Preparation of Expandable Polystyrene by Multi-Stage Initiator Dosing/Styrene-Butadiene-Styrene Blends with Application of Artificial Neural Networks

Document Type : Research Article


Department of Chemical Engineering, Ahar Branch, Islamic Azad University, Ahar, I.R. IRAN


Expandable Polystyrene (EPS) is one of the most used polymers. Preparation of this polymer by the conventional method has some problems which cause the synthesis process to be difficult and also decrease the quality of the prepared EPS. In this study, Styrene-Butadiene-Styrene (SBS)  has been added to improve some properties of the prepared polymer and the Multi-stage Initiator Dosing (MID) method has been used to reduce the time of the polymerization which causes the polymer’s production capacity to increase. SBS has been added to EPS in shares of 2%wt, 4%wt, and 6%wt. The polydispersity index (PDI) test and the amount of tension in the yield point of the polymer have been checked. The amount of absorbed pentane on the polymer studied. The amount of residual monomer on the polymer has been investigated. All of the studies happened under different conditions like different percentages of initiator, different numbers of dosings, and different time periods of the first stage of the polymerization. Experimental data have been simulated by Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) methods of Artificial Neural Networks (ANN). The performance of the simulation for the RBF method was better in comparison to the MLP method due to having a strong scientific foundation and also the ability to filter noises. The experimental data show that a higher amount of SBS causes improvement in properties like elongation at break, better pentane absorption, and PDI amount has improved, which shows the better distribution of molecular weight and a decrease in residual monomer in products. 


