Modeling the optimal rubber composition using industrial waste

  • Madi B. Abilev Sarsen Amanzholov East Kazakhstan University, Ust-Kamenogorsk, Kazakhstan
  • Almira M. Zhilkashinova Sarsen Amanzholov East Kazakhstan University, Ust-Kamenogorsk, Kazakhstan
  • Sana K. Kabdrakhmanova Sarsen Amanzholov East Kazakhstan University, Ust-Kamenogorsk, Kazakhstan
  • Anna V. Troyeglazova Siberian State University of Geosystems and Technologies, Novosibirsk, Russia
Keywords: rubber composite, optimization of the composition, mathematical modeling, industrial waste, tensile test, vulcanization

Abstract

Due to the complex composition of the rubber compound, the optimization of the formulation for its preparation is a complex process. The experiments required to determine the optimal composition are a multi-step process that requires time and money. The purpose of this article is to use the method of mathematical modeling to determine the optimal composition of a rubber compound with the addition of industrial waste. Sulfur of the Tengiz deposit and metallurgical production slags were used as industrial waste. The Protodyakonov equation was used to derive the generalized equation and check its adequacy. The escaped equations were used to prepare the rubber compound. The process of vulcanization of the mixture with and without the addition of waste was carried out. The kinetics of vulcanization of the optimized mixture has been studied. The optimized composite provides higher minimum and maximum torque levels, shorter initiation times and optimal cure times compared to a blend without additive. Tensile tests have shown that the composition of the rubber compound, selected by the method of mathematical modeling, is not inferior to the standard formulation. The computational model for determining the optimal composition of the rubber compound can be used for research and applied purposes in various industries related to rubber.

References

1 Rolere S, Liengprayoon S, Vaysse L et al (2015) Polym Test 43:83-93. Crossref

2 Choi S-S, Kwon H-M, Kim Y et al (2017) Polym Test 59:414-422. Crossref

3 Zhuang G-L, Wey M-Y, Tseng H-H (2016) J Membr Sci 520:314-325. Crossref

4 Zhao X, Niu K, Xu Y et al (2016) Compos Part B-Eng 107:106-112. Crossref

5 Vélez JS, Velásquez S, Giraldo D (2016) Polym Test 56:1-9. Crossref

6 Lopes D, Ferreira MJ, Russo R et al (2015) J Clean Prod 92:230-236. Crossref

7 Sisanth KS, Thomas MG, Abraham J et al (2017) Progress in Rubber Nanocomposites. Woodhead Publishing, Cambridge, UK. P.1-39. ISBN: 978-0-08-100409-8

8 George SC, Rajan R, Aprem AS et al (2016) Polym Test 51:165-173. Crossref

9 Sushkevich VL, Ordomsky VV, Ivanova II (2012) Appl Catal A-Gen 441-442:21-29. Crossref

10 Rabiei S, Shojaei A (2016) Eur Polym J 81:98-113. Crossref

11 Polacco G, Filippi S (2014) Construction and Building Materials 58:94-100. Crossref

12 Griebel JJ, Glass RS, Char K et al (2016) Progress in Polymer Science 58:90-125. Crossref

13 Alves LC, Rubinger MMM, Tavares EC et al (2013) Journal of Molecular Structure 1048:244-251. Crossref

14 Aprem AS, Joseph K, Mathew T et al (2003) Eur Polym J 39(7):1451-1460. Crossref

15 Silva LMA, Andrade FD, Filho EGA et al (2016) Fuel 186:50-57. Crossref

16 Schwartz GA, Cerveny S, Marzocca ÁJ et al (2003) Polymer 44(23):7229-7240. Crossref

17 Malas A, Das CK (2017) J Alloys Compd 699:38-46. Crossref

18 Song J, Ma L, He Y et al (2015) Chin J Chem Eng 23(5):853-859. Crossref

19 Song K (2017) Progress in Rubber Nanocomposites. Woodhead Publishing, Cambridge, UK. P.41-80. ISBN: 978-0-08-100409-8

20 Tchalla ST, Le Gac PY, Maurin R et al (2017) Polym Degrad Stabil 139:28-37. Crossref

21 Wang J, Ji C, Yan Y et al (2015) Polym Degrad Stabil 121:149-156. Crossref

22 Bahl K, Miyoshi T, Jana SC (2014) Polymer 55(16):3825-3835. Crossref

23 Zhong B, Jia Z, Luo Y et al (2017) Polym Test 58:31-39. Crossref

24 Ondrušová D, Slavomíra D, Pajtášová M et al (2017) Procedia Eng 177:462-469. Crossref

25 Uddin MS, Ju J (2016) Polymer 101:34-47. Crossref

26 Alimardani M, Razzaghi-Kashani M, Ghoreishy MHR (2017) Mater Des 115:348-354. Crossref

27 Nguyen QT, Tinard V, Fond C (2015) Int J Solids Struct 75-76:235-246. Crossref

28 Ovalle Rodas C, Zaïri F, Naït-Abdelaziz M et al (2015) Int J Solids Struct 58:309-321. Crossref

29 Marckmann G, Chagnon G, Le Saux M et al (2016) Int J Solids Struct 97-98:554-565. Crossref

30 Shangguan W-B, Wang X-L, Deng J-X et al (2014) Mater Des 58:65-73. Crossref
Published
2020-12-24
How to Cite
Abilev, M., Zhilkashinova, A., Kabdrakhmanova, S., & Troyeglazova, A. (2020). Modeling the optimal rubber composition using industrial waste. Chemical Bulletin of Kazakh National University, (4), 12-25. https://doi.org/https://doi.org/10.15328/cb1176
Section
Analytical Chemistry