Search Results (1,853)

Direct Air Capture (DAC) technology plays a crucial role in reducing atmospheric CO₂, but large-scale deployment faces challenges such as high energy consumption, operational costs, and slow material development. This study provides a comprehensive review of DAC principles, including chemical and solid adsorption methods, with a focus on emerging technologies like Metal–Organic Frameworks (MOFs) and graphene aerogels. MOFs have achieved adsorption capacities up to 1.5 mmol/g, while modified graphene aerogels reach 1.3 mmol/g. Other advancing approaches include DAC with Methanation (DACM), variable-humidity adsorption, photo-induced swing adsorption, and biosorption. The study also examines global industrialization trends, noting a significant rise in DAC projects since 2020, particularly in the U.S., China, and Europe. The integration of DAC with renewable energy sources, such as photovoltaic/electrochemical regeneration, offers significant cost-reduction potential and can cut reliance on conventional heat by 30%. This study focuses on the integration of Artificial Intelligence (AI) for accelerating material design and system optimization. AI and Machine Learning (ML) are accelerating DAC R&D: high-throughput screening shortens material design cycles by 60%, while AI-driven control systems optimize temperature, humidity, and adsorption dynamics in real time, improving CO₂ capture efficiency by 15–20%. The study emphasizes DAC’s future role in achieving carbon neutrality through enhanced material efficiency, integration with renewable energy, and expanded CO₂ utilization pathways, providing a roadmap for scaling DAC technology in the coming years. Full article

Buildings account for a substantial share of global energy use, with cooling and heating systems contributing significantly to this demand. Conventional fixed setpoint temperatures overlook occupants’ thermal adaptability, often resulting in unnecessary energy consumption. Although adaptive setpoint temperatures have been investigated in residential and conventional office buildings, their applicability to public buildings, where occupancy is highly variable and indoor–outdoor thermal exchange occurs frequently, remains insufficiently explored. This study examines the performance of an adaptive cooling setpoint strategy in a public building in South Korea through simulation and in situ evaluation. A calibrated simulation model was used to compare cooling energy consumption between fixed and adaptive setpoint temperatures. Simulations indicated an overall 9.0% reduction in cooling energy use, with monthly savings exceeding 11.0% during cooling-dominant months. Validation results confirmed a 7.7% daily energy reduction, while survey results verified that occupant thermal comfort was maintained. The study findings indicate that adaptive thermal comfort-based setpoint temperature control shows promise for effective application in public buildings with similar operational characteristics, improving energy efficiency without compromising occupant comfort. This approach offers a practical pathway for sustainable HVAC operation in buildings with dynamic occupancy and operation features. Full article

The building sector represents a major source of global carbon emissions, with heating and cooling systems being particularly critical contributors, making the evaluation of sustainable low-carbon alternatives an urgent priority. In this study, life cycle assessment (LCA) methodology is used to analyze ground source heat pump (GSHP) systems against traditional heating, ventilation, and air conditioning (HVAC) systems based on project data from the city of Jinan and electrical grid characteristics of Northern China. It is specified that the functional unit is providing heating and cooling that maintains the indoor temperature of the building between 18 °C and 26 °C for 20 years. Following ISO 14040 standards, carbon emissions and economic performance across four phases—production, transportation, construction, and operation—over a 20-year life cycle were quantified using actual material inventory data and region-specific carbon emissions factors. The results demonstrate obvious environmental advantages for GSHP systems, which achieve a 51% reduction in life cycle carbon emissions compared to traditional systems based on the current power generation structure. Furthermore, sensitivity analysis shows that as the proportion of renewable energy in the grid increases to meet carbon neutrality targets, the reduction potential can even reach 88%. Economic analysis reveals that despite higher initial investments, GSHP systems achieve favorable performance with a positive 20-year net present value and an acceptable dynamic payback period for the project. This study shows that GSHP systems represent a viable strategy for sustainable building design in northern China, and the substantial carbon reduction potential can be further enhanced through grid decarbonization and renewable energy integration. The implementation of the GSHP system in newly constructed buildings, which require both heating and cooling, in Northern China, can be an effective strategy for advancing carbon neutrality goals. Full article

