Diverse methodologies exist to determine the chemical composition, proximate analysis, and calorific value of biomass. Researchers select and apply a specific methodology according to the lignocellulosic material they study and the budgetary resources available. In this project, we determined the primary chemical constitution and proximate analysis of Prosopis laevigata (Humb. & Bonpl.) Jonhst wood using a traditional chemical method and a novel procedure based on the deconvolution of the DTG signal produced by TGA. The highest calorific value was verified using a calorimetric pump based on mathematical models. We also conducted elemental analysis and a microanalysis of ash, and applied Fourier transform infrared spectroscopic analysis (FT-IR). The means of the results obtained by the chemical method and TGA-DTG, respectively, were: hemicelluloses 7.36%–(8.72%), cellulose 48.28%–(46.08%), lignin 30.57%–(32.44%), extractables 13.53%–(12.72%), moisture 2.03%–(4.96%), ash 1.77%–(1.90%), volatile matter 75.16%–(74.14%), and fixed carbon 23.05%–(18.93%). The procedure with the calorimetric pump generated a calorific value above 20.16 MJ/kg. The range generated by the various models was 18.23–21.07 MJ/kg. The results of the elemental analysis were: carbon 46.4%, hydrogen 6.79%, oxygen 46.43%, nitrogen 0.3%, and sulfur 0.5%. The microanalysis of ash identified 18 elements. The most abundant ones were potassium ˃ calcium ˃ sodium. Based on the infrared spectrum (FT-IR) of Prosopis laevigata wood, we detected the following functional groups: OH, C-H, C=O, CH_2, CH₃, C-O-C, C-OH, and C4-OH. Our conclusion is that the TGA-DTG method made it possible to obtain results in less time with no need for the numerous reagents that chemical procedures require. The calorific value of P. laevigata wood is higher than the standards. Finally, according to our results, proximate analysis provides the best model for calculating calorific value.

The standard methods for determining the basic chemical composition of wood are well-established, but include processes that demand a great deal of time and diverse chemical reagents. TGA and DTG analyses, in contrast, offer precise results in less time. This study was designed to identify the primary components and results of the proximal analysis of wood from three species –Acacia farnesiana, A. pennatula and Albizia plurijuga—using TGA with deconvolution of the DTG curve and a chemical method. Higher heating value (HHV) was determined using a bomb calorimeter and mathematical models. Elemental organic and inorganic analyses were conducted. No statistically significant differences appeared in the results of the TGA-DTG and chemical methods for the wood in terms of cellulose, lignin, and volatile material content. Results were especially accurate in the samples of A. pennatula and A. plurijuga for hemicelluloses, extractives, and moisture. Regarding HHV, the wood of A. plurijuga showed no statistically significant differences between the bomb calorimeter test, calculations as a function of chemical composition, or the proximal analysis. Elemental organic results were C = 43.76–46.65%; H = 6.70–6.95%; O = 46.06–48.95%; N = 0.21–0.42%; and S = 0.06–0.11%. For the inorganic fraction we identified 18 elements in the ash. We conclude that the TGA-DTG method made it possible to obtain results in a short time with no need for the numerous reagents that chemical processes require. Findings suggest that in the absence of a bomb calorimeter, the best model for calculating HHV is proximal analysis.