Solid particle emissions from burning wood in three internal combustion biomass cooking stoves commonly used in southern Chile were compared. Each stove was used to show differences in sealing systems, combustion chamber shape, and heating surfaces in order to optimize biomass combustion and the energy produced at a low manufacturing cost. The influence of cooking stove design along with particle and gas emissions that resulted from the biomass combustion within the cooking stove was investigated in this study. Levels of diverse atmospheric contaminants, such as particulate matter, emission factor, NO_x, CO₂, and CO, and the temperature of the flue gases were determined with the Ch-28 method and UNE-EN 12815. The average emission of particulate matter was significantly reduced by modifying the geometry of the combustion chamber and heating surface of each stove, resulting in 5 g/h particle emissions in conventional equipment and 2 g/h in the improved equipment. In relation to gas emissions, there was a 25% maximum decrease in NO_x gases and 35% in CO after modifying the heating surface of each stove. This background supports the evidence of technological improvement with high environmental impact and low economic cost for local manufacturers.

This study investigates the limitations of localized heating in a single-story dwelling, using a validated computational fluid dynamics (CFD) model to analyze thermal stratification and its impact on occupant comfort. A comparative evaluation of turbulence models (k-ε and k-ω SST) and equations of state (Soave–Redlich–Kwong and Peng–Robinson) identified the k-ω SST model with the Soave–Redlich–Kwong equation as the most accurate and computationally efficient combination for capturing temperature gradients and achieving rapid convergence. Experimental validation demonstrated strong agreement between simulated and measured temperature profiles, confirming the model’s reliability. The results highlight a fundamental trade-off between localized thermal comfort and overall indoor temperature uniformity in conventionally heated spaces. While localized heating enhances comfort near the heat source, it generates vertical temperature disparities exceeding acceptable comfort thresholds at greater distances. Specifically, at 3 m from the heat source, the temperature difference between ankle and head height reached 6 °C, surpassing the 4 °C limit recommended by ASHRAE-55 for standing occupants. These findings underscore the need for alternative heating solutions that prioritize uniform heat distribution, energy efficiency, and optimized ventilation to improve indoor thermal comfort in residential buildings. This study provides critical insights to help develop and implement sustainable heating strategies and the design of energy-efficient dwellings.

This manuscript is related to a formulation for modelling cellulose pyrolysis with a pseudo-equilibrium approach. The objective is to model the kinetics of the cellulose pyrolysis with a semi-global mechanism obtained from the literature in order to obtain the yield and the rate of formation, mainly that of char. The pseudo-equilibrium approach consists of the assumption that the solid phase devolatilisation can be described kinetically—at a finite rate—thus preserving the competitive characteristic between the production of char and tar, while the gas phase can be described directly by means of chemical equilibrium. The aforementioned approach gives a set of ordinary, linear, and nonlinear differential equations that are solved numerically with a consistent numerical scheme (i.e., the Totally Implicit Euler method). Chemical equilibrium was solved using CANTERA coupled with a code written in MATLAB. The results showed that the scheme preserved the tar-gas competitive characteristic for cellulose pyrolysis. The gas phase was defined as a mixture of CO₂, CO, H₂O, CH₄, H₂, and N₂, showing a similar composition compared to models from the literature. Finally, the extension of the model to biomass in general is straightforward for including hemicellulose and lignin. The formulation is described in detail throughout the document in order to be replicated and evaluated for other biological components.

