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Optimizing macroalgal biochar-based catalysts for monophenols recovery during wood dust catalytic pyrolysis: A comparative study using response surface methodology and artificial neural network

Author

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  • Cao, Bin
  • Shen, Weiwei
  • Pan, Cheng
  • Kumar, Sunel
  • Yuan, Chuan
  • Jiang, Ding
  • Hu, Yamin
  • Ling, Qifan
  • Abomohra, Abdelfatah
  • Gong, Xun
  • Esakkimuthu, Sivakumar
  • Wang, Shuang

Abstract

This study aimed to explore the optimization of macroalgal biochar-based catalysts (MBBCs) for enhanced monophenolic production in biomass pyrolysis. By utilizing response surface methodology (RSM) and artificial neural network (ANN), key variables in the activation process during preparation of MBBCs were identified including activation temperature, activator ratio, and CO2 residence time. The results showed that the ANN model, with an R2 of 0.9816, accurately predicted optimal catalyst activation conditions, closely matching the experimentally determined optimal conditions (ENPC800-1:2–30). The bio-oil yield decreased under ENPC800-1:2–30 catalyst, but the monophenolic content increased significantly, reaching a relative content of 60 %, with a total concentration of phenol, cresols, and ethylphenol amounting to 35.54 mg/gbio-oil. The present study revealed significant changes in non-condensable gas composition, including increased hydrogen and carbon monoxide which aid in enhancing their energy applications. Thus, the optimized MBBCs greatly help in improving the bio-oil and non-condensable gas quality and thereby providing vital insights over sustainable chemical production through biomass pyrolysis.

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  • Cao, Bin & Shen, Weiwei & Pan, Cheng & Kumar, Sunel & Yuan, Chuan & Jiang, Ding & Hu, Yamin & Ling, Qifan & Abomohra, Abdelfatah & Gong, Xun & Esakkimuthu, Sivakumar & Wang, Shuang, 2024. "Optimizing macroalgal biochar-based catalysts for monophenols recovery during wood dust catalytic pyrolysis: A comparative study using response surface methodology and artificial neural network," Renewable Energy, Elsevier, vol. 237(PA).
    • Handle: RePEc:eee:renene:v:237:y:2024:i:pa:s0960148124016999
    DOI: 10.1016/j.renene.2024.121631
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    References listed on IDEAS

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