Journal of Thermal Analysis and Calorimetry最新文献

The conventional hydrothermal upgrading (HT) of lignite exhibits high dehydration efficiency, but high reaction pressure and temperature, coupled with substantial wastewater generation, constrain its widespread adoption. Mild heat-pressure upgrading (MHU) conducted under reduced reaction conditions, despite not relying on liquid water, has constrained dehydration capabilities, thus falling short of delivering the anticipated enhancement in lignite quality. This study proposes a waterless mild heat-pressure upgrading (DES-MHU) strategy based on a deep eutectic solvent that efficiently dehydrates and deoxygenates lignite by restructuring the hydrogen bond network, thereby enhancing lignite rank. Compared to HT, DMT significantly reduced the reaction pressure from 8.6 MPa to 3.2 MPa at 300 °C, achieving dehydration efficiency of 77.11% and an oxygen removal rate of 14.49% at 280 °C. Compared to MHU, DES-MHU enhanced the removal of hydroxyl and carboxyl groups, especially in the 250–280 °C range, increasing them by 15.34% and 39.00%, respectively, and improved the removal of bound water by 15.24%. Density functional theory calculations revealed that the deep eutectic solvent effectively weakens the hydrogen bond strength between water molecules and oxygen-containing functional groups, with reductions of 12.66% for hydroxyl groups and 44.68% for ester groups, while reducing the Mayer and Laplace bond orders of the C–O and O–H bonds. This study presents a promising, environmentally benign strategy for lignite upgrading, contributing to the development of more efficient, lower-energy industrial processes.

The present review focuses on the issue of freshwater shortage and growing global request for freshwater, which requires a serious need for original technologies, predominantly solar stills combined to thermoelectric cooling (TEC) to improve desalination competence. The originality of this paper lies in directing a methodical review to analytically inspect design optimizations and performance enhancements in solar stills engaging TEC. Therefore, it goes beyond the prior efforts by resolving the insistent encounters of low productivity and energy inefficiency of conservative systems and discovering the developments made by the combined solar stills and TEC. Similarly, this review emphasizes appraising the helpfulness of different layouts and materials used in these systems through energy and exergy analyses. Important results elucidate that integrated TEC can meaningfully increase freshwater productivity, with reported gains of more than 570%. Effectiveness enhancements are ranged between 11.2 and 76.4%. Furthermore, the incorporation of nanofluids, mainly copper oxide nanoparticles at a 0.08% concentration, has improved freshwater productivity by 81% and exergy efficacy by 112.5%. Further benefits are stated by presenting hybrid designs that incorporate photovoltaic panels, phase change materials (PCMs), and heat pipes. Specifically, the hybrid designs afford the possibility of continuous 24-h operation at reduced freshwater production cost of less than $0.031 per liter. Referring to energy and exergy analyses, it can be assured that TEC can play an essential role in minimizing exergy destruction and maximizing thermal gradients within the system. Thus, it can be determined that TEC-integrated solar stills can offer a wonderful solution for sustainable freshwater production to tackle the progressive water scarcity issue. However, some other barriers are still existed that related to high energy consumption and economic viability that must be resolved. Future investigation should therefore put efforts toward developing optimal designs of TEC-integrated solar stills to ensure a balance between performance, cost, and scalability to enable broader implementation.

The widespread use of electronic appliances is driving the increasing electricity demand in residential sectors, putting immense pressure on local distribution grids. While smart grid technologies integrated with renewable energy systems are widely promoted for demand-side energy management, their effectiveness is often hindered by the intermittent nature of solar and wind power and the lack of user-independent control strategies. Most existing studies have not addressed the impact of human behavior on IoT-enabled smart grid performance in real-world residential settings, nor proposed adaptive control solutions that operate reliably across seasonal variations. This study aims to bridge this gap by experimentally analyzing a solar photovoltaic (PV)-powered household equipped with IoT-based energy monitoring systems and evaluating its seasonal energy performance. The test environment consisted of a family of four, and energy usage data were collected across summer, winter, and monsoon seasons. Initial assessments showed only marginal reductions in grid electricity demand, especially during the monsoon (1.5%), due to manual overrides interfering with IoT operations. To overcome this limitation, a novel hybrid machine learning algorithm, combining two adaptive models, was introduced to automate energy control and decision-making. The deployment led to grid load reductions of 69.0%, 41.0%, and 43.0% in the summer, winter, and monsoon seasons, respectively. The study demonstrates that integrating AI-driven automation with IoT systems significantly enhances the energy efficiency and autonomy of residential smart grids, offering a robust solution to overcome behavioral and seasonal variability.

