journal article·2025-01-02Open access

Characterization of Congolese Woody Biomass and Its Potential as a Bioenergy Source

Maryse D. Nkoua Ngavouka, Tania S. Mayala, Dick H. Douma, Aaron E. Brown, James M. Hammerton, Andrew B. Ross, Gilbert Nsongola, Bernard M’Passi-Mabiala, Jon C. Lovett

Applied Sciences· Volume 15· Issue 1

Abstract

This study assesses and characterizes six woody biomass (WB) species commonly harvested in the Republic of Congo: Millettia laurentii (WB1), Millettia eetveldeana (WB2), Hymenocardia ulmoides (WB3), Markhamia tomentosa (WB4), Pentaclethra eetveldeana (WB5), and Hymenocardia acida (WB6). Characterization was performed using proximate analysis with a Thermo Gravimetric Analyser (TGA), ultimate analysis with a CHNS Analyser, higher heating value (HHV) determination, metal content analysis by X-ray fluorescence (XRF), and aboveground biomass (AGB) estimation. The proximate analysis results showed that volatile matter varied between 74.6% and 77.3%, while the ultimate analysis indicated that carbon content ranged from 43% to 46%, with low nitrogen content. XRF analysis revealed low levels of heavy metals in all samples. The HHV results, using three models (Dulong’s equation, Friedl, and proximate analysis), showed higher values with Friedl’s method (17.3–18.2 MJ/kg) and proximate analysis (15.26–19.23 MJ/kg) compared to Dulong’s equation (13.9–14.9 MJ/kg). Savannah biomass (WB6) exhibited high AGB (7.28 t), 14.55 t/ha, and carbon stock (7.28 t). Compared to forest biomass, savannah biomass presents a higher potential for bioenergy production. Minimal statistical analysis of wood biomass showed that parameters such as volatile matter (VM), carbon (C), hydrogen (H), and calculated HHV have low variability, suggesting the biomass is relatively homogeneous. However, moisture and nitrogen showed significant standard deviations, indicating variability in storage conditions or sample nature. Statistical analysis of forest biomass estimation revealed different mean values for diameter, AGB (t and t/ha), and carbon stock, with high standard deviations, indicating a heterogeneous forest with both young and mature trees. These analyses and estimates indicate that these WB species are suitable for biofuel and bioenergy production using gasification, pyrolysis, and combustion processes. Among these thermochemical processes, gasification is the most efficient compared to combustion and pyrolysis.
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Research context

OpenAlex topics: Forest Biomass Utilization and Management · Bioenergy crop production and management · Environmental Impact and Sustainability

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Faculté des Sciences et Techniques, Université Marien Ngouabi, Brazzaville BP.69, Congo

Institut National de Recherche en Sciences Exactes et Naturelles, Brazzaville BP.2400, Congo

School of Chemical and Process Engineering, University of Leeds, Leeds LS2 9JT, UK

School of Geography, University of Leeds, Leeds LS2 9JT, UK

Funding

Royal Society-DFID Africa Capacity Building Initiative grant · UNESCO and the International Development Research Centre · CRESUM HYRES: Creating resilient sustainable microgrids through hybrid renewable energy systems · BEFWAM: Bioenergy, fertiliser and clean water from invasive aquatic macrophytes

References (34)

