Experimental studies and artificial neural network modeling of surface tension of aqueous sodium L-prolinate solutions and piperazine blends

doi:10.1016/j.cjche.2019.01.006

中国化学工程学报 ›› 2019, Vol. 27 ›› Issue (8): 1904-1911.DOI: 10.1016/j.cjche.2019.01.006

• Chemical Engineering Thermodynamics • 上一篇下一篇

Experimental studies and artificial neural network modeling of surface tension of aqueous sodium L-prolinate solutions and piperazine blends

Muhammad Shuaib Shaikh¹, A. M. Shariff², M. A. Bustam², Sahil Garg³, Khadija Qureshi¹, Pervez Hameed Shaikh⁴, Inamullah Bhatti^1,4

1 Department of Chemical Engineering, Mehran University of Engineering & Technology Jamshoro, Sindh 76062, Pakistan;
2 CO₂ Research Centre(CO2RES), Universiti Teknologi PETRONAS, Bandar Seri Iskandar 32610, Perak, Malaysia;
3 School of Chemical Engineering, The University of Queensland, St Lucia 4072, Australia;
4 Office of Research Innovation and Commercialization(ORIC), Mehran University of Engineering & Technology Jamshoro, Sindh 76062, Pakistan

收稿日期:2018-08-16 修回日期:2018-12-23 出版日期:2019-08-28 发布日期:2019-11-16
通讯作者: Muhammad Shuaib Shaikh

Experimental studies and artificial neural network modeling of surface tension of aqueous sodium L-prolinate solutions and piperazine blends

Muhammad Shuaib Shaikh¹, A. M. Shariff², M. A. Bustam², Sahil Garg³, Khadija Qureshi¹, Pervez Hameed Shaikh⁴, Inamullah Bhatti^1,4

1 Department of Chemical Engineering, Mehran University of Engineering & Technology Jamshoro, Sindh 76062, Pakistan;
2 CO₂ Research Centre(CO2RES), Universiti Teknologi PETRONAS, Bandar Seri Iskandar 32610, Perak, Malaysia;
3 School of Chemical Engineering, The University of Queensland, St Lucia 4072, Australia;
4 Office of Research Innovation and Commercialization(ORIC), Mehran University of Engineering & Technology Jamshoro, Sindh 76062, Pakistan

Received:2018-08-16 Revised:2018-12-23 Online:2019-08-28 Published:2019-11-16
Contact: Muhammad Shuaib Shaikh

摘要/Abstract

摘要： The surface tension study is very crucial for the design of CO₂ gas absorption contacting equipment. The significance of the surface tension has been increasing due to its consideration in various technological fields. This property influences the mass transfer and hydrodynamics of gas absorption systems, mainly multiphase systems, in which the interface between gas and liquid exists. Therefore, in this study, surface tension of aqueous solutions of sodium L-prolinate (SP) and piperazine (PZ) blends were measured at ten different temperatures from (298.15 to 343.15) K. The SP mass fractions were 0.10, 0.20, and 0.30; while the mass fractions of PZ were 0.02 and 0.05. The experimental results showed that the surface tension increase with increasing the mass fractions of SP and PZ in aqueous blends, and decrease linearly with rising temperature. The experimental data of surface tension were correlated by two empirical correlations as a function of temperature and mass fractions for estimating the predicted data using the optimized correlation coefficients. Moreover, the modeling of surface tension data was carried out using Artificial Neural Network (ANN) approach. The results obtianed from ANN modeling were compared with applied empirical correlation. It was found that the ANN approach outperformed the empirical correlation used in this study. Besides, a quantitative analysis of variation (ANOVA) was performed in order to determine the significance of data. The surface tension of aqueous SP and SP + PZ was also compared with various conventional solvents.

关键词: Surface tension, L-Prolinate, Piperazine, Correlation, Prediction, Neural network

Abstract: The surface tension study is very crucial for the design of CO₂ gas absorption contacting equipment. The significance of the surface tension has been increasing due to its consideration in various technological fields. This property influences the mass transfer and hydrodynamics of gas absorption systems, mainly multiphase systems, in which the interface between gas and liquid exists. Therefore, in this study, surface tension of aqueous solutions of sodium L-prolinate (SP) and piperazine (PZ) blends were measured at ten different temperatures from (298.15 to 343.15) K. The SP mass fractions were 0.10, 0.20, and 0.30; while the mass fractions of PZ were 0.02 and 0.05. The experimental results showed that the surface tension increase with increasing the mass fractions of SP and PZ in aqueous blends, and decrease linearly with rising temperature. The experimental data of surface tension were correlated by two empirical correlations as a function of temperature and mass fractions for estimating the predicted data using the optimized correlation coefficients. Moreover, the modeling of surface tension data was carried out using Artificial Neural Network (ANN) approach. The results obtianed from ANN modeling were compared with applied empirical correlation. It was found that the ANN approach outperformed the empirical correlation used in this study. Besides, a quantitative analysis of variation (ANOVA) was performed in order to determine the significance of data. The surface tension of aqueous SP and SP + PZ was also compared with various conventional solvents.

Key words: Surface tension, L-Prolinate, Piperazine, Correlation, Prediction, Neural network

Muhammad Shuaib Shaikh, A. M. Shariff, M. A. Bustam, Sahil Garg, Khadija Qureshi, Pervez Hameed Shaikh, Inamullah Bhatti. Experimental studies and artificial neural network modeling of surface tension of aqueous sodium L-prolinate solutions and piperazine blends[J]. 中国化学工程学报, 2019, 27(8): 1904-1911.

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Experimental studies and artificial neural network modeling of surface tension of aqueous sodium L-prolinate solutions and piperazine blends

Experimental studies and artificial neural network modeling of surface tension of aqueous sodium L-prolinate solutions and piperazine blends

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