Search Results (10,755)

This study presents a thermodynamic equilibrium analysis of hydrogen-rich syngas production via biomass–methane co-pyrolysis, employing the Gibbs free energy minimization method. A critical temperature threshold at 700 °C is identified, below which methanation and carbon deposition are thermodynamically favored, and above which cracking and reforming reactions dominate, enabling high-purity syngas generation. Methane addition shifts the reaction pathway towards increased reduction, significantly enhancing carbon and H₂ yields while limiting CO and CO₂ emissions. At 1200 °C and a 1:1 methane-to-biomass ratio, cellulose produces 50.84 mol C/kg, 119.69 mol H₂/kg, and 30.65 mol CO/kg; lignin yields 78.16 mol C/kg, 117.69 mol H₂/kg, and 19.14 mol CO/kg. The H₂/CO ratio rises to 3.90 for cellulose and 6.15 for lignin, with energy contents reaching 43.16 MJ/kg and 52.91 MJ/kg, respectively. Notably, biomass enhances methane conversion from 25% to over 53% while sustaining a 67% H₂ selectivity. These findings demonstrate that syngas composition and energy content can be precisely controlled via methane co-feeding ratio and temperature, offering a promising approach for sustainable, tunable syngas production. Full article

Modern energy systems are undergoing a profound transformation characterized by the active replacement of conventional fossil-fuel-based power plants with renewable energy sources. This transition aims to reduce the carbon emissions associated with electricity generation while enhancing the economic performance of electric power market players. However, alongside these benefits come several challenges, including reduced overall inertia within energy systems, heightened stochastic variability in grid operation regimes, and stricter demands on the rapid response capabilities and adaptability of emergency controls. This paper presents a novel methodology for selecting effective control laws for low-inertia energy systems, ensuring their dynamic stability during post-emergency operational conditions. The proposed approach integrates advanced techniques, including feature selection via decision tree algorithms, classification using Random Forest models, and result visualization through the Mean Shift clustering method applied to a two-dimensional representation derived from the t-distributed Stochastic Neighbor Embedding technique. A modified version of the IEEE39 benchmark model served as the testbed for numerical experiments, achieving a classification accuracy of 98.3%, accompanied by a control law synthesis delay of just 0.047 milliseconds. In conclusion, this work summarizes the key findings and outlines potential enhancements to refine the presented methodology further. Full article

Dry reforming of methane and ethanol is a promising catalytic process for the conversion of carbon dioxide and hydrocarbon feedstocks into synthesis gas (H₂/CO), which serves as a key platform for the production of fuels and chemicals. Over the past decade, substantial progress has been achieved in the design of catalysts with enhanced activity and stability under the demanding conditions of these strongly endothermic reactions. This review summarizes the latest developments in catalyst systems for DRM and EDR, including Ni-based catalysts, perovskite-type oxides, MOF-derived materials, and high-entropy alloys. Particular attention is given to strategies for suppressing carbon deposition and preventing metal sintering, such as oxygen vacancy engineering in oxide supports, rare earth and transition metal doping, strong metal–support interactions, and morphological control via core–shell and mesoporous architectures. These approaches have been shown to improve coke resistance, maintain metal dispersion, and extend catalyst lifetimes. The review also highlights emerging concepts such as multifunctional hybrid systems and innovative synthesis methods. By consolidating recent findings, this work provides a comprehensive overview of current progress and future perspectives in catalyst development for DRM and EDR, offering valuable guidelines for the rational design of advanced catalytic materials. Full article