Decarbonizing building energy use requires efficient heat pumps and low-impact geothermal exchangers. A novel standalone TRNSYS Type was developed for a patented shallow horizontal ground heat exchanger (HGHE), called flat-panel (FP), designed at the University of Ferrara. Beyond simulating the FP in isolation, the Type enables coupling with other components within heat-pump configurations, allowing performance assessments that reflect realistic operating conditions. The Type was implemented in TRNSYS models of a ground-source heat pump (GSHP) and of a dual air and ground source heat pump (DSHP) to verify Type reliability and evaluate potential DSHP advantages over GSHP in terms of efficiency and ground-loop downsizing. The performance of the system was analyzed under varying HGHE lengths and DSHP control strategies, which were based on onset temperature differential $DT$. The results highlighted that shorter HGHE lines yielded higher specific HGHE performance, while higher $DT$ reduced HGHE operating time. Concurrently, the total energy extracted from the ground decreased with increasing $DT$ and reduced length, thus supporting long-term thermal preservation and allowing HGHE to operate under more favorable conditions. Exploiting air as an alternative or supplemental source to the ground allows significant reduction of the HGHE length and the related installation costs, without compromising the system performance. Full article

As the global cement industry moves toward energy efficiency and intelligent manufacturing, refined control of key processes like precalciner outlet temperature is critical for improving energy use and product quality. The precalciner’s outlet temperature directly affects clinker calcination quality and heat consumption, so developing a high-accuracy prediction model is essential to shift from empirical to intelligent control. This study proposes a TCN-BiLSTM hybrid neural network model for the accurate prediction and regulation of the outlet temperature of the decomposition furnace. Based on actual operational data from a cement plant in Guangxi, the Spearman correlation coefficient method is employed to select feature variables significantly correlated with the outlet temperature, including kiln rotation speed, high-temperature fan speed, temperature A at the middle-lower part of the decomposition furnace, temperature B of the discharge from the five-stage cyclone, exhaust fan speed, and tertiary air temperature of the decomposition furnace. This method effectively reduces feature dimensionality while enhancing the prediction accuracy of the model. All selected feature variables are normalized and used as input data for the model. Finally, comparative experiments with RNN, LSTM, BiLSTM, TCN, and TCN-LSTM models are performed. The experimental results indicate that the TCN-BiLSTM model achieves the best performance across major evaluation metrics, with a Mean Relative Error (MRE) as low as 0.91%, representing an average reduction of over 1.1% compared to other benchmark models, thereby demonstrating the highest prediction accuracy and robustness. This approach provides high-quality predictive inputs for constructing intelligent control systems, thereby facilitating the advancement of cement production toward intelligent, green, and high-efficiency development. Full article

This study investigates the performance of an electrooxidation (EO) process employing Sb₂O₅-doped RuO₂–ZrO₂|Ti anodes integrated into a concave-cover solar still for the degradation of Allura Red dye in aqueous solution and real river water. The anode was synthesized and characterized via scanning electron microscopy (SEM) and X-ray diffraction (XRD) to confirm its porous morphology and crystalline structure. Operational parameters—including supporting electrolyte concentration, initial solution pH, and current density—were systematically optimized. Under optimal conditions (pH 2–3 and 5 mA cm⁻²), the EO process was evaluated under natural solar irradiation. Sunlight exposure increased the solution temperature from approximately 20 °C to 50 °C, enhancing molecular diffusion and mass transport, thereby accelerating decolorization kinetics. Compared to EO performed under laboratory conditions, the solar-assisted system achieved an additional 20% increase in chemical oxygen demand (COD) removal and a fast reduction in color. When applied to real Lerma River water samples under these optimal conditions, the treatment achieved approximately 50% reduction in both COD and true color, demonstrating its applicability to complex environmental matrices. These results confirm that coupling electrooxidation with solar thermal input significantly improves pollutant degradation efficiency and energy performance, establishing this integrated approach as a promising and sustainable technology for advanced wastewater treatment. Full article

