Co-pyrolysis kinetics and enhanced synergy for furfural residues and polyethylene using artificial neural network and fast heating.

Journal: Waste management (New York, N.Y.)
PMID:

Abstract

The efficient co-utilization of biomass and waste plastics is a key method to address the widely concerned environmental problem and replace traditional energy. Co-pyrolysis behaviors and synergistic effects of furfural residues (FR) and polyethylene (PE) were studied by TG and artificial neural network (ANN). The FWO and KAS method were employed to analyze the kinetics and thermodynamics. The average activation energies calculated by FWO and KAS methods were 269.17 kJ/mol and 276.77 kJ/mol, respectively. The ANN achieved the minimum validation error at 79 iterations, and its best performance was at the 73rd iteration, with a minimum MSE of 0.0073503. Co-pyrolysis experiments were conducted in a fast heating reactor with different temperatures and ratios. Product distributions were analyzed using GC-MS, simulated distillation, and Pearson correlation coefficient analysis. As the co-pyrolysis temperature increased from 500 to 800 °C, the bio-oil yield initially rose from 19.20 % to a peak of 21.97 % at 600 °C, then declined to 12.48 %. Co-pyrolysis promoted hydrocarbon production while reducing oxygenate compounds in the bio-oil. Pearson correlation analysis revealed that bio-oil yield exhibited a positive correlation with water and char yields at different temperatures and ratios, while showing an inverse correlation with wax yield. This research contributes to advancing our understanding of co-pyrolysis characteristics of FR and PE, with implications for optimizing bio-oil production and facilitating sustainable waste utilization strategies.

Authors

  • Shuai Li
    School of Molecular Biosciences, Center for Reproductive Biology, College of Veterinary Medicine, Washington State University.
  • Rui Qu
    Breast and Thyroid Center, The First People's Hospital of Zunyi (The Third Affiliated Hospital of Zunyi Medical University), Zunyi, Guizhou, China.
  • Erfeng Hu
    State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044 PR China. Electronic address: ehu@cqu.edu.cn.
  • Zuohua Liu
    State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044 PR China.
  • Qingang Xiong
    State Key Laboratory of Pulp and Paper Engineering, South China University of Technology, Guangzhou 510640 PR China.
  • Jianglong Yu
  • Yongfu Zeng
    State Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing 400044 PR China.
  • Moshan Li
    Suzhou Industrial Park Monash Research Institute of Science and Technology, Suzhou, PR China; Biological and Chemical Engineering, Monash University, Clayton, Australia.