Investigations were performed in relation to the thermal gasification of wood granulate using steam in an allothermal reactor with electric heaters. They studied the impact of the temperature inside the reactor and the steam flow rate on the percentage shares of H₂, CH₄, CO, and CO₂ in synthesis gas and on the calorific value of syngas. The tests were conducted at temperatures inside the reactor equal to 750, 800, and 850 °C and with a steam flow rate equal to 10.0, 15.0, and 20.0 kg∙h⁻¹. The intensity of gasified biomass was 20 kg∙h⁻¹. A significant impact of the temperature on the percentages of all the components of synthesis gas and a significant impact of the steam flow rate on the content of hydrogen and carbon dioxide in syngas were found. The highest percentage of hydrogen obtained was 43.3%. The calorific value of the gas depended significantly on the temperature inside the reactor and the correlation between the temperature and the steam flow rate. Its maximum value was 13.3 MJ∙m⁻³ at 800 °C. This paper also includes an assessment of the mutual correlations of the percentage shares of the individual synthesis gas components.

The development of bioink-based 3D-printed scaffolds has revolutionized bone tissue engineering (BTE) by enabling patient-specific and biomimetic constructs for bone regeneration.

Biochar (BC) and hydrochar (HC) have attracted considerable attention owing to their versatile characteristics and proven effectiveness in diverse technical fields. Solid BC is generated as a result of the dry carbonisation process of pyrolysis, in contrast to the slurry HC, which is produced during the hydrothermal carbonisation process. In this study, we evaluated the adsorption potential of two hydrochar samples (HCs) and three biochar samples (BCs) produced from sugar cane bagasse. The adsorption capacity of these samples was tested for ammonium, nitrate, and phosphate ions under various conditions. The BCs and HCs were subjected to characterisation using a CHNS/O analyser, the zeta potential, and Fourier transform infrared (FTIR). Elevating the pyrolysis temperature of the biochar resulted in changes in the fixed carbon and ash contents, while the volatile matter and H/C and O/C atomic ratios decreased. As the residence time increased, the H/C ratio and volatile matter content of the hydrochars (HCs) decreased. However, the fixed carbon content, ash content, and O/C and C/N ratios exhibited an increase. Thermodynamics, adsorption isotherms, and pH were also taken into consideration. The FTIR spectra analysis indicated that the carboxyl and ester functional groups present in both the BCs and HCs displayed reduced peak intensities subsequent to the adsorption of the three ions. While the adsorption was exothermic, we noticed that the adsorption capacity increased with temperature. The results indicate that sorption was homogenous across all binding sites, as evidenced by the optimal fit to the Langmuir isotherm. The research findings indicate that the adsorption capacity of various BC and HC adsorbents is significantly influenced by the surface area of the adsorbents in the case of nitrate and phosphate, but in the case of ammonia, adsorption is dictated by the functional polar groups present on the adsorbent surface.

Nowadays, thermochemical biomass conversion appears to be a very promising way to process heat and steam generation, for use in a cogeneration unit engine, or for example in gas turbines producing electrical energy. The biggest problem regarding using the syngas in internal combustion engines, are pollutants, which have quite an inauspicious influence on their proper working. This article deals with the establishment of the distribution size of solid particles captured by the fiber filters in the syngas with a suitable cleaning design. Gas was produced in the fixed-bed “Imbert” type generator. Filter cake, which contained pollutants, was captured on a filter and then analyzed. Based on single total solid particles (TSP) components, we conclude that this includes its partial elimination.

This paper presents the research results of the effect of using calcium oxide and potassium permanganate on the combustion of pellets from wheat bran and beet pulp. The measurements were performed in the technical laboratory of the Centre of Energy Utilization of Non-Traditional Energy Sources in Ostrava. The research examined the effect of the use of chemical substances on the amount of air pollutants from biomass thermal conversion in a low-power boiler and the process temperature. First, we performed technical and elementary analyses of agricultural waste. The raw material was then comminuted, mixed with a selected additive, pelletized, and finally burned in a low-power boiler. The additive was added in three proportions: 1:20, 1:10, and 1:6.67 (i.e., 15%) relative to the fuel weight. The combustion process efficiency was measured using a flue gas analyzer and three thermocouples attached to the data recorder. From the measurement results, we were able to determine the percentage reduction of pollutant emissions into the atmosphere (CO, NO_x, and SO₂) due to the use of additives. Because emission standards are becoming increasingly stringent and fuel and energy prices are rising, the results presented in this article may be useful to agri-food processing plants that want to manage these materials thermally.

In this paper, we analyze the interpretable models from real gasification datasets of the project “Centre for Energy and Environmental Technologies” (CEET) discovered by symbolic regression. To evaluate CEET models based on input data, two different statistical metrics to quantify their accuracy are usually used: Mean Square Error (MSE) and the Pearson Correlation Coefficient (PCC). However, if the testing points and the points used to construct the models are not chosen randomly from the continuum of the input variable, but instead from the limited number of discrete input points, the behavior of the model between such points very possibly will not fit well the physical essence of the modelled phenomenon. For example, the developed model can have unexpected oscillatory tendencies between the used points, while the usually used statistical metrics cannot detect these anomalies. However, using dynamic system criteria in addition to statistical metrics, such suspicious models that do fit well-expected behavior can be automatically detected and abandoned. This communication will show the universal method based on dynamic system criteria which can detect suitable models among all those which have good properties following statistical metrics. The dynamic system criteria measure the complexity of the candidate models using approximate and sample entropy. The examples are given for waste gasification where the output data (percentage of each particular gas in the produced mixture) is given only for six values of the input data (temperature in the chamber in which the process takes place). In such cases instead, to produce expected simple spline-like curves, artificial intelligence tools can produce inappropriate oscillatory curves with sharp picks due to the known tendency of symbolic regression to produce overfitted and relatively more complex models if the nature of the physical model is simple.