Soil salinity disrupts rhizosphere interactions, impairing root–microbe symbioses, nutrient uptake, and water relations in onion (Allium cepa L.) and garlic (Allium sativum L.). This study evaluated the efficacy of biofertilizers (Azotobacter chroococcum SARS 10 and Azospirillum lipoferum SP2) and organic amendments (sewage sludge and poultry manure) in salt-affected soils in Kafr El-Sheikh, Egypt. Five treatments were applied: (T1) control (no amendments); (T2) biofertilizer (3 L/ha for onion, 12 L/ha for garlic) + inorganic P (150 kg/ha P₂O₅ for onion, 180 kg/ha for garlic) and K (115 kg/ha K₂SO₄ for onion, 150 kg/ha for garlic); (T3) 50% inorganic N (160 kg/ha for onion, 127.5 kg/ha for garlic) + 50% organic manure (6000 kg/ha for onion, 8438 kg/ha for garlic) + P and K; (T4) biofertilizer + T3; and (T5) conventional inorganic NPK (320 kg/ha N for onion, 255 kg/ha N for garlic + P and K). Soil nutrients (N, P, K), microbial biomass carbon (MBC), dehydrogenase activity, and microbial populations were analyzed using standard protocols. Plant growth (chlorophyll, photosynthetic rate), stress indicators (malondialdehyde, proline), and yield (bulb diameter, fresh yield) were measured. Treatment T4 increased MBC by 30–40%, dehydrogenase activity by 25–35%, available N (39.7 mg/kg for onion, 35.7 mg/kg for garlic), P (17.9 mg/kg for onion), and K (108 mg/kg for garlic). Soil organic matter rose by 8–12%, and cation exchange capacity by 26–36%. Chlorophyll content improved by 25%, malondialdehyde decreased by 20–30%, and fresh yields increased by 20–30% (12.17 tons/ha for garlic). A soybean bioassay confirmed sustained fertility with 20–25% higher dry weight and 30% greater N uptake in T4 plots. These findings highlight biofertilizers and organic amendments as sustainable solutions for Allium productivity in saline rhizospheres. Full article

The rapid rise in atmospheric CO₂ concentrations has intensified the need for scalable, sustainable, and economically viable carbon sequestration technologies. This study introduces a cost-effective CaO/Ca(OH)₂-based mineralization process that not only enables efficient CO₂ removal but also allows the simultaneous recovery of high-purity calcite nanoparticles as value-added products. The process involves hydrating CaO, followed by controlled carbonation under optimized CO₂ flow rates, temperature conditions, and and additive use, yielding nanocrystalline calcite with an average particle size of approximately 100 nm. Comprehensive characterization using X-ray diffraction, transmission electron microscopy, and energy-dispersive X-ray spectroscopy confirmed a polycrystalline structure with exceptional chemical purity (99.9%) and rhombohedral morphology. Techno-economic analysis further demonstrated that coupling CO₂ sequestration with nanoparticle production can markedly improve profitability, particularly when utilizing CaO/Ca(OH)₂-rich industrial residues such as steel slags or lime sludge as feedstock. This hybrid, multi-revenue strategy—integrating carbon credits, nanoparticle sales, and waste valorization—offers a scalable pathway aligned with circular economy principles, enhancing both environmental and economic performance. Moreover, the proposed system can be applied to CO₂-emitting plants and facilities, enabling not only effective carbon dioxide removal and the generation of carbon credits, but also the production of calcite nanoparticles for diverse applications in agriculture, manufacturing, and environmental remediation. These findings highlight the potential of CaO/Ca(OH)₂-based mineralization to evolve from a carbon management technology into a platform for advanced materials manufacturing, thereby contributing to global decarbonization efforts. Full article

Management of smart greenhouses represents a transformative advancement in precision agriculture, enabling sustainable intensification of food production through the integration of multi-sensor networks, intelligent control, and sophisticated data filtering techniques. Unlike conventional greenhouses that rely on manual monitoring, smart greenhouses combine environmental sensors, Internet of Things (IoT) platforms, and artificial intelligence (AI)-driven decision making to optimize microclimates, improve yields, and enhance resource efficiency. This review systematically investigates three key technological pillars, multi-sensor monitoring, intelligent control, and data filtering techniques, for smart greenhouse environment management. A structured literature screening of 114 peer-reviewed studies was conducted across major databases to ensure methodological rigor. The analysis compared sensor technologies such as temperature, humidity, carbon dioxide (CO₂), light, and energy to evaluate the control strategies such as IoT-based automation, fuzzy logic, model predictive control, and reinforcement learning, along with filtering methods like time- and frequency-domain, Kalman, AI-based, and hybrid models. Major findings revealed that multi-sensor integration enhanced precision and resilience but faced changes in calibration and interoperability. Intelligent control improved energy and water efficiency yet required robust datasets and computational resources. Advanced filtering strengthens data integrity but raises concerns of scalability and computational cost. The distinct contribution of this review was an integrated synthesis by linking technical performance to implementation feasibility, highlighting pathways towards affordable, scalable, and resilient smart greenhouse systems. Full article