Digital transformation (DT) is reshaping cold chain operations through technologies such as the Internet of Things (IoT), artificial intelligence (AI), blockchain, and digital twins. However, evidence remains fragmented, and a systematic synthesis focused on how these technologies affect cold chain performance, sustainability, and cost-efficiency is limited. This PRISMA-based systematic literature review analyzes 107 studies published between 2009 and 2025 to examine enabling technologies and application areas, operational and sustainability impacts, and the main adoption challenges. The reviewed evidence suggests that digitalization can improve real-time visibility, temperature control, traceability, and energy management, supporting waste reduction and improved quality assurance. Key challenges include high implementation costs and uncertain returns on investment, interoperability constraints, data governance and cybersecurity concerns, and organizational readiness gaps. The paper concludes with implications for managers and policymakers and a future research agenda emphasizing integrated multi-technology solutions, standardized sustainability assessment, and rigorous validation through pilots, testbeds, and real-world deployments to enable scalable and resilient cold chain digitalization. Full article

With the rapid development of renewable energy sources in power generation, utility boilers need to perform load regulation over a wide range to maintain the stability of the power supply system. Preheating combustion technology is a potential approach to achieve wide load range operation, improve combustion stability, and lower NO_x emissions from utility boilers. Preheating combustion devices (PCDs) were designed and installed in the reduction zone of a boiler. These devices preheated the coal at an excess air ratio ranging from 0.35 to 0.7 to generate high-temperature gas and char, which effectively reduced NO_x formation in the furnace. Numerical studies were conducted to evaluate the combustion performance and nitrogen oxides emissions of a 330 MW utility boiler retrofitted with PCDs at different loads. The simulations were conducted over a load range of 20% to 100% of the rated load, corresponding to an electrical power of 66 MW to 330 MW. The preheated combustion device’s previous experimental data served as the boundary conditions of the preheated product nozzles. The simulation results demonstrated that the retrofitted boiler could operate stably from 20% to 100% of the rated load, maintaining acceptable combustion efficiency and lower NO_x emissions. The combustion efficiency gradually dropped with decreasing boiler load, reaching a minimum value of 95.6%. As the load declined, the size of the imaginary tangent circle of the boiler shrank, while the ignition distance increased. Additionally, the variation in NO_x concentration with load was complex. The NO_x concentration at the furnace outlet was between 102.7 and 220.3 mg/m³, and the preheated products effectively reduced the nitrogen oxides produced by combustion in the furnace at all loads. Full article

Driven by the shipping industry’s pressing need to reduce its environmental impact, methanol has emerged as a promising marine fuel. Methanol-diesel dual-fuel (DF) engines present a viable solution, yet their optimization is challenging due to complex, nonlinear interactions among operational parameters. This study develops an integrated simulation and data-driven framework for multi-objective optimization of a large-bore two-stroke marine DF engine. We first establish a high-fidelity 1D model in GT-POWER, rigorously validated against experimental data with prediction errors within 10% for emissions (NOx, CO, CO₂) and 3% for performance indicators. To address computational constraints, we implement a Polynomial Regression (PR) surrogate model that accurately captures engine response characteristics. The innovative Triple-Adaptive Chaotic Sparrow Search Algorithm (TAC-SSA) serves as the core optimization tool, efficiently exploring the parameter space to generate Pareto-optimal solutions that simultaneously minimize fuel consumption and emissions. The Entropy-Weighted TOPSIS (E-TOPSIS) method then identifies the optimal compromise solution from the Pareto set. At 75% load, the framework determines an optimal configuration: methanol substitution ratio (MSR) = 93.4%; crank angle at the beginning of combustion (CAB) = 2.15 °CA; scavenge air pressure = 1.70 bar; scavenge air temperature = 26.9 °C, achieving concurrent reductions of 7.1% in NOx, 13.3% in CO, 6.1% in CO₂, and 4.1% in specific fuel oil consumption (SFOC) relative to baseline operation. Full article

A modified Enzyme Action Optimizer (mEAO) is proposed to tune a Fractional-Order Proportional–Integral–Derivative (FOPID) controller for precise temperature regulation of a nonlinear continuous stirred tank reactor (CSTR). The nonlinear reactor model, adopted from a standard benchmark formulation widely used in CSTR control studies, is employed as the simulation reference. The tuning framework operates in a simulation-based manner, as the optimizer relies solely on the time-domain responses to evaluate a composite cost function combining overshoot, settling time, rise time, and steady-state error. Comparative simulations involving EAO, Starfish Optimization Algorithm (SFOA), Success History-based Adaptive Differential Evolution with Linear population size reduction (L-SHADE), and Particle Swarm Optimization (PSO) demonstrate that the proposed mEAO achieves the lowest cost value, the fastest convergence, and superior transient performance. Further comparisons with classical tuning methods, Rovira 2DOF-PID, Ziegler–Nichols PID, and Cohen–Coon PI, confirm improved tracking accuracy and smoother actuator behavior. Robustness analyses under varying set-points, feed-temperature disturbances, and measurement noise confirm stable temperature regulation without retuning. These findings demonstrate that the mEAO-based FOPID controller provides an efficient and reliable optimization framework for a nonlinear thermal-process control, with strong potential for future real-time and multi-reactor applications. Full article