Main Subjects

[1] Jing X., Peng X.F., Mi H.Y., Wang Y.S., Zhang S., Chen B.Y., Zhou H.M., Mou W.J., Cell Evolution and Compressive Properties of Styrene–Butadiene–Styrene Toughened and Calcium Carbonate Reinforced Polystyrene Extrusion Foams with Supercritical Carbon Dioxide, J. Appl. Polym. Sci., 133(23) (2016).
        DOI: 10.1002/APP.43508.
[2] Varnagiris S., Tuckute S., Lelis M. Milcius D., SiO2 Films as Heat Resistant Layers for Protection of Expandable Polystyrene Foam from Flame Torch–Induced Heat, Journal of Thermoplastic Composite Materials, 31(5) (2017).
       DOI: 10.1177/0892705717718238
[3] Zhang S., Ji W., Han Y., Gu X., Li H., Sun J., Flame-Retardant Expandable Polystyrene Foams Coated with Ethanediolmodified Melamine–Formaldehyde Resin and Microencapsulated Ammonium Polyphosphate, Journal of Applied Polymer Science, 135(28): 46471 (2018).
       DOI: 10.1002/app.46471
[4] Sankar L.P., Sivasankar S.,  Shunmugasundaram M., Kumar A.P., Predicting the Polymer Modified Ferrocement Ultimate Flexural Strength Using Artificial Neural Network and Adaptive Network Based Fuzzy Inference System, Materials Today: Proceedings, 27: 1375-1380 (2020).
[5] Sharmaa A., Kumar S. A., Kushvaha V., Effect of Aspect Ratio on Dynamic Fracture Toughness of Particulate Polymer Composite Using Artificial Neural Network, Engineering Fracture Mechanics 228: 106907, Elsevier, (2020).
[6] Ji W., Yao Y., Guo J., Fei B., Gu X., Li H., Sun J., Zhang S., Toward an Understanding of How Red Phosphorus and Expandable Graphite Enhance the Fire Resistance of Expandable Polystyrene Foams, Journal of Applied Polymer Science, 137(35): 49045 (2020).
       DOI: 10.1002/app.49045
[7] Shao X., Du Y., Zheng X., Wang J., Wang Y., Zhao S., Xin Z., Li L., Reduced Fire Hazards of Expandable Polystyrene Building Materials via Intumescent Flame-Retardant Coatings, J Mater. Sci., 55: 7555-7572 (2020).
       DOI: 10.1007/s10853-020-04548-z
[8] Nawghare S.M., Mandal J.M., Effectiveness of Expanded Polystyrene (EPS) Beads Size on Fly Ash Properties, International Journal of Geosynthetics and Ground Engineering,6: (2020).
       DOI: 10.1007/s40891-020-0189-3
[9] Ji W., Wang D., Guo J., Fei B., Gu X., Li H., Sun J., Zhang S., The Preparation of Starch Derivatives Reacted with Urea-Phosphoric Acid and Effects on Fire Performance of Expandable Polystyrene Foams, Carbohydrate Polymers233: 115841 (2020).
       DOI: 10.1016/j.carbpol.2020.115841
[10] Wang L., Wang C., Liu P., Jing Z., Ge X., Jiang Y., The Flame Resistance Properties of Expandable Polystyrene Foams Coated with a Cheap and Effective Barrier Layer, Construction and Building Materials, 176: 403–414 (2018)
[11] Kannan P., Biernacki J.J., Visco Jr D.P., Lambert W., Kinetics of Thermal Decomposition of Expandable Polystyrene in Different Gaseous Environments, J. Anal. Appl. Pyrolysis, 84: 139–144 (2009)
        DOI: 10.1016/j.jaap.2009.01.003
[12] Scheirs J., Priddy D., Modern Styrenic Polymers. Wiley Series in polymer science, England- Swieten APV, Westmijze H, Schut J (2003) U.S. Pat. 6639037 (B2), (2003).
[13] Derakhshanfard F., Fazeli N., Vaziri A., Heydarinasab A., Kinetic Study of the Synthesis of Expandable Polystyrene via Multi-Stage Initiator Dosing Method, J. Polym. Res., 22:   (2015).
       DOI: 10.1007/s10965-015-0766-7
[14] Shan L., Qi X., Duan X., Liu S., Chen G., Effect of Styrene-Butadiene-Styrene (SBS) on the Rheological Behavior Of Asphalt Binders, Construction and Building Materials, 231: 117076 (2020).
[15] Qian C., Fan W., Yang G., Han L., Xing B., Lv X., Influence of Crumb Rubber Particle Size and SBS Structure on Properties of CR/SBS Composite Modified Asphalt, Construction and Building Materials, 235: 117517 (2020).
[16] Ghanoon S.A., Tanzadeh J., Mirsepahi M., Laboratory Evaluation of the Composition of Nano-Clay, Nano-Lime And SBS Modifiers on Rutting Resistance of Asphalt Binder, Construction and Building Materials, 238: 117592 (2020).
[17] Zhang H., Wang H., Zhong W., A Novel Type of Shape Memory Polymer Blend and the Shape Memory Mechanism, Polymer, 50(6):1596–1601 (2009).
        DOI: 10.1016/j.polymer
[18] Ben Abdallah A., Kallel A., ­“Gamaoun F (2017) Shape Memory Property and Driving Force of the Shape Memory Blend (40% PCL/60% SBS)”, 7th International Conference Design Modelling Mechanical System, Tunisia (2017).