Achieving carbon emission reduction synergy is vital for green economic transformation. This study examines whether environmental governance pressure promotes such synergy, simultaneously driving carbon reduction and pollution control. Leveraging the 2012 Ambient Air Quality Standard as a quasi-natural experiment, we employ a continuous difference-in-differences (DID) method on 250 prefecture-level cities from 2009 to 2022. Our findings reveal that increased environmental governance pressure significantly reduces both the total amount and intensity of carbon emissions, demonstrating a clear synergistic effect. This synergy is positively correlated with reductions in major air pollutants (e.g., SO₂ and NO_x), indicating that pressure curbs both the total amount and intensity of carbon emissions. Mechanistic analysis shows that this pressure primarily curtails carbon emissions by fostering green innovation and accelerating cleaner energy transitions, with no ‘green paradox’. It also promotes low-carbon industrial restructuring while reducing reliance on end-of-pipe pollution management. Heterogeneity analysis indicates stronger synergistic effects in regions with lower emission reduction costs (e.g., western China, less developed industrial bases). We recommend robust central government environmental regulation policies to amplify local governance pressure, strengthen carbon reduction synergy, and facilitate continuous green development. Full article

In this study, the decarburization behavior in basic oxygen furnace (BOF) steelmaking was investigated based on the multi-zone reaction mechanism. The contributions of the main reaction zones to decarburization were clarified, and the effects of key factors—including the effective reaction amount in the main reaction zones, the post combustion ratio (PCR) in auxiliary reaction zones, and the carbon content of scrap steel—on decarburization behavior were quantitatively analyzed. The results indicate that decarburization predominantly occurs in the jet impact reaction zone (approximately 76% of the total decarburization), followed by the emulsion and metal droplet reaction zone (approximately 14%) and the bulk metal and slag reaction zone (approximately 10%). Variations in the effective reaction amount for the main reaction zones significantly affect both the decarburization rate and the endpoint carbon content, with the direct oxidation decarburization reaction in the jet impact reaction zone being the dominant factor. In addition, the PCR in the gas homogenization zone of the auxiliary reaction zones determines the distribution ratio of effective reaction oxygen, while the melting behavior of scrap steel in the metal homogenization zone plays a critical role in the precise control of the endpoint carbon content. This study provides a quantitative elucidation of the effects of different reaction zones on decarburization behavior, offering a foundation for the precise control of endpoint carbon content in BOF steelmaking. Full article

Steel products play an important role in daily production and life as a common production material. Currently, the quality of steel products is judged by manual experience. However, various inspection criteria employed by human operators and complex factors and mechanisms in the steelmaking process may lead to inaccuracies. To address these issues, we propose a learning-based method for steel quality prediction, which is named DRBoost,based on multiple machine learning techniques, including Decision tree, Random forest, and the LSBoost algorithm. In our method, the decision tree clearly captures the nonlinear relationships between features and serves as a solid baseline for making preliminary predictions. Random forest enhances the model’s robustness and avoids overfitting by aggregating multiple decision trees. LSBoost uses gradient descent training to assign contribution coefficients to different kinds of raw materials to obtain more accurate predictions. Five key chemical elements, including carbon, silicon, manganese, phosphorus, and sulfur, which significantly influence the major performance characteristics of steel products, are selected. Steel quality prediction is conducted by predicting the contents of these chemical elements. Multiple models are constructed to predict the contents of five key chemical elements in steel products. These models are symmetrically complementary, meeting the requirements of different production scenarios and forming a more accurate and universal method for predicting the steel product’s quality. In addition, the prediction method provides a symmetric quality control system for steel product production. Experimental evaluations are conducted based on a dataset of 2012 samples from a steel plant in Liaoning Province, China. The input variables include various raw material usages, while the outputs are the content of five key chemical elements that influence the quality of steel products. The experimental results show that the models demonstrate their advantages in different performance metrics and are applicable to practical steelmaking scenarios. Full article

