Prediction model for biochar energy potential based on biomass properties and pyrolysis conditions derived from rough set machine learning

References

• Li J, Wei B, Wang C, et al. High-performance and stable La0.8Sr0.2Fe0.9Nb0.1O3-δ anode for direct carbon solid oxide fuel cells fueled by activated carbon and corn straw derived carbon. Int J Hydrog Energy. 2018;43(27):12358–12367. doi:10.1016/j.ijhydene.2018.04.176.
   Web of Science ®Google Scholar
• Pan F, Cao Z, Zhao Q, et al. Nitrogen-doped porous carbon nanosheets made from biomass as highly active electrocatalyst for oxygen reduction reaction. J Power Sources. 2014;272:8–15. doi:10.1016/j.jpowsour.2014.07.180.
   Web of Science ®Google Scholar
• Chen C, Yu D, Zhao G, et al. Three-dimensional scaffolding framework of porous carbon nanosheets derived from plant wastes for high-performance supercapacitors. Nano Energy. 2016;27:377–389. doi:10.1016/j.nanoen.2016.07.020.
   Web of Science ®Google Scholar
• Zaini IN, Gomez-Rueda Y, García López C, et al. Production of H2-rich syngas from excavated landfill waste through steam co-gasification with biochar. Energy. 2020;207:118208. doi:10.1016/j.energy.2020.118208.
   Web of Science ®Google Scholar
• Zhang J, Liu J, Liu R. Effects of pyrolysis temperature and heating time on biochar obtained from the pyrolysis of straw and lignosulfonate. Bioresour Technol. 2015;176:288–291. doi:10.1016/j.biortech.2014.11.011.
   PubMed Web of Science ®Google Scholar
• Mu W, Ben H, Ragauskas A, et al. Lignin pyrolysis components and upgrading—technology review. Bioenergy Res. 2013;6(4):1183–1204. doi:10.1007/s12155-013-9314-7.
   Web of Science ®Google Scholar
• Tomczyk A, Sokołowska Z, Boguta P. Biochar physicochemical properties: pyrolysis temperature and feedstock kind effects. Rev Environ Sci Bio/Technol. 2020;19(1):191–215. doi:10.1007/s11157-020-09523-3.
   Web of Science ®Google Scholar
• Cheng F, Luo H, Colosi LM. Slow pyrolysis as a platform for negative emissions technology: an integration of machine learning models, life cycle assessment, and economic analysis. Energy Convers Manage. 2020;223:113258. doi:10.1016/j.enconman.2020.113258.
   Web of Science ®Google Scholar
• Khan M, Ullah Z, Mašek O, et al. Artificial neural networks for the prediction of biochar yield: a comparative study of metaheuristic algorithms. Bioresour Technol. 2022;355:127215. doi:10.1016/j.biortech.2022.127215.
   Google Scholar
• Zhu X, Li Y, Wang X. Machine learning prediction of biochar yield and carbon contents in biochar based on biomass characteristics and pyrolysis conditions. Bioresour Technol. 2019;288:121527. doi:10.1016/j.biortech.2019.121527.
   PubMed Web of Science ®Google Scholar
• Karimi M, Aminzadehsarikhanbeglou E, Vaferi B. Robust intelligent topology for estimation of heat capacity of biochar pyrolysis residues. Measurement. 2021;183:109857. doi:10.1016/j.measurement.2021.109857.
   Web of Science ®Google Scholar
• Karimi M, Hosin Alibak A, Seyed Alizadeh SM, et al. Intelligent modeling for considering the effect of bio-source type and appearance shape on the biomass heat capacity. Measurement. 2022;189:110529. doi:10.1016/j.measurement.2021.110529.
   Web of Science ®Google Scholar
• Ascher S, Watson I, You S. Machine learning methods for modelling the gasification and pyrolysis of biomass and waste. Renewable Sustainable Energy Rev. 2022;155:111902. doi:10.1016/j.rser.2021.111902.
   Web of Science ®Google Scholar
• Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell. 2019;1(5):206–215. doi:10.1038/s42256-019-0048-x.
   PubMedGoogle Scholar
• Chong JW, Thangalazhy-Gopakumar S, Tan RR, et al. Estimation of fast pyrolysis bio-oil properties from feedstock characteristics using rough-set-based machine learning. Int J Energy Res. 2022;46(13):19159–19176. doi:10.1002/er.8201.
   Web of Science ®Google Scholar
• Rudin C, Ertekin Ş. Learning customized and optimized lists of rules with mathematical programming. Math Program Comput. 2018;10(4):659–702. doi:10.1007/s12532-018-0143-8.
   Google Scholar
• Aviso KB, Janairo JIB, Promentilla MAB, et al. Prediction of CO2 storage site integrity with rough set-based machine learning. Clean Technol Environ Policy. 2019;21(8):1655–1664. doi:10.1007/s10098-019-01732-x.
   Web of Science ®Google Scholar
• Radhakrishnapany KT, Wong CY, Tan FK, et al. Design of fragrant molecules through the incorporation of rough sets into computer-aided molecular design. Mol Syst Des Eng. 2020;5(8):1391–1416. doi:10.1039/D0ME00067A.
