钋
共晶体系
化学
电阻率和电导率
电导率
铵
人工神经网络
热力学
生物系统
分析化学(期刊)
色谱法
物理化学
机器学习
有机化学
计算机科学
物理
生物
量子力学
合金
作者
Fatemeh Saadat Ghareh Bagh,Kaveh Shahbaz,Farouq S. Mjalli,Inas M. AlNashef,Mohd Ali Hashim
标识
DOI:10.1016/j.fluid.2013.07.012
摘要
The evaluation of deep eutectic solvents (DESs) as a new generation of solvents for various practical application requires an insight of the main physical, chemical, and thermodynamic properties. In this study, the experimental measurements of the electrical conductivity of two classes of DESs based on ammonium and phosphonium salts at different compositions and temperatures were reported. The results revealed that electrical conductivity of DESs has temperature-dependency. In addition, molar conductivities of ammonium and phosphonium salts in DESs were obtained using DESs experimental values of electrical conductivities. The feasibility of using an artificial neural network (ANN) model to predict the electrical conductivity of ammonium and phosphonium based DESs at different temperatures and compositions was also examined. A feed-forward back propagation neural network with 8 hidden neurons was successfully developed and trained with the measured electrical conductivity data. The results indicated that among the different networks tested, the network with 8 hidden neurons had the best prediction performance and gave the smallest value of Normalized Mean Square Error (NMSE) (0.0010) and acceptable values of Index of Agreement (IA) (0.9999) and Regression Coefficient (R2) (0.9988). The comparison of the predicted electrical conductivity of DESs by the proposed model with those obtained by experiments confirmed the reliability of the ANN model with an average absolute relative deviation (AARD%) of 4.40%.
科研通智能强力驱动
Strongly Powered by AbleSci AI