Feedstock properties play a crucial role in thermal conversion processes, where understanding the influence of these properties on treatment performance is essential for optimizing both feedstock selection and the overall process. In this study, a series of van Krevelen diagrams were generated to illustrate the impact of H/C and O/C ratios of feedstock on the products obtained from six commonly used thermal conversion techniques: torrefaction, hydrothermal carbonization, hydrothermal liquefaction, hydrothermal gasification, pyrolysis, and gasification. Machine learning methods were employed, utilizing data, methods, and results from corresponding studies in this field. Furthermore, the reliability of the constructed van Krevelen diagrams was analyzed to assess their dependability. The van Krevelen diagrams developed in this work systematically provide visual representations of the relationships between feedstock and products in thermal conversion processes, thereby aiding in optimizing the selection of feedstock and the choice of thermal conversion technique.

Department of Chemical and Biomolecular Engineering National University of Singapore 4 Engineering Drive 4 Singapore 117585 Singapore

Department of Chemical Engineering University of South Carolina 301 Main St Columbia SC 29208 USA

Department of Energy Conversion Engineering Wroclaw University of Science and Technology 27 wybrzeże Stanisława Wyspiańskiego st 50 370 Wroclaw Poland

Department of Industry and Energy CIRCE Research Centre for Energy Resources and Consumption 50018 Zaragoza Spain

Department of Materials Science and Engineering KTH Royal Institute of Technology SE 100 44 Stockholm Sweden

Department of Mechanical Engineering Chiang Mai University 239 Huay Kaew Rd Muang District Chiang Mai 50200 Thailand

Energy Research Centre Centre for Energy and Environmental Technologies VŠB Technical University of Ostrava 708 00 Ostrava Poruba Czech Republic

Faculty of Creative Arts University of Malaya 50603 Kuala Lumpur Malaysia

Institut de Mécanique des Fluides de Toulouse Université de Toulouse CNRS INPT UPS 31400 Toulouse France

Jiangsu Co Innovation Center for Efficient Processing and Utilization of Forest Resources College of Chemical Engineering Nanjing Forestry University Longpan Road 159 210037 Nanjing China

Jiangsu Province Key Laboratory of Biomass Energy and Materials National Engineering Laboratory for Biomass Chemical Utilization Institute of Chemical Industry of Forest Products Chinese Academy of Forestry 210042 Nanjing China

Laboratory of Environment Enhancing Energy Key Laboratory of Agricultural Engineering in Structure and Environment of Ministry of Agriculture and Rural Affairs China Agricultural University 100083 Beijing China

School of Energy Science and Engineering Harbin Institute of Technology 150001 Harbin China

See more in PubMed

Akhtar A, Krepl V, Ivanova T. A combined overview of combustion, pyrolysis, and gasification of biomass. Energy Fuels. 2018;32:7294–7318.

Gao N, Kamran K, Quan C, Williams PT. Thermochemical conversion of sewage sludge: a critical review. Prog. Energy Combust. Sci. 2020;79:100843.

Lee J, Kim S, You S, Park Y-K. Bioenergy generation from thermochemical conversion of lignocellulosic biomass-based integrated renewable energy systems. Renew. Sustain. Energy Rev. 2023;178:113240.

Asghar U, et al. Review on the progress in emission control technologies for the abatement of CO2, SOx and NOx from fuel combustion. J. Environ. Chem. Eng. 2021;9:106064.

Goyal H, Seal D, Saxena R. Bio-fuels from thermochemical conversion of renewable resources: a review. Renew. Sustain. Energy Rev. 2008;12:504–517.

Kumar G, et al. A review of thermochemical conversion of microalgal biomass for biofuels: chemistry and processes. Green Chem. 2017;19:44–67.

Siwal SS, et al. Advanced thermochemical conversion technologies used for energy generation: advancement and prospects. Fuel. 2022;321:124107.

Van Krevelen D. Graphical-statistical method for the study of structure and reaction processes of coal. Fuel. 1950;29:269–284.

Laszakovits, J. R. & MacKay, A. A. Data-based chemical class regions for Van Krevelen diagrams. J. Am. Soc. Mass Spectrom.33, 198–202 (2021). PubMed

Prins MJ, Ptasinski KJ, Janssen FJ. From coal to biomass gasification: comparison of thermodynamic efficiency. Energy. 2007;32:1248–1259.