Zirconium and its alloys are widely used in nuclear power engineering due to their favorable physical and mechanical properties and their low thermal-neutron absorption cross-section. Their high corrosion resistance in aqueous and steam environments at elevated temperatures is essential for the reliable operation of fuel assemblies and is associated with the formation of a stable, compact ZrO₂ oxide layer. However, under reactor conditions, the presence of hydrogen, iodine and other fission products can reduce corrosion resistance, making detailed corrosion assessment necessary. Manufacturing technology, alongside alloy composition, also plays a decisive role in determining corrosion behavior. This study presents corrosion test results for a Zr-1%Nb alloy processed under thermomechanical conditions corresponding to rolling in a special type of three-roll skew rolling–Radial-Shear Rolling (RSR). The applied rolling technology ensured the formation of a pronounced ultrafine-grained (UFG) structure in the near-surface layers, with an average grain size below 0.6 µm. EBSD and TEM observations revealed a largely equiaxed microstructure with refined grains and increased grain boundary density. The corrosion testing was performed in high-temperature steam vessels at 400 °C and 10.3 MPa for 72, 336, 720 and 1440 h. The results demonstrate that RSR processing is an efficient alternative to conventional multi-pass normal bar rolling with vacuum heat treatments, allowing a significant reduction in processing steps and eliminating the need for expensive tooling and intermediate thermal or chemical treatments. Bars manufactured using this method meet the ASTM B351 requirements. The specific weight gain did not exceed 22 mg/dm² after 72 h and 34.5 mg/dm² after 336 h. After 1440 h, the samples exhibited a continuous, uniform dark-grey oxide layer with an average thickness below 5.3 µm. Full article

Studying the thermodynamic properties of soft rocks is critical for geothermal energy extraction, as it elucidates their temperature-dependent mechanical behaviors and heat transfer mechanisms, thereby optimizing reservoir stimulation, enhancing extraction efficiency, and ensuring long-term operational stability. Owing to the intricate geothermal settings and interconnected physicochemical processes, the thermodynamic properties exhibit pronounced spatial heterogeneity and interdependencies. Concurrently, constraints imposed by technical and economic limitations result in scarce practical field survey and experimental data on these properties, severely hampering comprehensive assessments of geothermal energy potential and exploitation feasibility. To evaluate the spatial variability of thermodynamic properties for heterogeneous soft rock using limited data, the thermal conductivity (TC), heat capacity (HC), and thermal diffusivity (TD) were measured. A new Copula statistical method is used to analyze thermodynamic properties under limited measurement data. Spatial variability in heterogeneous soft rocks is quantified using random field theory. The methodology’s reliability is confirmed through cross-validation against theoretical predictions, empirical measurements, and simulation outputs. The analysis framework of thermodynamic variability characteristics has been presented by stability point analysis and linear regression analysis processes. The variance reduction function, scale of fluctuation, autocorrelation distances, and autocorrelation structure of thermodynamic properties for heterogeneous soft rock are analyzed and discussed. This study can provide scientific data for thermal energy analysis and geothermal reservoir modification specifically applicable to soft rock formations with diagenetic and tectonic histories similar to those investigated in the Weishan Lake area. Full article