Show source-provided references
1. A Review of the Production and Upgrading of Biofuel; Raw Materials, Processes and Products
2. Biomass residue characterization for their potential application as biofuels10.1007/s10973-018-7560-9 ↗
3. Forestry biomass in a bioenergy perspective
4. Lignocellulosic Residues: Biodegradation and Bioconversion by Fungi10.1016/j.biotechadv.2008.11.001 ↗
5. Cell-Wall Carbohydrates and their Modification as a Resource for Biofuels10.1111/j.1365-313X.2008.03463.x ↗
6. Ouissika, B.C., and Milandou, C.S. (2019, January 13–15). Cartographie du couvert forestier et des changements en République du Congo. Proceedings of the Conférence OSFACO: Des Images Satellites Pour la Gestion Durable des Territoires en Afrique, Cotonou, Benin.
7. Predictions of elemental composition of coal and biomass from their proximate analyses using ANFIS, ANN and MLR10.1007/s40789-020-00346-9 ↗
8. Solar Biomass Pyrolysis with the Linear Mirror II10.4236/sgre.2015.67016 ↗
9. Review of Physicochemical Properties and Analytical Characterization of Lignocellulosic Biomass10.1016/j.rser.2017.03.072 ↗
10. The Physicochemical Properties and Composition of Biomass Ash and Evaluating Directions of its Applications10.15244/pjoes/80870 ↗
11. Chemical composition of lignocellulosic biomass in the wood of Abies religiosa across an altitudinal gradien10.1007/s10086-016-1585-0 ↗
12. Microalgae for biodiesel production and other applications: A review10.1016/j.rser.2009.07.020 ↗
13. Tree allometry and improved estimation of carbon stocks and balance in tropical forests10.1007/s00442-005-0100-x ↗
14. Characterisation of Congolese Aquatic Biomass and Their Potential as a Source of Bioenergy10.3390/biomass2010001 ↗
15. Solar-driven pyrolysis and gasification of low-grade carbonaceous materials10.1016/j.ijhydene.2017.02.026 ↗
16. Hasan, M., Haseli, Y., and Karadogan, E. (2018). Correlation to Predict Elemental compositions and Heating Value of Torrefied Biomass. Energies, 11.10.3390/en11092443 ↗
17. Predicting heating value of lignocellulosic biomass based on elemental analysis10.1016/j.energy.2019.116501 ↗
18. Kwaghger, A., Enyejoh, L.A., and Iortyer, H.A. (2017). The development of equations for estimating high heating values from proximate and ultimate analysis for some selected indigenous fuel woods. Eur. J. Eng. Technol., 5.
19. Fiber and Lignin Analysis in Concentrate, Forage, and Feces: Detergent Versus Enzymatic-Chemical Method10.3168/jds.S0022-0302(06)72287-1 ↗
20. Weldenson, D. (2010). Evaluation de la Biomasse et des Stocks de Carbone sur des Placettes Forestières en Forêts Tropicales Humides de Guadeloupe, Mémoire DEA; Université des Antilles et de la Guyane.
21. Lignin Biosynthesis and Structure, American society of plant biologists10.1104/pp.110.155119 ↗
22. McKendry, P. (2022). Energy production from biomass: Overview of biomass. Bioresour. Technol., 83.
23. Estimation of higher heating value of biomass from proximate analysis: A new approach10.1016/j.fuel.2012.04.015 ↗
24. Williams, C.L., Rachel, M.E., and Jaya, S.T. (2017). Biomass compositional analysis for conversion to renewable fuels and chemicals. Biomass Vol. Estim. Valorization Energy, 251–270.10.5772/65777 ↗
25. A Heating value estimation of refuse derived fuel using the genetic programming model10.1016/j.wasman.2019.09.035 ↗
26. Michalak, I., Baśladyńska, S., Mokrzycki, J., and Rutkowski, P. (2019). Biochar from Macroalga as A Potential Biosorbent for Wastewater Treatment. Water, 11.10.3390/w11071390 ↗
27. Álvarez-Álvarez, P., Pizarro, C., Barrio-Anta, M., Cámara-Obregón, A., Bueno, J.L.M., Álvarez, A., Gutiérrez, I., and Burslem, D.F. (2018). Evaluation of Tree Species for Biomass Energy Production in Nortnwest Spain. Forests, 9.10.3390/f9040160 ↗
28. Characterization of solar fuels obtained from beech wood solar pyrolysis10.1016/j.fuel.2016.10.036 ↗
29. Influence of moisture contents on the fast pyrolysis of Trommel fines in a Bubbling Fluidized Bed Reactor10.1007/s12649-018-00560-2 ↗
30. Brandić, I. (2024). Development of New Nonlinear Mathematical Models for Modelling the Higher Heating Value of Biomass. [Doctoral Thesis, University of Zagreb, Faculty of Agriculture].
31. Sustainable Forest biomass: A review of current residue harvesting guidelines10.1186/s13705-021-00281-w ↗
32. Woody biomass energy potential in 205010.1016/j.enpol.2013.11.033 ↗
33. The role of woody biomass for reduction of fossil GHG emissions in the future North European energy sector10.1016/j.apenergy.2020.115360 ↗
34. (2024, December 26). Available online: https://www.donneesmondiales.com/afrique/congo-brazzaville/bilan-energetique.php.