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A quantitative investigation on pyrolysis behaviors of metal ion-exchanged coal macerals by interpretable machine learning algorithms

Author

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  • Yao, Qiuxiang
  • Wang, Linyang
  • Ma, Mingming
  • Ma, Li
  • He, Lei
  • Ma, Duo
  • Sun, Ming

Abstract

Generalizing the rules from complex processes such as catalytic pyrolysis to guide their process control is always a difficult but attractive task. The influences of ion-exchange of metal ions (Na+, K+, Ca2+, Mg2+, Co2+ and Ni2+) on the pyrolysis behavior of vitrinite and inertinite from Shendong coal were investigated by thermogravimetric analyzer-Fourier transform infrared spectrometer (TG-FTIR), fixed-bed reactor (FBR), gas chromatograph-mass spectrometer (GC-MS) and X-ray diffractometer (XRD). A set of machine learning models was successfully constructed based on random forest, support vector machine, and Gaussian process regression, to quantify the relationships between pyrolysis behaviors and the properties of metals and macerals. The leave-one-out-cross validation showed that there are considerable determination coefficients (R2 > 0.9) between predicted and experimental values for most responses (17 out of 29). By combining genetic programming-based symbolic regression with the black-box algorithm, 23 symbolic regression expressions with high confidence were successfully constructed. This work is a pioneering attempt of optimization in small-scale experiments. By utilizing the highly interpretable models, a demand-orientated (multi-)optimization coal pyrolysis can be achieved. The bi-objective optimization was conducted on the yield of tar and the content of light aromatics in tar, and the results show that Co is the optimal loading metal.

Suggested Citation

  • Yao, Qiuxiang & Wang, Linyang & Ma, Mingming & Ma, Li & He, Lei & Ma, Duo & Sun, Ming, 2024. "A quantitative investigation on pyrolysis behaviors of metal ion-exchanged coal macerals by interpretable machine learning algorithms," Energy, Elsevier, vol. 300(C).
    • Handle: RePEc:eee:energy:v:300:y:2024:i:c:s0360544224013872
    DOI: 10.1016/j.energy.2024.131614
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    References listed on IDEAS

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