Due to the energy crisis that some countries are facing nowadays, the gasification process appears to be a good alternative to produce some energy from solid materials. Increasingly, gasification involves using wastes as a solid fuel, making the process green and reusing some materials that otherwise could end up in a landfill. However, the process of finding the best gasification parameters of a sample can be very expensive and time-consuming. In this sense, a refuse-derived fuel (RDF) char produced from an original RDF under 30 min at 400 °C was tested on a small-scale reactor using macro thermogravimetric analysis (TGA), as presented in this paper. The goal was to study and evaluate the devolatilization and residual carbon rate of the sample under several conditions and, at the same time, quantify and analyze the released gas. In the first round of tests, 5, 10, and 20 g of samples were tested at 750 °C with an excess of air coefficient (λ) = 0 and 0.2. It was possible to conclude that the lower the mass, the higher the devolatilization rate. The λ only had an influence on the devolatilization rate with a 20 g sample. Regarding the gas, CO, CO₂, and H₂ had no variation in the sample mass in contrast to CH₄, which increased with the increase in the sample mass. The second round of tests was performed with samples of 10 g of mass at temperatures of 700, 800, and 900 °C and λ values of 0.15, 0.2, and 0.25. The tests indicated that the temperature influenced the devolatilization rate but not the residual carbon combustion rate. Regarding the gas composition, CH₄, CO₂, and CO followed the same trend, decreasing the concentration with the increase in temperature; in contrast, H₂ increased in concentration with an increase in temperature. The heating value of the gas followed the same behavior as CH₄.

The combustion of wood pellets in a fixed bed combustor of a 20 kW capacity domestic pellet boiler was tested according to several factors including Power, Excess Air (EA), Primary/Secondary air Split Ratio (SR) and Grate Area (GA). The Taguchi method was applied to program the experimental design. Several parameters were measured, including gas emissions (CO), fuel bed temperature (measured at 4 different heights), and efficiency. The experimental results show that the lower CO emission and the higher efficiency were obtained at medium thermal loads and the highest temperature on the fuel bed was obtained at about ¼ of its height (15 mm). The results obtained from the analysis of variance (ANOVA) show that the SR and the Power are the most important factors contributing to the CO reduction and also increase the fuel bed temperature.

The combustion of solid biomass in industrial boilers involves a sequence of processes that include heating, drying, devolatilization, and char conversion. To maintain a repeatable and fully controlled environment, and to monitor all the dynamics involved in the phenomena at a real scale, field-scale experiments become necessary to perform investigations. In this way, to evaluate different thermochemical conversion conditions of biomass particles under an oxidative atmosphere, and to quantify the emission of the main gas compounds continuously, a small-scale reactor was developed and presented in this paper. Hence, in this work, larger particles of eucalyptus are burned at 400 and 800 °C under different stoichiometric conditions to understand the differences between different biomass conversion regimes (gasification and combustion). The analysis of the mass loss at the different temperatures was characterized by only two different and consecutive stages for both thermochemical conditions. The first region does not present the influence on the air flow rate; however, there is a significant difference in the second region. This fact highlighted the importance of the diffusion of oxygen during the char conversion. Regarding the quantification of the gas compounds, an increase of around 3 times in the CO and CO₂ emissions when gasification occurs was observed at 400 °C. However, at 800 °C, the same trend was verified, also verifying a considerable amount of CH₄.

The combustion instability and ash agglomeration in a wood pellet boiler were investigated in this study. The tests were conducted using the Taguchi method of orthogonal array L₂₇(13³). Several parameters are applied, including grate area (GA), primary to secondary air split ratio (SR), excess air (EA), and fuel power (P). Pine wood pellets were used, and the boiler’s nominal load was 20 kW. The results show that instability during combustion occurs since the fuel bed rises as the accumulation of the unburned wood pellets on the grate causes a slow combustion rate and pressure drop, which creates noise and disturbances. A good combination of the parameters applied to TN9 and TN20 can be useful in obtaining stable combustion. In addition, the ash agglomerations were influenced by the duration of the combustion and the temperature of the fuel bed. The largest size of the ash agglomeration was referred to as test number-TN26 (P: 16 kW, EA: 110%, SR: 30/70, and GA: 115 mm × 75 mm), which is 59 mm, and the duration time is 14,400 s (≈4 h).

The lower heating value (LHV) is one of the most important fuel properties in thermochemical conversion systems, directly affecting energy balances, process efficiency, and fuel selection. Although several empirical correlations have been proposed in the literature, most existing models are limited to specific fuel classes or focused primarily on higher heating value (HHV) prediction. In this work, generalized regression models were developed to estimate the LHV of solid, liquid, and gaseous fuels from ultimate analysis data. A comprehensive database containing 520 fuels was used to construct both a full polynomial model and a simplified polynomial formulation. The predictive performance of the models was evaluated using multiple statistical metrics, while robustness and cross-validated predictive performance were assessed through K-fold cross-validation, residual analysis, Bland–Altman analysis, and variance inflation factor diagnostics (VIF). The full polynomial model achieved the highest fitting accuracy, whereas the simplified formulation demonstrated improved statistical stability and reduced multicollinearity with only minor reductions in predictive performance. Compared with representative literature correlations, the proposed models showed competitive or superior predictive capability, demonstrating their applicability as practical preliminary correlations for practical engineering and energy-related applications.