Estimation of Surface Tension of Aqueous Polymer Solutions Using Soft Computing Approaches

Document Type : Research Article


Faculty of Technology and Engineering, University of Mazandaran, Babolsar, I.R. IRAN


The surface tension of aqueous polymer solutions is an important property that plays a vital role in mass and heat transfer. In this study, the surface tension of a polymer mixture is modeled using four algorithms (Adaptive Neuro-Fuzzy Inference System (ANFIS), Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), and  Adaptive group of Ink Drop Spread (AGIDS) ) which has been developed in the soft-computing domain. In this paper, four models for predicting the surface tension are applied and the results were compared with our published experimental data and it was found that the predictions of these models fit the experimental data very accurately. Also, a comparison has been done to evaluate the effectiveness of the relevant four algorithms in the current problem. The simulation results have shown that ANFIS and RBF model predictions are more accurate than the two others in the current problem.


Main Subjects

[1] Hu R.Y.Z., Wang A.T.A., Hartnett J.P., Surface Tension Measurement of Aqueous Polymer Solutions, Experimental Thermal and Fluid Science, 4(6): 723-729 (1991).
[2] Wu S., Interfacial and Surface Tensions of Polymers, Journal of Macromolecular Science—Reviews in Macromolecular Chemistry, 10(1): 1-73 (1974).
[3] Szymczyk K, Zdziennicka A., Wettability, Adhesion, Adsorption and Interface Tension in the Polymer/Surfactant Aqueous Solution System. I. Critical surface tension of polymer wetting and its surface tension, Colloids and Surfaces A: Physicochemical and Engineering Aspects, 402: 132– 138 (2012).
[4] Sharma S., Kamil M., Studies on the Interaction Between Polymer and Surfactant in Aqueous Solutions, Ind. J. of Chemical Technol., 25(3): 294-299 (2018)
[5] Vis M., Blokhuis E.M.,  Interfacial Tension of Phase-Separated Polydisperse Mixed Polymer Solutions, J. Phys. Chem. B, 122: 3354−3362 (2018).
[6] Nath S., Surface Tension of Nonideal Binary Liquid Mixtures as a Function of Composition, Journal of Colloid and Interface Science, 209(1): 116-122 (1999).
[7] Larsen B.L., Rasmussen P., Fredenslund A., A Modified UNIFAC Group-Contribution Model for Prediction of Phase Equilibria and Heats of Mixing, Industrial & Engineering Chemistry Research, 26(11): 2274-2286 (1987).
[8] Bongiorno V., Davis H.T., Modified Van Der Waals Theory of Fluid Interfaces, Physical Review A, 12(5): 2213 (1975).
[9] Babuška R., Verbruggen H.B., An Overview of Fuzzy Modeling for Control, Control Engineering Practice, 4(11): 1593-1606 (1996).
[10] Roy D.G., Singh T.N., Regression and Soft-Computing Models to Estimate Young’s Modulus of CO2 Saturated Coals, Measurement, 129: 91-101 (2018).
[11] Vaidyanathan S., Zhu Q., Azar A.T., Adaptive Control of a Novel Nonlinear Double Convection Chaotic System, In: “Fractional Order Control and Synchronization of Chaotic Systems” (pp. 357-385). Springer, Cham (2017).
[12] Singh R., Umrao R.K., Ahmad M., Ansari M.K., Sharma L.K., Singh T.N., Prediction of Geomechanical Parameters Using Soft-computing and Multiple Regression Approach, Measurement, 99: 108-119 (2017).
[13] Liu Y., Zhang Y., Iterative local ANFIS-based Human Welder Intelligence Modeling and Control
in Pipe GTAW Process: A Data-Driven Approach
, IEEE/ASME Transactions on Mechatronics, 20(3): 1079-1088 (2014).
[14] Wu Q., Wang, X., Shen Q., Research on Dynamic Modeling and Simulation of Axial-Flow Pumping System Based on RBF Neural Network, Neurocomputing, 186: 200-206 (2016).
[16] Vakili M., Yahyaei M., Kalhor K., Thermal Conductivity Modeling of Graphene Nanoplatelets/Deionized Water Nanofluid by MLP Neural Network and Theoretical Modeling Using Experimental Results, International Communications in Heat and Mass Transfer, 74: 11-17 (2016).
[17] Jang J.S., ANFIS: Adaptive-Network-based Fuzzy Inference System, IEEE Transactions on Systems, man, and cybernetics, 23(3): 665-685 (1993).
[18] Hosoz,M., et al., ANFIS Modelling of the Performance and Emissions of a Diesel Engine
Using Diesel Fuel and Biodiesel Blends
, Applied Thermal Engineering, 60(1-2): 24-32 (2013).
[19] Fu Y., Yang H., Ding J., Multiple Operating Mode ANFIS Modelling for Speed Control of HSEMU, IET Intelligent Transport Systems, 12(1): 31-40 (2017).