Rivas-Ubach A, et al. Moving beyond the van Krevelen diagram: a new stoichiometric approach for compound classification in organisms. Anal. Chem. 2018;90:6152–6160. PubMed

Danger G, et al. Exploring the link between molecular cloud ices and chondritic organic matter in laboratory. Nat. Commun. 2021;12:3538. PubMed PMC

McDonough LK, et al. A new conceptual framework for the transformation of groundwater dissolved organic matter. Nat. Commun. 2022;13:2153. PubMed PMC

Yu S, et al. Decoupled temperature and pressure hydrothermal synthesis of carbon sub-micron spheres from cellulose. Nat. Commun. 2022;13:3616. PubMed PMC

Chen H, et al. Mesophilic and thermophilic anaerobic digestion of aqueous phase generated from hydrothermal liquefaction of cornstalk: molecular and metabolic insights. Water Res. 2020;168:115199. PubMed

Qambrani NA, Rahman MM, Won S, Shim S, Ra C. Biochar properties and eco-friendly applications for climate change mitigation, waste management, and wastewater treatment: a review. Renew. Sustain. Energy Rev. 2017;79:255–273.

Herzsprung P, et al. Improved understanding of dissolved organic matter processing in freshwater using complementary experimental and machine learning approaches. Environ. Sci. Technol. 2020;54:13556–13565. PubMed

Martins N, et al. Revisiting 3D van Krevelen diagrams as a tool for the visualization of volatile profile of varietal olive oils from Alentejo region, Portugal. Talanta. 2020;207:120276. PubMed

Ollivier S, et al. New insights into the Van Krevelen diagram: automated molecular formula determination from HRMS for a large chemical profiling of lichen extracts. Phytochem. Anal. 2022;33:1111–1120. PubMed PMC

Chew JJ, Doshi V. Recent advances in biomass pretreatment—torrefaction fundamentals and technology. Renew. Sustain. Energy Rev. 2011;15:4212–4222.

Weiner B, Poerschmann J, Wedwitschka H, Koehler R, Kopinke F-D. Influence of process water reuse on the hydrothermal carbonization of paper. ACS Sustain. Chem. Eng. 2014;2:2165–2171.

Abomohra AE-F, Sheikh HM, El-Naggar AH, Wang Q. Microwave vacuum co-pyrolysis of waste plastic and seaweeds for enhanced crude bio-oil recovery: experimental and feasibility study towards industrialization. Renew. Sustain. Energy Rev. 2021;149:111335.

Hungsberg, M. et al. Thermodynamic equilibrium investigation to operational capabilities and process tolerance of plasma gasification for various feedstock. Chem. Eng. Sci.250, 117401 (2022).

Kim S, Kramer RW, Hatcher PG. Graphical method for analysis of ultrahigh-resolution broadband mass spectra of natural organic matter, the van Krevelen diagram. Anal. Chem. 2003;75:5336–5344. PubMed

Wu Z, Rodgers RP, Marshall AG. Two-and three-dimensional van Krevelen diagrams: a graphical analysis complementary to the Kendrick mass plot for sorting elemental compositions of complex organic mixtures based on ultrahigh-resolution broadband Fourier transform ion cyclotron resonance mass measurements. Anal. Chem. 2004;76:2511–2516. PubMed

Lozano DCP, Jones HE, Reina TR, Volpe R, Barrow MP. Unlocking the potential of biofuels via reaction pathways in van Krevelen diagrams. Green Chem. 2021;23:8949–8963.

Herzsprung P, et al. Variations of DOM quality in inflows of a drinking water reservoir: linking of van Krevelen diagrams with EEMF spectra by rank correlation. Environ. Sci. Technol. 2012;46:5511–5518. PubMed

Qiu Y, et al. Effects of cellulose-lignin interaction on the evolution of biomass pyrolysis bio-oil heavy components. Fuel. 2022;323:124413.

Akhtar A, Jiříček I, Ivanova T, Mehrabadi A, Krepl V. Carbon conversion and stabilisation of date palm and high rate algal pond (microalgae) biomass through slow pyrolysis. Int. J. Energy Res. 2019;43:4403–4416.

Yang K, et al. Secondary reactions of primary tar from biomass pyrolysis: characterization of heavy products by FT-ICR MS. Energy Fuels. 2021;35:13191–13199.

