DCLR Modifi ed Petroleum Asphalt Optimization and Mixture Road Performance

Authors

  • Yifan Chu College of Chemistry and Biological Engineering, Taiyuan University of Science and Technology
  • Tao Li College of Chemistry and Biological Engineering, Taiyuan University of Science and Technology
  • Jiaying Ding College of Chemistry and Biological Engineering, Taiyuan University of Science and Technology
  • Zhenmin Liu College of Chemistry and Biological Engineering, Taiyuan University of Science and Technology
  • Yongbing Xue College of Chemistry and Biological Engineering, Taiyuan University of Science and Technology

DOI:

https://doi.org/10.30564/frae.v5i1.5196

Abstract

In recent decades, modifi ed asphalt materials have been used in enhancing the traffi c load on the roads. The main objective of this paper is to explore the modifi cation eff ect of direct coal liquefaction residue (DCLR) on as- phalt binders and investigate the eff ectiveness of DCLR in improving the performance of asphalt road. This paper prepared modifi ed petroleum as-
phalt under diff erent process conditions and tested its penetration, softening point and ductility index. Based on the experimental data, according to gray correlation degree, the performance for the asphalt was compared. The performance for the modifi ed asphalt is simulated and predicted using poly-nomial functions. The modifi ed asphalt was analyzed by FT-IR, TGA, SEM and HPLC. The results show that the optimal process conditions for DCLR modifi ed asphalt are shear mixing time of 45 min, shear mixing tempera- ture of 150 °C and shear mixing rate of 4000 r/min. The predicted fi t with the experimental data of 0.993 further demonstrates the effectiveness of the method. The characterization results show no signifi cantchemical change between the DCLR and the asphalt. DCLR can signifi cantly improve the high temperature performance and water stability of the asphalt, but it has little impact on its low temperature performance.

Keywords:

Orthogonal design, DCLR modifi ed asphalt, Asphalt mixture Coal liquefaction residue, Microscopic composition

References

[1] Wang, Zh.X.,Yang, J.L.,Liu, Zh.Y., 2007. Preliminary study on direct coal liquefaction residue as paving asphalt modifier. Journal of Fuel Chemistryand Technology. 35(1), 109-112.

[2] Xue, Y.B.,Wang, Zh.Y., Yang, J.L., et al., 2002. Effect of temperature on properties of FCC slurry and coal treated asphalt. Coal conversion. (03), 47-50. (in Chinese with English abstract)

[3] Zhang, K.Q., 2014. Study on coal tar pitch / SBS composite modified petroleum asphalt.Taiyuan University of science and technology. (in Chinese with English abstract)

[4] Xue, Y.B., Ge, Z.F., Li, F.Ch., et al., 2017. Modified asphalt properties by blending petroleum asphalt and coal tar pitch. Fuel. 207, 64-70.

[5] Ji, J., Ma, R.D., Zheng, W.H., et al., 2018. Effect of coal liquefaction residue on asphalt aggregate adhesion. China Journal of Highway and Transport. 31(09), 27-33. (in Chinese with English abstract)

[6] Lv, S.T., et al., 2021. Experimental investigation on the performance of bone glue and crumb rubber compound modified asphalt. Construction and Building Materials. 305.

[7] Xue, Y.B., Wang, Zh.Y., Li, B.Zh., et al., 2013. Co-processing petroleum catalytic slurry with coal.Journal of Coal Science and Engineering. 16(4), 554-559.

[8] Song, Zh.Zh., Sun, M., et al., 2017. Modification of petroleum asphalt by extraction components of Shenhua coal direct liquefaction residue. Chemical Industry and Engineering Progress. 36(09), 3273-3279.

[9] Ge, Z.F., 2016. Study on modification process and mechanism of road asphalt. Taiyuan University of science and technology. (in Chinese with English abstract)

[10] Zhao, Y., Zhang, W.L., et al., 2020. Research on prediction model of asphalt pavement compactness based on BP artificial neural network. Transpo World. (14), 29-32.

[11] Xie, Ch.L., Zhang, Y., et al., 2018. Fatigue performance prediction model of asphalt mixture based on BP neural network. Journal of Chongqing Jiaotong University(Natural Sciences). 37(02), 35-40. (in Chinese with English abstract)

[12] Dou, F.H., Xu, Zh.M., et al., 2016. Application of BP neural network in asphalt ductility prediction.Petroleum Asphalt. 30(01), 57-61. (in Chinese with English abstract)

[13] Shi, Y.F., 2017. Preparation and properties of modified asphalt from direct coal liquefaction residue. Beijing architecture university. (in Chinese with English abstract)

[14] Li, Y.B., Li, X.H., et al., 2016. Prediction of regenerative fatigue life of rubber asphalt concrete based on artificial neural network model. Journal of China & Foreign Highway. 36(01), 239 245. (in Chinese with English abstract)

[15] Wang, Y.Zh., 2018. Research on key tasks of energy system and mechanism reform in Shanxi Province. Research on Coal Economy. 38(04), 37-43. (in Chinese with English abstract)

[16] Zhao, P., 2012. Study on road performance of coal asphalt and petroleum asphalt.Taiyuan University of science and technology. (in Chinese with English abstract)

Downloads

Issue

Article Type

Articles