[20] Veluchamy B., Karthikeyan N., Krishnan B.R., Sundaram C.M., Surface Roughness Accuracy Prediction in Turning of Al7075 by Adaptive Neuro-Fuzzy Inference System, Materials Today: Proceedings, 37: 1356-1358 (2021).
[21] Raj A.S., Oliver D.H., Srinivas Y., Geoelectrical Data Processing Using Neuro Fuzzy Pattern Recognition Scheme for Unambiguous Subsurface Modelling. International Journal of Hydrology Science and Technology, 7(4): 364-389 (2017).
[22] Broomhead D.S., Lowe D., Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks, Royal Signals and Radar Establishment Malvern (United Kingdom) (1988).
[23] Lazzaro D., Montefusco L.B., Radial Basis Functions for the Multivariate Interpolation of Large Scattered Data sets, Journal of Computational and Applied Mathematics, 140(1-2): 521-536 (2002).
[24] Belloir F., Fache A., Billat A., April. A General Approach to Construct RBF Net-Based Classifier, In “ESANN” (pp. 399-404) (1999).
[25] Li Y., et al., Robust and Adaptive Back Stepping Control for Nonlinear Systems using RBF Neural Networks, IEEE Transactions on Neural Networks, 15(3): 693-701 (2004).
[26] Karayiannis N.B., Gradient Descent Learning of Radial Basis Neural Networks, In: Proceedings of International Conference on Neural Networks (ICNN'97) (Vol. 3, pp. 1815-1820). IEEE (1997).
[28] Ruck D.W., et al., The Multilayer Perceptron as an Approximation to a Bayes Optimal Discriminant Function, IEEE Transactions on Neural Networks, 1(4): 296-298 (       ).
[29] Norgaard M., Ravn O., Poulsen N.K., Hansen L.K., “Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook”. London: Springer (2000).
[30] Gardner M.W., Dorling S., Artificial Neural Networks (the Multilayer Perceptron) a Review of Applications in the Atmospheric Sciences, Atmospheric Environment, 32(14-15): 2627-2636 (1998).
[31] Agirre-Basurko E., Ibarra-Berastegi G., Madariaga I., Regression and Multilayer Perceptron-Based Models to Forecast Hourly O3 and NO2 Levels in the Bilbao Area, Environmental Modelling & Software, 21(4): 430-446 (2006).
[32] Radha Krishnan B., Vijayan V., Parameshwaran Pillai T., and Sathish T., Influence of Surface Roughness in Turning Process—an Analysis Using Artificial Neural Network, Transactions of the Canadian Society for Mechanical Engineering, 43(4): 509-514 (2019).
[33] Hecht-Nielsen R., Theory of the Backpropagation Neural Network. In Neural networks for perception (pp. 65-93). Academic Press (1992).
[34] Shouraki S.B., Recursive Fuzzy Modeling Based on Fuzzy Interpolation, Journal of Advanced Computational Intelligence, 3(2): 114-125 (1999).
[35] Murakami M., Honda N., A Study on the Modeling Ability of the IDS Method: A Soft Computing Technique Using Pattern-based Information Processing. International Journal of Approximate Reasoning, 45(3): 470-487 (2007).
[36] Firouzi M., Shouraki S.B., Afrakoti I.E.P., Pattern Analysis by Active Learning Method Classifier. Journal of Intelligent & Fuzzy Systems, 26(1): 49-62 (2014).
[37] Sagha H., Afrakoti I.E.P., Bagherishouraki S., Actor-Critic-Based Ink Drop Spread as an Intelligent Controller, Turkish Journal of Electrical Engineering and Computer Sciences, 21(4): 1015-1034 (2013).
[38] Afrakoti I.E.P., Shouraki S.B., Bayat F.M., Gholami M., Using a Memristor Crossbar Structure to Implement a Novel Adaptive Real-Time Fuzzy Modeling Algorithm, Fuzzy Sets and Systems, 307: 115-128 (2017).
[39] Afrakoti I.E.P., Shouraki S.B., Haghighat B., An Optimal Hardware Implementation for Active Learning Method Based on Memristor Crossbar Structures, IEEE Systems Journal, 8(4): 1190-1199 (2014).
[40] Afrakoti I.E.P., Ghaffari A., Shouraki S.B., March. “Effective Partitioning of Input Domains for ALM Algorithm”. In 2013 First Iranian Conference on Pattern Recognition and Image Analysis (PRIA) (pp. 1-5). IEEE (2013).
[41] Sagha H., Shouraki S.B., Beigy H., Khasteh H., Enayati E., December. “Genetic Ink Drop Spread”. In 2008 Second International Symposium on Intelligent Information Technology Application (Vol. 2, pp. 603-607). IEEE (2008).
[42] Hosseini S.A., Afrakoti I.E.P., Adaptive Group of Ink Drop Spread: a Computer Code to Unfold Neutron Noise Sources in Reactor Cores, Nuclear Engineering and Technology, 49(7): 1369-1378 (2017).