鍵字

Feedforward Neural Network ； Nonpolar fluids ； Surface Tension ； Saturated Vapor Pressure ； Saturated Densities ； Data-fitting

參考文獻

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