中国科学院广州能源研究所

Fast pyrolysis offers a promising pathway for converting biomass into liquid bio-oil, yet its high oxygen content, chemical instability, and complex composition hinder direct application as fuels or chemicals. Recent advances in experimental platforms and computational methods have reshaped research in this field. Multidimensional chromatography, high-resolution spectroscopies, synchrotron-based diagnostics, and quantum chemical modeling now enable accurate quantification of products, detection of reactive intermediates, and the development of predictive kinetic frameworks for individual yields. These innovations provide the scientific context for more systematic evaluations of biomass pyrolysis chemistry and facilitate the establishment of mechanistic links between molecular-level reactions and process performance. To ensure comprehensive coverage and transparency, this review applies the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for literature selection and analysis. Through this systematic approach, key studies on model compounds-including cellulose, hemicellulose, and especially lignin derivatives-are critically assessed, highlighting bond scission routes, reaction intermediates, cross-component coupling, and the origins of major pyrolysis vapors. The resulting insights have informed the construction of detailed kinetic models, though challenges remain in addressing multiphase effects, expanding reaction families, and reducing uncertainties in large-scale simulations. By integrating state-of-the-art methodologies with systematic literature synthesis, this review identifies both progress and persisting gaps in molecular-level understanding of fast pyrolysis, providing guidance for advancing detailed kinetic modeling and accelerating the development of efficient, mechanism-based pyrolysis technologies.

Identifying and quantifying the key pollutants that link CO2 and air pollutants (APs) is critical to achieving effective synergistic effects, yet existing research is very limited. Therefore, this study proposed an "EIQ" comprehensive analysis framework aimed at improving the efficiency of synergistic emission reduction of CO2 and APs. It identified the APs with the best synergistic effect with CO2 from multiple perspectives, and then constructed a quantitative assessment model for CO2 and APs by combining the LMDI and econometric model. We applied the "EIQ" framework to the industrial sector of Guangdong. Economically developed regions exhibit greater synergistic effects between CO2 and APs. NOx shows a high synergistic effect with CO2 compared to other APs during 2012-2021. The contribution of the synergy effect to NOx emission reduction gradually increases over time. CO2 emission reduction significantly affects NOx emission reduction, with every 1 Mt decrease in CO2 reducing NOx by 1873 tons.

Developing economical catalysts is of significant importance for the advancement of the sorption-enhanced gasification technology. In this study, through fixed-bed experiments and Aspen Plus simulations, the potential of steel slag as a catalyst in the biomass sorption-enhanced gasification process was comprehensively explored. The sorption-enhanced gasification experimental results indicated that steel slag could facilitate the sorption-enhanced gasification reaction, with the gas yield increasing from 250.3 ml/gbiomass in the absence of catalysts to 389.8 ml/gbiomass, representing an increment of 55.73 %. The typical components of steel slag, Fe2O3 and CaO, could enhance the yield of gas in the sorption-enhanced gasification, while SiO2 exhibited a relatively minor promoting effect. Furthermore, after five cycles of tests, the steel slag demonstrated a gas yield of 341.4 ml/gbiomass, exhibiting good cyclic stability. However, the low selectivity of gas products limited the application of steel slag in the sorption-enhanced gasification. Enhancing the specific surface area and oxygen reactivity of steel slag was potential directions for improving its sorption-catalytic properties. The Aspen results indicated that the introduction of steel slag led to a 41.64 % increase in the annual production income of methanol. This study provided valuable insights into the high-value utilization of steel slag.

Adsorption thermal energy storage (ATES) is one of the most important ways to realize the efficient utilization of solar energy. The adsorption reaction wave model revealed the heat and mass transfer process in the ATES reactor. However, the existing adsorption reaction wave model can only be used to calculate the overall performance of the ATES reactor with a stable output temperature because the reaction wave mechanism is not yet clear. In this paper, the adsorption rate wave transfer equation was developed using the mechanical wave transfer equation as a reference, which could be used to predict the thermal characteristics of reactor, including the overall performance and physical parameter distribution within the reactor. The results indicated that the maximum prediction deviation of the ATES reactor overall performance was only 6.1 % compared with experimental measurement. The evolution of moisture concentration and temperature in the reactor was characterized, and the minimum coefficient of determination of the predicted physical parameter distributions within the reactor reached 0.967. This prediction method bridged the gap that existing reactor performance prediction methods limited to stable output temperatures, while also predicting detailed physical parameter distributions in the reactor.

Low-temperature salt hydrate thermochemical energy storage technology has irreplaceable advantages in longterm energy storage. Currently, heat storage reactors are still in the preliminary research stage. Enhancing their practicality and scalability is a further research direction. Closed reactors have long cycle lifetimes and wide controllable ranges, making them highly practical. However, their complex structure poses significant challenges for optimization design. Utilizing Buckingham Pi-theorem for dimensional analysis can help identify design ideas among numerous parameters, simplifying the optimization process and providing feasible pathways for reactor scaling. In this study, a numerical model for closed reactors used in low-temperature salt hydrate thermochemical energy storage is established using numerical simulation methods. The Buckingham-Pi theorem is employed to summarize the factors affecting kinetics and heat/mass transfer processes within the reactor, introducing the Damk & ouml;hler number to characterize the relative adaptation relationship between reaction kinetics and transfer phenomena. The results indicate that Sherwood number (Sh), Nusselt number(Nu), temperature(Theta*), pressure(p*) and porosity(epsilon) are dimensionless factors influencing the heat/mass transfer performance of closed reactors, following a power-law distribution pattern. A nonlinear fitting yields the empirical correlation Da = (2449.8 +/- 43.5) Nu(-0.185) center dot Sh(-0.385) center dot Theta(*0.313) center dot p(*-0.434) center dot (1-epsilon)(1.364)(0.5 <= Nu <= 5, 0.5 <= Sh <= 5, 0.7 <= Theta* < 1, 1 < p*<= 5,0 < epsilon < 1). This correlation provides similarity criteria for reactor scaling and can assist engineers in predicting overall reaction rates and heat output under different operating conditions.