Wang S, et al. Effect of hydrothermal carbonization pretreatment on the pyrolysis behavior of the digestate of agricultural waste: a view on kinetics and thermodynamics. Chem. Eng. J. 2022;431:133881.

Jordan MI, Mitchell TM. Machine learning: trends, perspectives, and prospects. Science. 2015;349:255–260. PubMed

Naqvi SR, et al. Applications of machine learning in thermochemical conversion of biomass—a review. Fuel. 2023;332:126055.

Seo MW, et al. Recent advances of thermochemical conversion processes for biorefinery. Bioresour. Technol. 2022;343:126109. PubMed

Greenwell BM. pdp: an R Package for constructing partial dependence plots. R. J. 2017;9:421.

Zhang T, et al. Machine learning prediction of bio-oil characteristics quantitatively relating to biomass compositions and pyrolysis conditions. Fuel. 2022;312:122812.

Chen W-H, et al. Progress in biomass torrefaction: principles, applications and challenges. Prog. Energy Combust. Sci. 2021;82:100887.

Du S-W, Chen W-H, Lucas JA. Pretreatment of biomass by torrefaction and carbonization for coal blend used in pulverized coal injection. Bioresour. Technol. 2014;161:333–339. PubMed

Thengane SK, Kung KS, Gomez-Barea A, Ghoniem AF. Advances in biomass torrefaction: parameters, models, reactors, applications, deployment, and market. Prog. Energy Combust. Sci. 2022;93:101040.

Martínez MG, et al. Torrefaction of cellulose, hemicelluloses and lignin extracted from woody and agricultural biomass in TGA-GC/MS: linking production profiles of volatile species to biomass type and macromolecular composition. Ind. Crops Prod. 2022;176:114350.

Zheng A, et al. Impact of torrefaction on the chemical structure and catalytic fast pyrolysis behavior of hemicellulose, lignin, and cellulose. Energy Fuels. 2015;29:8027–8034.

Chen D, et al. Investigation of biomass torrefaction based on three major components: hemicellulose, cellulose, and lignin. Energy Convers. Manag. 2018;169:228–237.

Kruse A, Funke A, Titirici M-M. Hydrothermal conversion of biomass to fuels and energetic materials. Curr. Opin. Chem. Biol. 2013;17:515–521. PubMed

Sharma HB, Panigrahi S, Dubey BK. Food waste hydrothermal carbonization: study on the effects of reaction severities, pelletization and framework development using approaches of the circular economy. Bioresour. Technol. 2021;333:125187. PubMed

Wang L, et al. Hydrothermal treatment of food waste for bio-fertilizer production: formation and regulation of humus substances in hydrochar. Sci. Total Environ. 2022;838:155900. PubMed

Djandja OS, Yin L-X, Wang Z-C, Duan P-G. From wastewater treatment to resources recovery through hydrothermal treatments of municipal sewage sludge: a critical review. Process Saf. Environ. Prot. 2021;151:101–127.

Inoue S, Sawayama S, Ogi T, Yokoyama S-Y. Organic composition of liquidized sewage sludge. Biomass Bioenergy. 1996;10:37–40.

Sheng G-P, Yu H-Q, Li X-Y. Extracellular polymeric substances (EPS) of microbial aggregates in biological wastewater treatment systems: a review. Biotechnol. Adv. 2010;28:882–894. PubMed

Channiwala S, Parikh P. A unified correlation for estimating HHV of solid, liquid and gaseous fuels. Fuel. 2002;81:1051–1063.

Wang S, et al. A machine learning model to predict the pyrolytic kinetics of different types of feedstocks. Energy Convers. Manag. 2022;260:115613.

Kang S, Li X, Fan J, Chang J. Characterization of hydrochars produced by hydrothermal carbonization of lignin, cellulose, D-xylose, and wood meal. Ind. Eng. Chem. Res. 2012;51:9023–9031.

