Oxygen-steam circulating fluidized bed gasification of biogenic waste enables biofuel production with low feedstock pretreatment, and its efficiency can be improved by an active bed material. This study presents the first oxygen carrier aided gasification (OCAG) tests with ilmenite at 1 MW(th) pilot scale. The non-optimized, autothermal plant achieves cold gas efficiencies including C 2 H 4 up to 72 % and carbon conversion efficiencies of 86–97 % (gas-based) and 92 % (solids-based). A high oxidation degree in the bottom ash and a low one in the circulating material indicate oxygen transport to the upper reactor part. About 29 % of the measured carbon output (excluding CO 2 and CO) and 42 % of the feedstock energy input (excluding H 2 and CO) remain available for downstream conversion. Averaged over four balance points, ilmenite yields markedly lower heterocyclic and oxygenated aromatic hydrocarbons (0.3 g kg(feed) −1 ) and polycyclic aromatic hydrocarbons (3.3 g kg(feed) −1 ) than sand and olivine. Polycyclic aromatic hydrocarbon yields are similar to or lower than those from magnesite and dolomite, at higher monocyclic aromatic hydrocarbon yields (22.1 g kg(feed) −1 ). Differences in reactor type, pressure, residence time, and measuring setup exclude attribution to the bed material alone.
Incomplete oxidation of light pyrolysis gases caused by locally insufficient oxygen, limited residence time, and imperfect mixing may reduce combustion efficiency in biomass circulating fluidized-bed (Bio-CFB) systems. Ca- and Mg-based materials, commonly introduced for in-furnace desulfurization and agglomeration control, have the potential to affect the oxidation of those volatile species through heterogeneous catalytic pathways. This study investigated high-temperature acetylene (C2H2, a representative intermediate of biomass-derived volatiles) oxidation over CaO and MgO in a tubular-flow fixed-bed reactor at 700–900 °C, excess oxygen ratios of 3–12, and particle sizes of 3, 45, and 120 μm. A multiscale kinetic framework including the surface-, particle-, and reactor-scale models was developed to distinguish homogeneous and heterogeneous contributions and kinetic parameters. Both CaO and MgO substantially promoted C2H2 oxidation, with higher catalytic activity of CaO. Both materials exhibited a similar non-monotonic particle-size dependence. Multiscale fitting yielded activation energies of 81.58 and 96.05 kJ∙mol−1 for C2H2 oxidation to CO2 and CO over CaO, compared with 94.57 and 103.25 kJ∙mol−1 over MgO. These findings clarify the coupled effects of intrinsic catalytic activity and mass transport on C2H2 conversion, providing a kinetic basis for improving volatile burnout and coordinating the use of functional bed additives in Bio-CFB combustion.
In aviation, the most promising strategy for reducing greenhouse gas emissions is the use of sustainable aviation fuels (SAF). Biomass being a key feedstock poses specific challenges due to its limited availability and heterogeneous composition, highlighting the necessity for developing efficient and flexible conversion pathways for maximizing carbon and energy utilization. Given that integrated conversion pathways remain insufficiently explored, this study evaluates a novel, integrated process concept, utilizing food waste as the primary feedstock and combining biochemical conversion (consisting of dark fermentation, a microbial electrolysis cell and aerobic fermentation) with hydrothermal gasification to produce the intermediate 2,3-butanediol (2,3-BDO), followed by thermochemical conversion of 2,3-BDO to SAF. The overall process is modelled and simulated in Aspen Plus and evaluated in terms of overall carbon and energy efficiencies, supplemented by a sensitivity analysis of uncertain key parameters. The results show that the proposed pathway converts food waste into hydrocarbons with an overall carbon efficiency of 25.4% and an energy efficiency of 20.5%, of which 9.5% of the carbon and 7.6% of the energy are recovered as SAF. Additionally, the study identifies key parameters and strategies for potential process improvement, providing a quantitative basis for further process development.
With increasing demand for high-safety, high-performance, and thrust-controllable fuels in aerospace propulsion systems, gel fuels have emerged as an important research focus. Gel fuels combine convenient transportability and flexible thrust controllability of liquid fuels with the high energy density and excellent storage safety of solid fuels. This review provides an overview of recent research progress on gel fuels within the framework of “preparation–microstructure–rheology–atomization,” emphasizing the relationships among preparation, rheology, and atomization. Firstly, it focuses on their preparation processes and gelation mechanisms. Secondly, it summarizes and analyzes the key non-Newtonian rheological properties of gel fuels, including shear thinning, yield stress, thixotropy, viscoelasticity, and extensional rheology. Thirdly, atomization represents a critical step in translating fundamental physical properties into practical engineering applications, as atomization quality can significantly influence combustion efficiency and propulsion performance. This review focuses on mechanical pressure atomization, pneumatic atomization, and auxiliary atomization technologies, providing a detailed account of gel fuel atomization characteristics and analyzing the core factors influencing atomization efficiency. Finally, it assesses the achievements and limitations of gel fuels and discusses future development trends. The aim is to provide guidance for the engineering application of gel fuels in aerospace propulsion systems.
Accurate prediction of the performance and emission characteristics of nanoparticle-enhanced biodiesel-fueled compression ignition engines is essential for efficient engine optimization, cleaner combustion, and reducing experimental costs and time. This study integrates combustion physics with stacked ensemble learning to develop a physics-informed Bayesian-optimized hybrid (PIBoH) framework to simultaneously predict brake thermal efficiency (BTE), brake-specific fuel consumption (BSFC), hydrocarbon (HC), carbon monoxide (CO), and nitrogen oxide (NOx) emissions. A comprehensive experimental database was compiled from published studies covering diverse biodiesel feedstocks, metal oxide nanoparticles, and engine operating conditions. To embed domain knowledge into the learning process, conventional categorical descriptors were replaced with physically meaningful fuel-property variables, a quantitative additive overall effectiveness index, and seven physics-informed engineered features. Bayesian optimization was employed to fine-tune hyperparameters of the PIBoH framework. The designed framework predicted 298 datasets with average absolute relative deviations of 4.48%, 3.44%, 5.42%, 6.63%, and 5.30% for BTE, BSFC, HC, CO, and NOx, respectively, corresponding to coefficients of determination of 0.9543, 0.9808, 0.9924, 0.9934, and 0.9901. Applicability-domain analysis further demonstrated reliable prediction stability across the investigated operating conditions. SHapley Additive exPlanations (SHAP) analysis identified fuel density and engine load as the dominant variables governing both engine performance and pollutant formation.
