Applied Machine Learning for Prediction of CO2Adsorption on Biomass Waste-Derived Porous Carbons

Xiangzhou Yuan, Manu Suvarna, Sean Low, Pavani Dulanja Dissanayake, Ki Bong Lee, Jie Li, Xiaonan Wang, Yong Sik Ok

Research output: Contribution to journalArticlepeer-review

130 Citations (Scopus)


Biomass waste-derived porous carbons (BWDPCs) are a class of complex materials that are widely used in sustainable waste management and carbon capture. However, their diverse textural properties, the presence of various functional groups, and the varied temperatures and pressures to which they are subjected during CO2adsorption make it challenging to understand the underlying mechanism of CO2adsorption. Here, we compiled a data set including 527 data points collected from peer-reviewed publications and applied machine learning to systematically map CO2adsorption as a function of the textural and compositional properties of BWDPCs and adsorption parameters. Various tree-based models were devised, where the gradient boosting decision trees (GBDTs) had the best predictive performance withR2of 0.98 and 0.84 on the training and test data, respectively. Further, the BWDPCs in the compiled data set were classified into regular porous carbons (RPCs) and heteroatom-doped porous carbons (HDPCs), where again the GBDT model hadR2of 0.99 and 0.98 on the training and 0.86 and 0.79 on the test data for the RPCs and HDPCs, respectively. Feature importance revealed the significance of adsorption parameters, textural properties, and compositional properties in the order of precedence for BWDPC-based CO2adsorption, effectively guiding the synthesis of porous carbons for CO2adsorption applications.

Original languageEnglish
Pages (from-to)11925-11936
Number of pages12
JournalEnvironmental Science and Technology
Issue number17
Publication statusPublished - 2021 Sept 7

Bibliographical note

Funding Information:
This work was supported by the Cooperative Research Program for Agriculture Science and Technology Development (Project no. PJ01475801), Rural Development Administration, Republic of Korea. This work was also supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2021R1A2C2011734). Y.S.O, X.Y., and P.D.D. were partly supported by the KU Future Research Grant (KU FRG) Fund, Korea Biochar Research Center (KBRC) Fund, and the Association of Pacific Rim Universities (APRU) Sustainable Waste Management Program from the Korea University, Republic of Korea. X.W., M.S., S.L., and J.L. were supported by the Singaporean RIE2020 Advanced Manufacturing and Engineering (AME) IAF-PP grant “Cyber-physical production system (CPPS) toward contextual and intelligent response” by the Agency for Science, Technology and Research under grant no. A19C1a0018 and the model factory at SIMTech.

Publisher Copyright:
© 2021 The Authors. Published by American Chemical Society


  • carbon materials
  • gas adsorption and separation
  • gradient boosting decision trees
  • low carbon technology
  • machine learning
  • sustainable waste management

ASJC Scopus subject areas

  • General Chemistry
  • Environmental Chemistry


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