The chemical composition of rocks and sedimentation conditions vary by region, making it challenging to identify the typical evolution of rocks, even of the same type, under different temperatures. This study aimed to link the mineralogical alteration of Egyptian limestone induced by temperatures up to 800 °C with changes in its microstructure and physical and mechanical properties. The results indicated that limestone’s P-wave velocity and elastic modulus decrease linearly with increasing temperature. However, the porosity and mass loss of limestone change slowly as the temperature rises to 400 °C, followed by a noticeable increase above this threshold. Furthermore, the specimen’s mass rapidly changes between 600 and 800 °C, and the breakdown of CaCO₃ is most rapid at 782 °C. Once heated to 800 °C, the surface turns pale gray or white, and the exterior surface peels off, small chunks detach, and open fractures develop. On the other hand, the uniaxial compressive strength rises to even 400 °C and then drops sharply after that temperature. All the metrics studied showed significant changes at 400 °C, leading to a dramatic increase in crack density. Hence, we identified a remarkable evolution point at 400 °C. After this temperature, the mechanical, physical, and microstructural properties of limestone worsened. Compared with limestones from other regions, the Egyptian limestone used in this study is purer and less brittle. Therefore, Egyptian limestone is ideal for applications that require high-temperature stability.

As electric vehicles (EVs) continue to grow in popularity and adoption, one of the critical challenges in their design and operation is the efficient management of heat generated by the battery and powertrain systems. Battery temperature significantly impacts the performance, safety, and longevity of EV batteries. Excessive heat can cause accelerated degradation, reduced energy density, or thermal runaway. As critical components like batteries and motors overheat, EV performance declines. Thermal management systems are essential to regulate temperatures, but cooling systems consume energy, potentially reducing overall vehicle efficiency. Temperature fluctuations affect battery charge/discharge efficiency, lower energy density, speed up degradation, and shorten battery lifespan. Design and test innovative cooling technologies like liquid cooling, phase-change materials, and heat pipes to regulate battery temperature and prevent overheating. Optimize the energy consumption of cooling systems to minimize efficiency loss, ensuring thermal management enhances vehicle performance without compromising overall energy efficiency. The process involves designing an advanced thermal management system for EV batteries by selecting cooling technologies like liquid cooling, phase-change materials (PCMs), and heat pipes. Key elements include simulating temperature regulation and energy efficiency to minimize power loss while enhancing battery life. Materials with high thermal conductivity, such as water–glycol mixtures, copper, and paraffin wax, are chosen for their heat dissipation properties. The system is integrated with the Battery Management System (BMS) for real-time temperature control. Findings show Ni-MH batteries generate the most heat (1.4 W, 1.6 W), followed by Pb (0.8 W, 1.0 W) and Ni–Cd (0.6 W, 1.05 W). Lithium batteries are more efficient, generating less heat (0.2 W, 1.0 W, 1.1 W). Data were processed using Python Software. Future scope includes developing more efficient, lightweight cooling materials, integrating AI-driven temperature control systems, and exploring advanced phase-change materials (PCMs) to further optimize battery performance, lifespan, and energy efficiency in EVs.

Energy storage devices in thermal solar plants play a crucial role in controlling the energy and power demand. Their performance is significantly influenced by the thermal capacity of the materials used. Motivated by the growing need for enhanced thermal energy efficiency, a Williamson ternary hybrid nanofluid is used to examine the non-steady magnetohydrodynamic (MHD) flow through a porous stretching cylinder containing gyrotactic microorganisms. Physics-informed neural network (PINN) with GaussSwish hybrid activation function is utilized in this study. The network minimizes the residuals of the governing equations together with boundary constraints using automatic differentiation and the NADAM optimizer until it converges to the optimal loss. The effects of different flow parameters on temperature, momentum, concentration, and motile density are analyzed. Magnetic and electric field parameters show a drop in the momentum profile, whereas an inverse trend is noticed in the temperature profile. Weissenberg number, curvature, and heat sink parameters contribute to elevate the temperature. Schmidt number lowers the concentration profile; on the other hand, the curvature parameter exhibits an opposite relation. Peclet and bioconvection Lewis number cause the motile microorganism density to decline. Ternary hybrid nanofluid achieves up to (24.3%) greater heat transfer, (29.7%) mass transfer, and (34.1%) higher motile microorganisms density than the hybrid nanofluid, confirming its potential for advanced thermal energy storage systems. The results further show the effectiveness of physics-informed neural networks in handling complex fluid problems.