Active control of injection quantity during start-up idle optimizes automotive diesel engine starting performance, aligning with low-carbon goals. Conventional methods rely on a calibrated demand torque map adjusted by speed, temperature, and pressure variations, requiring extensive labor for calibration and limiting energy-saving and emission improvements. To address this problem, this paper proposes a transient injection quantity active control method for the start-up process based on the variation characteristics of target speed. Firstly, the target speed variation characteristics of the start-up process are optimized by setting different accelerations. Secondly, a transient injection quantity control strategy for the start-up process is proposed based on the target speed variation characteristics. Finally, the control strategy proposed in this paper was compared with the conventional starting injection quantity control method to verify its effectiveness. The results show that the start-up idle control strategy proposed in this paper reduces the cumulative fuel consumption of the start-up process by 25.9% compared to the conventional control method while maintaining an essentially unchanged start-up time. The emissions of hydrocarbon (HC), carbon monoxide (CO), and nitrogen oxides (NOx) exhibit peak reductions of 12.4%, 32.5%, and 62.9%, respectively, along with average concentration drops of 27.2%, 35.1%, and 41.0%. Speed overshoot decreases by 25%, and fluctuation time shortens by 23.6%. The results indicate that the proposed control method not only avoids complicated calibration work and saves labor and material resources but also effectively improves the starting performance, which is of great significance for the diversified development of automotive power sources. Full article

This paper presents a multivariable linear regression calibration method for non-dispersive infrared (NDIR) CO₂ sensors in a low-cost carbon monitoring network. We test this calibration method with data collected in a temperature- and pressure-controlled laboratory and evaluate the calibration method with long-term observational data collected at the Xinglong Atmospheric Background Observatory. Compared to data collected by a high-accuracy cavity ring-down spectrometer (Picarro), the results show that a multivariable linear regression approach incorporating temperature, pressure, and relative humidity can reduce the mean absolute bias from 5.218 ppm to 0.003 ppm, with root mean square errors (RMSE) within 2.1 ppm after calibration. For field observations, the RMSE is reduced from 8.315 ppm to 2.154 ppm, and the bias decreases from 39.170 ppm to 0.018 ppm. The calibrated data can effectively capture the diurnal variation of CO₂ mole fraction. The test of the number of reference data shows that about 10 days of co-located reference data are sufficient to obtain reliable measurements. Calibration windows taken from winter or summer provide better results, suggesting a strategy to optimize short-term calibration campaigns. Full article

Aviation is under increasing pressure to reduce carbon emissions in conventional transports and support the growth of low-altitude operations such as long-endurance eVTOLs. Hybrid-electric propulsion addresses these challenges by integrating the high specific energy of fuels or hydrogen with the controllability and efficiency of electrified powertrains. At present, the field of hybrid-electric aircraft is developing rapidly. To systematically study hybrid-electric propulsion control in aviation, this review focuses on practical aspects of system development, including propulsion architectures, system- and component-level modeling approaches, and energy management strategies. Key technologies in the future are examined, with emphasis on aircraft power-demand prediction, multi-timescale control, and thermal integrated energy management. This review aims to serve as a reference for configuration design, modeling and control simulation, as well as energy management strategy design of hybrid-electric propulsion systems. Building on this reference role, the review presents a coherent guidance scheme from architectures through modeling to energy-management control, with a practical roadmap toward flight-ready deployment. Full article