Xu Y, et al. Understand the antibacterial behavior and mechanism of hydrothermal wastewater. Water Res. 2022;226:119318. PubMed

Chen H, et al. Biogas production from hydrothermal liquefaction wastewater (HTLWW): focusing on the microbial communities as revealed by high-throughput sequencing of full-length 16S rRNA genes. Water Res. 2016;106:98–107. PubMed

Fernandez S, Srinivas K, Schmidt AJ, Swita MS, Ahring BK. Anaerobic digestion of organic fraction from hydrothermal liquefied algae wastewater byproduct. Bioresour. Technol. 2018;247:250–258. PubMed

Li J, et al. Machine learning aided bio-oil production with high energy recovery and low nitrogen content from hydrothermal liquefaction of biomass with experiment verification. Chem. Eng. J. 2021;425:130649.

Lu J, Watson J, Liu Z, Wu Y. Elemental migration and transformation during hydrothermal liquefaction of biomass. J. Hazard. Mater. 2022;423:126961. PubMed

Lu J, Liu Z, Zhang Y, Savage PE. Synergistic and antagonistic interactions during hydrothermal liquefaction of soybean oil, soy protein, cellulose, xylose, and lignin. ACS Sustain. Chem. Eng. 2018;6:14501–14509.

Xu Y, Lu J, Wang Y, Yuan C, Liu Z. Construct a novel anti-bacteria pool from hydrothermal liquefaction aqueous family. J. Hazard. Mater. 2022;423:127162. PubMed

Chen W-T, et al. Co-liquefaction of swine manure and mixed-culture algal biomass from a wastewater treatment system to produce bio-crude oil. Appl. Energy. 2014;128:209–216. PubMed

Hao WQ, Liu XJ. Molecular dynamics investigation on the co-gasification of various components of sewage sludge in supercritical water. Fuel. 2023;334:126729.

Okolie JA, et al. Modeling and process optimization of hydrothermal gasification for hydrogen production: a comprehensive review. J. Supercrit. Fluids. 2021;173:105199.

De Caprariis B, De Filippis P, Petrullo A, Scarsella M. Hydrothermal liquefaction of biomass: influence of temperature and biomass composition on the bio-oil production. Fuel. 2017;208:618–625.

Rutkowski P. Pyrolysis of cellulose, xylan and lignin with the K2CO3 and ZnCl2 addition for bio-oil production. Fuel Process. Technol. 2011;92:517–522.

Fahmi R, Bridgwater AV, Donnison I, Yates N, Jones J. The effect of lignin and inorganic species in biomass on pyrolysis oil yields, quality and stability. Fuel. 2008;87:1230–1240.

Kan T, Strezov V, Evans TJ. Lignocellulosic biomass pyrolysis: a review of product properties and effects of pyrolysis parameters. Renew. Sustain. Energy Rev. 2016;57:1126–1140.

Yu D, Hui H, Ding G, Dong N, Li S. Enhancement of aromatics production from catalytic co-pyrolysis of walnut shell and LDPE via a two-step approach. J. Anal. Appl. Pyrolysis. 2021;157:105216.

Arena U, Zaccariello L, Mastellone ML. Fluidized bed gasification of waste-derived fuels. Waste Manag. 2010;30:1212–1219. PubMed

Wilk V, Hofbauer H. Co-gasification of plastics and biomass in a dual fluidized-bed steam gasifier: possible interactions of fuels. Energy Fuels. 2013;27:3261–3273.

Zaini IN, et al. Production of H2-rich syngas from excavated landfill waste through steam co-gasification with biochar. Energy. 2020;207:118208.

Onsree T, Tippayawong N. Machine learning application to predict yields of solid products from biomass torrefaction. Renew. Energy. 2021;167:425–432.

Li J, et al. Multi-task prediction and optimization of hydrochar properties from high-moisture municipal solid waste: application of machine learning on waste-to-resource. J. Clean. Prod. 2021;278:123928.

Liu S, et al. Predicting gas production by supercritical water gasification of coal using machine learning. Fuel. 2022;329:125478.

Li Y, Gupta R, You S. Machine learning assisted prediction of biochar yield and composition via pyrolysis of biomass. Bioresour. Technol. 2022;359:127511. PubMed

Tang Q, et al. Machine learning prediction of pyrolytic gas yield and compositions with feature reduction methods: effects of pyrolysis conditions and biomass characteristics. Bioresour. Technol. 2021;339:125581. PubMed

Serrano D, Golpour I, Sánchez-Delgado S. Predicting the effect of bed materials in bubbling fluidized bed gasification using artificial neural networks (ANNs) modeling approach. Fuel. 2020;266:117021.