Investigation of the (catalytic) co-pyrolysis of Shendong coal and coal tar based on rapid pyrolysis and ANN modelling

The catalytic co-pyrolysis of coal and hydrogen-rich substances is an effective way to improve the yield and quality of coal tar. In this paper, the co-pyrolysis experiments of Shendong low rank coal (SD) and low temperature coal tar (HCT) catalyzed by hematite, chalcopyrite, nickel laterite, and molybdenite were carried out respectively. The catalytic co-pyrolysis characteristics and product distribution of coal-tar slurry with different coal-tar ratios were investigated by the thermogravimetric analyzer (TGA), pyrolysis-gas chromatography/mass spectrometer (Py-GC/MS), and fixed bed rapid pyrolysis reactor (FBR). In addition, the response surface design method and artificial neural network (ANN) were introduced to optimize catalyst ratios. The analysis results show that the experimental value of the maximum weight loss rate in the high temperature pyrolysis stage with a coal-tar ratio of 5: 5 is greater than the estimated value. Besides, the coal-tar co-pyrolysis has a synergistic effect, which strongly reduces the apparent activation energy of the pyrolysis process. The addition of HCT promotes the generation of oxygen-containing compounds and aromatics. The presence of natural minerals promotes the formation of light tar, especially mono-aromatics, aliphatics, and phenol in the pyrolysis tar. After optimizing and verifying the catalyst ratio by ANN, the pyrolysis tar yield, light component content, and light tar yield are 50.49 wt%, 46.14%, and 21.14 wt%, respectively. The co-pyrolysis of Shendong low rank coal (SD) and low temperature coal tar (HCT) catalyzed by hematite, chalcopyrite, nickel laterite, and molybdenite was carried out, and the catalytic co-pyrolysis characteristics and product distribution of coal-tar slurry with different coal-tar ratios were investigated by TG, Py-GC/MS, and fixed bed rapid pyrolysis reactor. In addition, the response surface design method and artificial neural network (ANN) were introduced to optimize catalyst ratios. Based on the results above, the catalytic co-pyrolysis mechanism of coal-tar slurry was proposed. [Display omitted] •Rapid catalytic co-pyrolysis of SD and HCT was carried out by Py-GC/MS and FBR.•The effects of natural minerals on co-pyrolysis and optimization by ANN modelling.•The best coal-tar ratio is 5:5, and its tar yield reaches 49.96 wt%.•The natural minerals promote the formation of mono-aromatics and phenol in tar.•The optimal values of YPT, CLC, and YLT are 50.49%, 46.14%, and 21.14%, respect