Junyu Tao

Ash deposition on heating surfaces can lead to clogging and corrosion of the incinerator and pipelines and heavy metal accumulation, significantly hindering the clean and stable running of municipal solid waste incineration (MSWI) systems. Relating deposit characteristics to operating descriptors is essential for developing evidence-based monitoring and mitigation strategies for heating surface deposition. In this study, ash samples were collected from heating surfaces along the flue gas path of seven full-scale MSWI plants. The correspondence between sampling conditions (location, temperature, aspect ratio, diagonal length, and flow rate) and the ash characteristics was discussed. According to the results, across all samples, the aspect ratio showed the clearest association with deposit roughness, while CaSO4 was the most frequently detected crystalline phase. The energy-dispersive spectroscopy data revealed a chloride-rich compositional pattern, although its strength varied across the sensitivity analyses. Pb showed the clearest decline with increasing temperature, whereas Cu, Zn, and As showed distinct associations with the flow rate or geometry. Integrated analysis separated carbonate- and chloride-rich patterns from sulfate- and metal-enriched patterns. Some of these overall relationships weakened when differences among plants were considered, highlighting the importance of the plant-specific conditions. Overall, geometry was more closely associated with deposit texture and capture-related patterns, whereas temperature was more closely associated with chemical evolution and volatile metal redistribution. This work provides an empirical framework for targeted deposit monitoring and corrosion risk assessment in full-scale MSWI systems.

Aerobic composting of biogas residue is critical for sustainable resource recovery. This study established a machine learning model to predict composting indexes (germination index, C/N_Final) and identified optimal operational conditions. The Random Forest algorithm was used to construct the machine learning model. The model was optimized through hyperparameter tuning and k-fold cross-validation, it achieved >94% accuracy across five performance indexes. Dual-method feature importance analysis identified feedstock composition (C/N_Inital, moisture content, organic matter) as the dominant factor, contributing 45.76% to composting efficiency. Experimental validation under model-predicted optimal conditions confirmed robust performance: high-benefit compost exhibited >95% agreement between predicted and actual values (germination index, C/N_Final), with compost maturity meeting international standards. This work provides a validated, data-driven strategy to maximize biogas residue valorization through precision composting.

The method based on machine learning and laser-induced breakdown spectroscopy (LIBS) is effective for rapid characterization of waste organic polymers (WOP). However, the lack of mechanistic interpretability leads to raises concerns regarding its reliability in practical applications. This study systematically investigated the fundamental chemical correlations between WOP fuel properties and LIBS spectral features through feature selection and machine learning interpretability analysis. Thirteen radical-associated key peaks were selected and strategically categorized into two groups for model construction. Under optimal conditions, the prediction accuracy for carbon, hydrogen, oxygen content and lower heating value (LHV) reach 97.74%, 91.22%, 91.28% and 97.02%, respectively. Notably, models utilizing 10 selected key peaks demonstrated superior performance compared to those employing raw LIBS spectra or principal components, especially with the absolute difference reaching 14.57% for O content prediction. Interpretability analysis showed that C2 swan bands had highest effects impacts on carbon, oxygen content and LHV prediction, whereas H I line was essential for hydrogen content prediction. This mechanistic investigation provided theoretical validation for LIBS-based rapid characterization systems, facilitating their practical implementation in downstream energy recovery processes. The established methodology offers a scientific foundation for advancing sustainable waste management and promoting circular economy development through efficient resource utilization.

Cold chain logistics is an emerging carbon emission source. The transportation section is estimated to generate more than 80% of its total carbon emission. Under the global warming background, a number of simulation and optimization studies have been thus conducted. Lately, cold chain logistics has been greatly aided by the quick advancements in data science and computer science. Consequently, the state-of-the-art developments in cold chain logistics system simulation and optimization were critically assessed in this research. The crucial indicators including optimization models, simulation models, and optimization algorithms were also quantitatively analyzed. It was discovered that, in single-objective optimization, carbon emissions were frequently included in the total cost objective function; but, in multi-objective optimization, they were utilized as a separate objective function. There were many studies on path optimization models, but site selection optimization models started relatively late and had high economic sensitivity, which might be a future trend. The ant colony algorithm was widely used in solving path optimization models. Genetic algorithms played a leading role in optimization algorithms, and they had different improvements and applications under different carbon reduction methods. Under the influence of multiple objective functions and constraints, multi-objective optimization algorithms might be a promising solution. The difficulties in modeling and refining the cold chain logistics system are introduced by the model accuracy, energy supply system revolution, carbon emission calibration, optimization result realization, and application scenarios that still exist. The outlook for the future is presented in terms of both technology orientation and demand orientation.

Aqueous-phase reforming (APR) is an alternative method for treating and utilizing biogas slurry (BS) to produce renewable hydrogen from organic oxygen-containing wastewater. Considering the fluctuating characteristics of BS with changes in the degree of fermentation, developing an efficient catalyst is a major concern for the APR of BS. The novel catalyst based on molybdenum-based metal–organic-framework-derived oxides (Mo-MOF-derived α-MoO₃) was reported in this study. The results indicated that the variables (e.g., pH, organic load, and salinity) of BS corresponded to the fermentation times and exhibited decreasing trends after APR under the reaction conditions of 225 °C and 30 min. Decarboxylation was identified as the main side reaction in the APR of BS over the catalyst. An optimal yield of 2.17 mL_hydrogen/mL_BS was achieved when BS was obtained from 6 days of fermentation. Finally, the Mo-MOF-derived α-MoO₃ catalyst was obtained from the greater specific surface area of MOFs. The catalyst had a weaker acidity than the initial α-MoO₃, making it more preferred for facilitating the APR of BS.

Ground-mounted photovoltaic (GMPV) systems are a crucial component of photovoltaic (PV) applications, and their environmental impacts during large-scale development require thorough attention. This study conducted continuous observations at a GMPV plant in an arid region, employing a three-site comparative monitoring system to assess the environmental impact of both shaded and non-shaded areas within GMPV systems. The parameters measured included atmospheric temperature (AT), relative humidity (RH), soil temperature (ST), soil water content (SWC), and wind speed. The results revealed significant diurnal and seasonal variations in AT, with daytime warming and nighttime cooling ranging from 0.1 to 0.7 °C, with particularly large variations observed during high-temperature seasons. Shaded areas under the PV panels exhibited increased RH at night and decreased RH during the day, along with a cooling effect on ST, with a maximum reduction of 7 °C. SWC was higher in shaded areas during dry seasons but exhibited complex redistribution patterns during rainy seasons. Wind speed and direction were notably altered, demonstrating a corridor effect. These findings contrast with previous studies that only focused on the environmental assessment of non-shaded areas within PV systems and external areas using two-site monitoring. This study highlights the critical role of shaded areas in understanding the local environmental impacts of PV systems. This comprehensive approach offers deeper insights into how PV systems influence local meteorological and environmental conditions, suggesting that optimized design and placement of PV systems can enhance their ecological benefits and mitigate adverse environmental impacts in arid regions.