Electrochemical CO₂ reduction reaction (CO₂RR) is a key technology for achieving carbon neutrality and efficient utilization of renewable energy, capable of converting CO₂ into high-value-added carbon-based fuels and chemicals. Copper (Cu)-based catalysts have attracted significant attention due to their unique performance in generating multi-carbon (C₂₊) products such as ethylene and ethanol; however, there are still many controversies regarding their complex reaction mechanisms, active sites, and the dynamic evolution of intermediates. In situ Raman spectroscopy, with its high surface sensitivity, applicability in aqueous environments, and precise detection of molecular vibration modes, has become a powerful tool for studying the structural evolution of Cu catalysts and key reaction intermediates during CO₂RR. This article reviews the principles of electrochemical in situ Raman spectroscopy and its latest developments in the study of CO₂RR on Cu-based catalysts, focusing on its applications in monitoring the dynamic structural changes of the catalyst surface (such as Cu⁺, Cu⁰, and Cu²⁺ oxide species) and identifying key reaction intermediates (such as *CO, *OCCO(*O=C-C=O), *COOH, etc.). Numerous studies have shown that Cu-based oxide precursors undergo rapid reduction and surface reconstruction under CO₂RR conditions, resulting in metallic Cu nanoclusters with unique crystal facets and particle size distributions. These oxide-derived active sites are considered crucial for achieving high selectivity toward C₂₊ products. Time-resolved Raman spectroscopy and surface-enhanced Raman scattering (SERS) techniques have further revealed the dynamic characteristics of local pH changes at the electrode/electrolyte interface and the adsorption behavior of intermediates, providing molecular-level insights into the mechanisms of selectivity control in CO₂RR. However, technical challenges such as weak signal intensity, laser-induced damage, and background fluorescence interference, and opportunities such as coupling high-precision confocal Raman technology with in situ X-ray absorption spectroscopy or synchrotron radiation Fourier transform infrared spectroscopy in researching the mechanisms of CO₂RR are also put forward. Full article

This study develops an integrated framework for groundwater pollution source identification by coupling Principal Component Analysis (PCA), Positive Matrix Factorization (PMF), and the Mantel test, with the Qujiang River Basin as a case study. The framework enables a full-process assessment, encompassing qualitative identification, quantitative apportionment, and spatial validation of pollution drivers. Results indicate that groundwater chemistry is primarily influenced by three categories of sources: natural rock weathering, agricultural and domestic activities, and industrial wastewater discharge. Anthropogenic sources account for 73.7% of the total contribution, with mixed agricultural and domestic inputs dominating (38.5%), followed by industrial effluents (35.2%), while natural weathering contributes 26.3%. Mantel test analysis further shows that agricultural and domestic pollution correlates strongly with intensive farmland distribution in the midstream area, natural sources correspond to carbonate outcrops and higher elevations in the upstream, and industrial contributions cluster in downstream industrial zones. By integrating PCA, PMF, and Mantel analysis, this study offers a robust and transferable framework that improves both the accuracy and spatial interpretability of groundwater pollution source identification. The proposed approach provides scientific support for regionalized groundwater pollution prevention and control under complex hydrogeological settings. Full article

The aim of this work was to investigate the evolutionary mechanisms of an artificial sedimentary agglomerate formed by cathodic polarization in natural seawater during its abandonment to a natural environment. Previous studies indicate that the mineralogical evolution of the material is controlled by kinetic factors and/or the local precipitation of aragonite on the brucite surface. However, the observation of the precipitation of metastable phase precipitation during the initial immersion of this material (in powder form) has suggested the possibility of a more complex mechanism. The present study builds upon previous experimental work and includes thermogravimetric analysis and infrared spectrometry. The results are analyzed using numerical experimentation to evaluate the proposed hypotheses. Findings show that the transformation mechanism is characterized by the precipitation of metastable calcium carbonate phases. Under supersaturation conditions, these hydrated phases form on the brucite surface, limiting the mineral’s contact with the solution. The subsequent transformation of these amorphous phases into aragonite further reduces brucite–solution interaction, which explains the persistence of brucite both in the residual powder after 120 h of immersion and in the consolidated material after more than 20 years of exposure to natural seawater. Full article