A novel and facile route for intercalating alkali-metal ions and ammonium ions into the layered mixed-ion compound tungsten oxychloride (WO₂Cl₂) has been developed using the iodide–triiodide redox couple as a mild redox-active reagent. Unlike traditional intercalation techniques employing highly reducing and air-sensitive reagents such as n-butyllithium, alkali triethylborohydride, and naphthalenide, the I⁻/I₃⁻ redox system operates at a moderate potential (0.536 V vs. SHE), enabling safer handling under ambient conditions without stringent inert-atmosphere requirements. This redox pair promotes the reduction of W⁶⁺ to W⁵⁺, thereby facilitating cation insertion into the van der Waal (vdW) gaps of WO₂Cl₂. This method uniquely enables ammonium ion intercalation into WO₂Cl₂, a first for this system. Intercalation was confirmed by X-ray diffraction, scanning electron microscopy (SEM/EDS), Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS), with measured lattice expansion correlating well with Shannon ionic radii and coordinating environments. Electrical transport measurements reveal a transition from insulating WO₂Cl₂ to a semiconducting phase for K_0.5WO₂Cl₂, exhibiting a resistance drop of over four orders of magnitude. This work demonstrates the I⁻/I₃⁻ couple as a general, safe, and versatile method for layered mixed-anion materials, broadening the chemical toolkit for low-temperature, solution-based tuning of structures and properties. Full article

Current CO₂-based energy storage systems still face several unsolved technical challenges, including strong thermal destruction between the multi-stage compression and expansion processes, significant exergy destruction in heat exchange units, limited utilization of low-grade heat, and the lack of an integrated comprehensive performance framework capable of simultaneously evaluating thermodynamic, economic, and environmental performance. Although previous studies have explored various compressed CO₂ energy storage (CCES) configurations and CCES–Organic Rankine Cycle (ORC) couplings, most works treat the two subsystems separately, neglect interactions between the heat exchange loops, or overlook the combined effects of exergy losses, cost trade-offs, and CO₂-emission reduction. These gaps hinder the identification of optimal operating conditions and limit the system-level understanding needed for practical application. To address these challenges, this study proposes an innovative system that integrates a multi-stage CCES system with ORC. The system introduces ethylene glycol as a dual thermal carrier, coupling waste-heat recovery in the CCES with low-temperature energy utilization in the ORC, while liquefied natural gas (LNG) provides cold energy to improve cycle efficiency. A comprehensive 4E (energy, exergy, economic, and environmental) assessment framework is developed, incorporating thermodynamic modeling, exergy destruction analysis, CEPCI-based cost estimation, and environmental metrics including primary energy saved (PES) and CO₂ emission reduction. Sensitivity analyses on the high-pressure tank (HPT) pressure, heat exchanger pinch temperature difference, and pre-expansion pressure of propane (P₃₀) reveal strong nonlinear effects on system performance. A multi-objective optimization combining NSGA-II and TOPSIS identifies the optimal operating condition, achieving 69.6% system exergy efficiency, a 2.07-year payback period, and 1087.3 kWh of primary energy savings. The ORC subsystem attains 49.02% thermal and 62.27% exergy efficiency, demonstrating synergistic effect between the CCES and ORC. The results highlight the proposed CCES–ORC system as a technically and economically feasible approach for high-efficiency, low-carbon energy storage and conversion. Full article

The operating temperature of lithium batteries directly affects their charge–discharge performance. This study is based on the LF50K prismatic power battery. The battery’s thermal model and the computational fluid dynamics (CFD) control equation were established. After completing the model verification, a thermal management system with a bionic leaf vein flow channel was designed. The study focused on investigating the effects of varied flow passage configurations, inlet–outlet flow channel angles, flow channel widths, flow rates, leaf vein angles, and inlet–outlet positions on the cooling effect of the lithium battery module. The results show that, as the inlet–outlet angle and width of the bionic leaf vein fluid flow channel increase, the battery cooling effect deteriorates; the increase in the angle and flow channel width has an adverse impact on battery heat dissipation. The significant reduction in the battery’s maximum temperature observed with an elevated fluid flow rate underscores the positive contribution of flow rate to the cooling process. The effect of the leaf vein angle on the cooling of lithium batteries shows a fluctuating trend: when the angle rises from 30° to 45°, the battery’s peak temperature shows a slow upward tendency; conversely, with the angle further increasing from 45° to 80°, the maximum temperature shows a gradual downward tendency. Specifically, at an angle of 45°, Battery No. 5 hits a maximum temperature of 306.58 K (around 33.43 °C), with the maximum temperature difference also reaching 6.38 K. After optimizing the structural parameters, when operating under the maximum ambient temperature conditions in 2024, the maximum temperature of the battery module decreased by 7 K, and the temperature difference decreased by 5.47 K, enabling the battery to achieve optimal operating efficiency. This study lays a foundation for a further optimization of the thermal management system for lithium-ion batteries in subsequent research. Full article