[19] Wang X., Meng S., Tebyetekerwa M., Li Y., Pionteck J., Sun B., Qin Z., Zhu M., Highly Sensitive and Stretchable Piezoresistive Strain Sensor Based on Conductive Poly(Styrene-Butadiene-Styrene)/Few-Layer Graphene Composite Fiber, Composites: Part A, 105: 291–299 (2018).
[20] Yimit M., Ni L., Du Y., Bkan R., Mechanical and Aging Properties of Polypropylene and Styrene-Butadiene-Styrene Composites Under Outdoor and Indoor Conditions, Strength of Materials, 50: 788–799 (2018).
        DOI: 10.1007/s11223-018-0024-4, (2018).
[22] Duong H.C., Chuai D., Woo Y.C., Shon H.K., Nghiem L.D., Sencadas V., A Novel Electrospun, Hydrophobic, and Elastomeric Styrene-Butadiene-Styrene Membrane for Membrane Distillation Applications, Journal of Membrane Science,549(1): 420-427 (2018).
[23] Gonzalez-Fernandez I., Iglesias-Otero M.A., Esteki M., Moldes O.A., Mejuto J.C, Simal-Gandara J., A Critical Review on the Use of Artificial Neural Networks in Olive Oil Production, Characterization and Authentication, Critical Reviews in Food
Science and Nutrition
, 59(12): 1913-1926 (2018).
        DOI: 10.1080/10408398.2018.1433628
[24] Swiercz M., Mariak Z., Krejza J., Lewko J., Szydlik P., Intracranial Pressure Processing with Artificial Neural Networks: Prediction of ICP Trends, Acta Neurochir (Wien) 142: 401–406(2000)., (2000).
[25] Al-Dousari M.M., Garrouch A.A., An Artificial Neural Network Model for Predicting the Recovery Performance of Surfactant Polymer Floods, Journal of Petroleum Science and Engineering 109 51–62 (2013). 
[26] Cecchetti M., Corani G., Guarisoa G., Artificial Neural Networks Prediction of PM10 in the Milan Area. International Congress on Environmental Modelling and Software. 140 (2004).
[27] Xie H., Ma F., “Prediction of Indoor Air Quality Using Artificial Neural Networks”, Fifth International Conference on Natural Computation, (2009).
[28] Rem B.R., Kaming N.K., Tarnowski M., Asteria L., Flaschner N., Becker C., Sengstock K., Weitenberg C., Identifying Quantum Phase Transitions Using Artificial Neural Networks on Experimental Data, Nature Physics, (2019).
[29] Derakhshanfard F., Mehralizadeh A., Application of Artificial Neural Networks for Viscosity of Crude Oil-Based Nanofluids Containing Oxides Nanoparticles, Journal of Petroleum Science and Engineering, 168: 263-272 (2018).
[30] Hedayati Moghaddam A., Sargolzaei J., Haghighi Asl M., Derakhshanfard F., Effect of Different Parameters
 on WEPS Production and Thermal Behavior Prediction Using Artificial Neural Network (ANN)
, Polymer-Plastics Technology and Engineering, 51: 480–486 (2012).
[32] Derakhshanfard F., Mehralizadeh A., Application of Artificial Neural Networks for Viscosity of Crude Oil-Based Nanofluids Containing Oxides Nanoparticles, Journal of Petroleum Science and Engineering, (2018).
         DOI: 10.1016/j.petrol.2018.05.018
[33] Kamarii E., Hajizadeh A. A., Kamali M. R., Experimental Investigation and Estimation of Light Hydrocarbons Gas-Liquid Equilibrium Ratio in Gas Condensate Reservoirs through Artificial Neural Networks, Iranian Journal of Chemistry and Chemical Engineering (IJCCE), 39(6): 163-172 (2020)
         DOI: 10.30492/ijcce.2019.36496,(2020)
[34] Akbarzade K., Danaee I., Nyquist Plots Prediction Using Neural Networks in Corrosion Inhibition of Steel by Schiff Base, Iranian Journal of Chemistry and Chemical Engineering (IJCCE), 37(3): 135-143 (2018).
        DOI: 10.30492/ijcce.2018.29963 (2018).
[35] Tarjomannejad A., Prediction of the Liquid Vapor Pressure Using the Artificial Neural Network-Group Contribution Method, Iranian Journal of Chemistry and Chemical Engineering (IJCCE), 34(4): 97-137 (2015).      
[36] Ghaemi A., Jafari Z., Etemad E., Prediction of CO2 Mass Transfer Flux in Aqueous Amine Solutions Using Artificial Neural Networks Iranian Journal of Chemistry and Chemical Engineering (IJCCE), 39(4): 269-280 (2020).
        DOI: 10.30492/ijcce.2018.31858 (2020)
[37] Derakhshanfard F., Mehralizadeh A. Ghazi Tabtabaei Z., Applications of Multi-Layer Perceptron Artificial Neural Networks for Polymerization of Expandable Polystyrene by Multi-Stage Dosing Initiator, Iranian Journal of Chemistry and Chemical Engineering (IJCCE), 41(3): 890-901 (2022).
[38] Derakhshanfard F., Vaziri A.,  Fzeli N., Heydarinasab A., Optimization of Synthesis of Expandable Polystyrene by Multi-Stage Initiator Dosing, Iranian Journal of Chemical Engineering (IJChE), 13(1): 20-31 (2016).