The study tried to contribute to solving two serious environmental issues: CO₂ reducing and sewage sludge disposal. Thus, sewage-sludge-derived materials were obtained in order to be evaluated for CO₂ adsorption capacity. Therefore, the char resulted after the sewage sludge pyrolysis was subjected to oxidation and chemical activation processes by using different quantities of alkaline hydroxide. One of the obtained materials, activated with a lower quantity of alkaline hydroxide, was also treated with acid chloride. Further, the materials were structural and texturally characterized, and material treated with acid chloride was used for CO₂ adsorption tests, due to high surface area and pore volume. The handmade system coupled to a gas chromatograph allowed the adsorption efficiency evaluation using different feed gases (rich and poor in CO₂) by completed purge of pipe line and on-line check. Additionally, the adsorption capacity, separation efficiency, and CO₂ recovery were calculated. Taking into account the values for adsorption capacity (separation efficiency and CO₂ recovery), it can be concluded that the sewage sludge derived material could be a promising solution for CO₂ reduction and waste disposal.

The aim of the study was to investigate the differences between walnut genotypes of various geographical and genetic origins grown under the same or different environmental conditions. The biological material analyzed consisted in walnut kernels of 34 cultivars, nine advanced selections, and six hybrids harvested in 2015 and 2016, summing up to a total of 64 samples. The walnuts, walnut oil, and residue were characterized in respect to their chemical (proximate composition—fat, protein, nutritional value, fatty acids profile by ¹H-NMR) and carbon-13 isotopic composition. The data was used to statistically discriminate the cultivars according to composition, geographical area of origin, and year of harvest, comparing the Romanian cultivars, selections, and hybrids with the internationally available ones.

Soil degradation, declining fertility, and rising greenhouse gas emissions highlight the urgent need for sustainable soil management strategies. Among them, biochar has gained recognition as a multifunctional material capable of enhancing soil fertility, sequestering carbon, and valorizing biomass residues within circular economy frameworks. This review synthesizes evidence from 186 peer-reviewed studies to evaluate how feedstock diversity, pyrolysis temperature, and elemental composition shape the agronomic and environmental performance of biochar. Crop residues dominated the literature (17.6%), while wood, manures, sewage sludge, and industrial by-products provided more targeted functionalities. Pyrolysis temperature emerged as the primary performance driver: 300–400 °C biochars improved pH, cation exchange capacity (CEC), water retention, and crop yield, whereas 450–550 °C biochars favored stability, nutrient concentration, and long-term carbon sequestration. Elemental composition averaged 60.7 wt.% C, 2.1 wt.% N, and 27.5 wt.% O, underscoring trade-offs between nutrient supply and structural persistence. Greenhouse gas (GHG) outcomes were context-dependent, with consistent Nitrous Oxide (N₂O) reductions in loam and clay soils but variable CH₄ responses in paddy systems. An emerging trend, present in 10.6% of studies, is the integration of artificial intelligence (AI) to improve predictive accuracy, adsorption modeling, and life-cycle assessment. Collectively, the evidence confirms that biochar cannot be universally optimized but must be tailored to specific objectives, ranging from soil fertility enhancement to climate mitigation.

The capitalization of agri-food waste is essential for the sustainability of a circular economy. This work focuses on a solution to eliminate such waste, meat and bone meal (MBM), which is produced in large quantities by the food industry and is prohibited for use as animal feed under the European directives. Therefore, with the focus of converting waste to energy, the catalytic pyrolysis of MBM in the presence of mesoporous silica nanocatalysts (SBA-3 and SBA-16 materials and metallic derivates) was investigated in a home-made reactor for the production of renewable energy. The mesoporous silica materials were synthesized using relatively simple methods and then characterized in order to determine their morpho-structural characteristics. The MBM pyrolysis behavior under different experimental conditions was examined in detail, both in the presence and absence of the new catalysts. The resulting MBM-based pyrolysis products, MBM_PYOILs and MBM_PYGASs, were also assessed as potential alternative fuels, highlighting comparable energy values to conventional fuels. The outcomes of this investigation offer a potential pathway to the clean production of gas and oil, thus promoting the high-grade utilization of MBM waste.

The valorization of black liquor, a by-product produced in considerable quantities from the paper manufacturing processes, has demonstrated the effectiveness of thermal reconversion into pyrolysis gas, bio-oil, and bio-char, a sustainable approach placing the feedstock into a circular economy concept. The present study focused on developing disposal solutions through energy recovery via pyrolysis at 300 °C and 450 °C when lignite and nanomaterials (such as Cu-Zn-MCM-41, Ni-SBA-3, or Ni-SBA16) were used as catalysts. The results were compared to those of non-catalytic pyrolysis. The use of the Cu-Zn-MCM-41 catalyst proved to be efficient for pyrolysis gas production, reaching 55.22 vol% CH₄. The increase in the calorific value of the pyrolysis gas was associated with the use of the Cu-Zn-MCM-41, showing a value of 42.23 MJ/m³ compared to that of the non-catalytic process, which yielded 39.56 MJ/m³. The bio-oil resulting from the pyrolysis with Cu-Zn-MCM-41 showed the highest energy value at 6457 kcal/kg compared to that obtained with the other two nanocatalysts, Ni-SBA-3 and Ni-SBA-16, as well as that of the raw material, which had a value of 3769 kcal/kg. The analysis of bio-char revealed no statistically significant differences when comparing the outcomes from using the various nanocatalysts, suggesting their minimal impact on the energy content.

This study presents the development and implementation of an AI-driven control system for dynamic regulation of hydrogen blending in natural gas networks. Leveraging supervised machine learning techniques, a Random Forest Classifier was trained to accurately identify the origin of gas blends based on compositional fingerprints, achieving rapid inference suitable for real-time applications. Concurrently, a Random Forest Regression model was developed to estimate the optimal hydrogen flow rate required to meet a user-defined higher calorific value target, demonstrating exceptional predictive accuracy with a mean absolute error of 0.0091 Nm³ and a coefficient of determination (R²) of 0.9992 on test data. The integrated system, deployed via a Streamlit-based graphical interface, provides continuous real-time adjustments of gas composition, alongside detailed physicochemical property estimation and emission metrics. Validation through comparative analysis of predicted versus actual hydrogen flow rates confirms the robustness and generalizability of the approach under both simulated and operational conditions. The proposed framework enhances operational transparency and economic efficiency by enabling adaptive blending control and automatic source identification, thereby facilitating optimized fuel quality management and compliance with industrial standards. This work contributes to advancing smart combustion technologies and supports the sustainable integration of renewable hydrogen in existing gas infrastructures.