Accurately predicting pollutant emission factors (EFs) from woody biomass fuels remains challenging because small-scale combustion tests are fuel-specific, time-consuming, and highly sensitive to operating conditions. This study combines controlled laboratory combustion experiments with a physics-informed artificial neural network (ANN–PINN) to estimate the emission factors of particulate matter (${\mathrm{EF}}_{PM}$), carbon monoxide (${\mathrm{EF}}_{CO}$), and nitrogen oxides (${\mathrm{EF}}_{NOx}$) using only laboratory-scale fuel characterization. Three pelletized woody biomass, Pinus radiata, Acacia dealbata, and Nothofagus obliqua, were analyzed through ultimate and proximate composition, lignin content, and TGA-derived parameters and tested in a residential pellet stove under identical control setpoints, resulting in a narrow and well-defined operating regime. A medium-depth ANN–PINN was constructed by integrating mechanistic constraints, monotonicity based on known emission trends and a weak carbon balance penalty, into a feed-forward neural network trained and evaluated using Leave-One-Out Cross-Validation. The model accurately reproduced the experimental behavior of ${\mathrm{EF}}_{CO}$ and captured structured variability in ${\mathrm{EF}}_{PM}$, while the limited nitrogen variability of the fuels restricted generalization for ${\mathrm{EF}}_{NOx}$. Sensitivities derived via automatic differentiation revealed physically coherent relationships, demonstrating that PM emissions depend jointly on fuel chemistry and aero-thermal conditions, CO emissions are dominated by mixing and temperature, and ${\mathrm{NO}}_x$ formation is primarily governed by fuel-bound nitrogen. When applied to external biomass fuels characterized independently in the literature, the ANN–PINN produced physically plausible predictions, highlighting its potential as a rapid, low-cost screening tool for assessing new biomass feedstocks and supporting cleaner residential heating technologies. The integrated experimental–PINN framework provides a physically consistent and data-efficient alternative to classical empirical correlations and purely data-driven ANN models.

The global energy crisis drives the search for sustainable biomass resources. Microalgae, particularly Chlorella vulgaris, represent a promising third-generation feedstock for bi-ochar and biofuels. However, detailed kinetic schemes for its slow devolatilization are still scarce. This work compares the thermogravimetric behavior of commercially Chlorella vulgaris with data reported in the literature under identical experimental conditions and develops a multi-stage kinetic scheme using model-free methods and simultaneous global optimization. A complete set of kinetic parameter is provided in conjunction with a mass wights in order to close the reaction scheme. Biological composition of microalgae was experimentally determined resulting in 21.20, 59.30 and 19.50% for carbohydrates, protein and lipids. Thermogravimetric (TG/DTG) analyses were conducted with 5, 10 and 20 °C/min heating rates. Activation energy distribution was obtained through isoconversional model-free methods (Fried-man, FWO, KAS and Starink). A parallel multi-stage kinetic model was subsequently optimized globally against the experimental data to determine the complete kinetic tri-plet (E, A, n). TG/DTG profiles exhibited in general a good agreement with the literature refer-ence in the number and the temperature of features, peaks and shoulders, however different in intensity probably due to the different amount of biological components, carbohydrates, proteins and lipids. The multi-stage model achieved excellent fitting quality accounting for 5 reactions. Activation energies for the principal devolatilization stages ranged from 140 to 220 kJ/mol, while ln(A) values lay between 20 and 35 s⁻¹. The findings an results provided by this study is considered useful for the community con-tributing with discussion and a robust kinetic scheme suitable for example for slow pyrolysis process simulation.

This works is related to the modeling of cellulose pyrolysis with a pseudo equilibrium approach. The objective is to model the kinetics of the cellulose pyrolysis with a semi-global mechanism obtained from the literature, in order to obtain the yield and the rate of formation of char. The pseudo equilibrium sense consists in the supposition that the solid phase devolatilization can be described kinetically - at finite rate - , preserving the competitive characteristic between the production of char and tar, while the gas phase can be described by means of chemical equilibrium. A set of ordinary, linear and non linear, differential equations was obtained and solved numerically with a simple but consistent scheme using the Totally Implicit Euler method. Chemical equilibrium was solved using CANTERA coupled with a code written in Matlab. Results showed that the scheme preserve the tar-gas competitive characteristic for cellulose pyrolysis. The gas phase, is defined as a mixture of CO₂, CO, H₂O, CH₄, H₂ and N₂ showed similarly composition compared with models from literature. Finally, the extension of the model to biomass in general, is straightforward, in order to include the hemicellulose and lignin.