Tar-rich coal pyrolysis offers a practical route to bolster domestic oil supply, yet pore structures in Shaanxi coals respond differently to temperature and systematic comparisons remain limited. Here, tar-rich coals from the Zhangjiamao, Caojiatan, Yuanzigou, and Dafosi mines were heated to 400, 450, 500, and 550 °C, and low-temperature N₂ adsorption was used to track temperature-driven pore-structure evolution. At room temperature, pore-volume distributions fall into two categories: Type-Ⅰ, dominated by micropores, and Type-Ⅱ, concentrated near the mesopore–macropore boundary. From 400 to 450 °C, mesopores respond most strongly, showing the largest volume increase and the fastest mass-loss rate, indicating progressive opening of previously closed pores and their linkage with native mesopores to form new pore networks. Above 450 °C, pyrolysis intensifies: newly generated micro- and macropores dominate, fracture connectivity grows, and thermal rupture drives mesopore collapse, together yielding rapid increases in micro/macropore volumes and enhanced connectivity that favor more complete pyrolysis. Fractal analysis further shows that, for Type-Ⅰ, pore-surface oxidation between 400 and 450 °C markedly increases pore complexity, whereas above 450 °C new micro/macropores control structural change; Type-Ⅱ exhibits opposite trends over the same window, governed by distinct thermal responses. These results identify 400–450 °C as a critical interval for internal pore transformation and provide guidance for efficient utilization of tar-rich coal resources.

Compact heat exchangers play a vital role in automotive thermal management, particularly in heavy-duty transportation, where efficient heat dissipation is essential for engine reliability. This study investigates the thermophysical behavior of locally available coolant–water blends to enhance heat transfer rate while maintaining higher temperature gradients. Experiments were conducted using a counter-flow compact heat exchanger test rig. Coolant mixtures were prepared by blending three commercially available coolants such as GOETZE, MFC, and CASTROL oil of each contribution at 10% and 12% volume concentrations with distilled water. Uniform dispersion was achieved through controlled sonication involving specific temperature, pressure, and stirring conditions. The Taguchi method was employed to optimize mixing parameters for superior thermal performance, revealing that sonication time and concentration ratio significantly influenced thermal conductivity and viscosity. Experimental results showed that GOETZE at 12% concentration provides a higher temperature gradient, leading to improved heat transfer efficiency. Comparative analysis of GOETZE and Castrol blends (10% and 12%) indicates that GOETZE exhibits superior thermal performance due to its higher temperature gradient, making it a strong candidate for heat exchanger applications.

Iron oxide is commonly used as the active component of catalysts. Highly crystalline pure-phase porous α-Fe₂O₃ particles were prepared by direct thermal decomposition of Fe(NO₃)₃·9H₂O through a solid-state one-step method of ball milling and mixing with the assistance of citric acid. The thermal decomposition process was discussed. The results show that after the addition of citric acid, the temperature at which all the crystalline water in Fe(NO₃)₃·9H₂O is removed is 18 °C lower than that of the single component Fe(NO₃)₃·9H₂O, and the initial temperature for the decomposition of Fe(NO₃)₃ is 6 °C lower. The interaction between citric acid and Fe(NO₃)₃·9H₂O, as well as the formation of new chemical bonds at high temperatures, increased the decomposition temperature of Fe(NO₃)₃ by 36 °C. The apparent activation energy for the decomposition of the precursor was estimated using the KAS and FWO methods. By varying the amount of citric acid, α-Fe₂O₃ particles with different pore structures could be prepared. This solid-phase method is an effective way to controllably prepare porous α-Fe₂O₃ with high yield and high crystallinity.

The aim of this study is to suggest pyrolysis as a possible route for pineapple crown, by estimating the kinetic triplet that efficiently represents the slow pyrolysis of raw and previously torrefied pineapple crown. Thermogravimetric analyses were performed at heating rates of 2.5, 5, and 10 K min⁻¹, and global methods were applied (isoconversional methods and compensation effect). The raw material was characterized by proximate analysis and chemical composition, and these values were consistent with other biomasses. The stages verified for the decomposition of the raw and torrefied biomass were dehydration, volatilization and carbonization. The torrefaction process mainly decomposed the hemicellulose. Furthermore, an increase in the activation energy of the pyrolysis of the torrefied material was observed, which occurs due to the degradation of the most reactive portion of the material. The slow pyrolysis of the raw pineapple crown presented an activation energy of 154.56 kJ mol⁻¹, a global pre-exponential factor of 1.75 × 10¹² s⁻¹, and a global conversion function N3 (third-order reaction). The pyrolysis of torrefied biomass presented an activation energy of 176.33 kJ mol⁻¹, a global pre-exponential factor of 1.34 × 10¹³ s⁻¹, and a global conversion function N2 (second-order reaction). This work proposed a methodology based on the reconstruction of experimental kinetic curves using all conversion functions, and the results indicate that the methodology effectively determined the parameters that represent the decomposition of the heterogeneous material.

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