   Web of Science ®Google Scholar
• Malucelli LC, Silvestre GF, Carneiro J, et al. Biochar higher heating value estimative using thermogravimetric analysis. J Therm Anal Calorim. 2020;139(3):2215–2220. doi:10.1007/s10973-019-08597-8.
   Web of Science ®Google Scholar
• Li Y, Gupta R, Zhang Q, et al. Review of biochar production via crop residue pyrolysis: development and perspectives. Bioresour Technol. 2023;369:128423. doi:10.1016/j.biortech.2022.128423.
   PubMed Web of Science ®Google Scholar
• Qian C, Li Q, Zhang Z, et al. Prediction of higher heating values of biochar from proximate and ultimate analysis. Fuel. 2020;265:116925. doi:10.1016/j.fuel.2019.116925.
   Web of Science ®Google Scholar
• Qian Y, Liang J, Dang C. Converse approximation and rule extraction from decision tables in rough set theory. Comput Math Appl. 2008;55(8):1754–1765. doi:10.1016/j.camwa.2007.08.031.
   Web of Science ®Google Scholar
• Pawlak Z. Rough sets, decision algorithms and Bayes' theorem. Eur J Oper Res. 2002;136(1):181–189. doi:10.1016/S0377-2217(01)00029-7.
   Web of Science ®Google Scholar
• He X, Liu Z, Niu W, et al. Effects of pyrolysis temperature on the physicochemical properties of gas and biochar obtained from pyrolysis of crop residues. Energy. 2018;143:746–756. doi:10.1016/j.energy.2017.11.062.
   Web of Science ®Google Scholar
• Akhtar J, Amin NAS. A review on process conditions for optimum bio-oil yield in hydrothermal liquefaction of biomass. Renew Sustain Energy Rev. 2011;15(3):1615–1624. doi:10.1016/j.rser.2010.11.054.
   Web of Science ®Google Scholar
• Ibn Ferjani A, Jellali S, Limousy L, et al. The use of exhausted grape marc to produce biofuels and biofertilizers: effect of pyrolysis temperatures on biochars properties. Renew Sustain Energy Rev. 2019;107:425–433. doi: 10.1016/j.rser.2019.03.034.
   Web of Science ®Google Scholar
• Mazerolle D, Rezaei H, Bronson B, et al. Sieving and acid washing as a pretreatment to fast pyrolysis of a high ash hog fuel. Energy Fuels. 2019;33(6):5352–5359. doi:10.1021/acs.energyfuels.9b00694.
   Web of Science ®Google Scholar
• Chow LWLW, Tio SASASA, Teoh JYJYJY, et al. Sludge as a relinquishing catalyst in co-pyrolysis with palm empty fruit bunch fiber. J Anal Appl Pyrolysis. 2018;132:56–64. doi:10.1016/j.jaap.2018.03.015.
   Web of Science ®Google Scholar
• Bushra B, Remya N. Biochar from pyrolysis of rice husk biomass—characteristics, modification and environmental application. Biomass Convers Biorefinery. 2020. doi:10.1007/s13399-020-01092-3.
   Web of Science ®Google Scholar
• Muley PD, Henkel C, Abdollahi KK, et al. A critical comparison of pyrolysis of cellulose, lignin, and pine sawdust using an induction heating reactor. Energy Convers Manag. 2016;117:273–280. doi:10.1016/j.enconman.2016.03.041.
   Web of Science ®Google Scholar
• Chen D, Zhou J, Zhang Q, et al. Upgrading of rice husk by torrefaction and its influence on the fuel properties. BioResources. 2014;9:4. doi:10.15376/biores.9.4.5893-5905.
   Web of Science ®Google Scholar
• Liu Y, Yao S, Wang Y, et al. Bio- and hydrochars from rice straw and pig manure: inter-comparison. Bioresour Technol. 2017;235:332–337. doi:10.1016/j.biortech.2017.03.103.
   PubMed Web of Science ®Google Scholar
• McBeath AV, Wurster CM, Bird MI. Influence of feedstock properties and pyrolysis conditions on biochar carbon stability as determined by hydrogen pyrolysis. Biomass Bioenergy. 2015;73:155–173. doi:10.1016/j.biombioe.2014.12.022.
   Web of Science ®Google Scholar
• Hagemann N, Kammann CI, Schmidt H-P, et al. Nitrate capture and slow release in biochar amended compost and soil. PLoS One. 2017;12(2):e0171214. doi:10.1371/journal.pone.0171214.
   PubMed Web of Science ®Google Scholar
• Çakman G, Gheni S, Ceylan S. Prediction of higher heating value of biochars using proximate analysis by artificial neural network. Biomass Convers Biorefinery. 2021. doi:10.1007/s13399-021-01358-4.
   Web of Science ®Google Scholar
• Yang X, Wang H, Strong PJ, et al. Thermal properties of biochars derived from waste biomass generated by agricultural and forestry sectors. Energies. 2017;10(4):469. doi:10.3390/en10040469.
   Web of Science ®Google Scholar