Search Results (383)

Hybrid thermoelectric generators (HTEGs) play a pivotal role in sustainable energy conversion by harnessing waste heat through the Seebeck effect, contributing to global efforts in energy efficiency and environmental sustainability. In practical sustainable energy systems, HTEG output performance is significantly influenced by uncertainties in the operational parameters (such as temperature differences and load resistance), material properties (including Seebeck coefficient and resistance), and structural configurations (like the number of series/parallel thermoelectric components), which impact both efficiency and system stability. This study employs the Sobol-sequence-sampling method to characterize these parameter uncertainties, analyzing their effects on HTEG output power and conversion efficiency using mean values and standard deviations as evaluation metrics. The results show that higher temperature differences enhance output performance but reduce stability, a larger load resistance decreases performance while improving stability, thermoelectric materials with high Seebeck coefficients and low resistance boost efficiency at the expense of stability, increasing series-connected components elevates performance but reduces stability, parallel configurations enhance power output yet decrease efficiency and stability, and greater contact thermal resistances diminish performance while enhancing system robustness. This research provides theoretical guidance for optimizing HTEGs in sustainable energy applications, enabling the development of more reliable, efficient, and eco-friendly thermoelectric systems that balance performance with environmental resilience for long-term sustainable operation. Full article

Thermocouples can be combined into thermopiles to sense heat differences or achieve localized heating and cooling. However, integrating them into textiles using yarns is not straightforward, and chemical methods face challenges like complex processing, poor scalability, and voltage non-uniformity. This study employs conventional weaving to fabricate textile-based thermocouples and thermopiles for wearable sensing and potential cooling applications, with a focus on protective clothing. Using stainless steel and nickel-coated carbon yarns, we demonstrate a more stable thermocouple than those made with chemical or welded methods, with minimal fabric damage. Four conductive yarns, stainless steel, carbon fiber (CF), and nickel-coated carbon fiber (NiFC), were woven and laser-cut to form thermocouples using three different binding types to connect them. Inox1–NiFC was the most efficient thermocouple, achieving the highest Seebeck coefficient of 21.87 µV/K with Binding 3. Binding 3 also reduced contact resistance by 66% across all configurations. Slightly lower but comparable performance was seen with Inox1–NiFC/Binding 2 (21.83 µV/K) and Inox2–NiFC/Binding 1 (15.79 µV/K). In contrast, FC-based thermocouples showed significantly lower Seebeck values: 5.67 µV/K (Inox2–FC/Binding 2), 5.43 µV/K (Inox1–FC/Binding 3), and 5.06 µV/K (Inox2–FC/Binding 1). A woven thermopile with three junctions made with the optimal binding and thermocouple combination generated an average of 55.54 µV/K and about 500 µV at small temperature differences (4–5 °C), with a linear voltage response suitable for sensing. While thermal sensing proved effective, Peltier cooling needs further optimization. This method offers a stable, low-cost, and scalable platform for textile-integrated thermoelectric systems, with strong potential for use in uniforms and other protective garments. Full article

This paper addresses the economic–environmental dispatch (EED) problem in DC power grids integrating thermoelectric and photovoltaic generation. A multi-objective optimization model is developed to minimize both fuel costs and CO₂ emissions while considering power balance, voltage constraints, generation limits, and thermal line capacities. To overcome the non-convexity introduced by quadratic voltage products in the power flow equations, a convex reformulation is proposed using second-order cone programming (SOCP) with auxiliary variables. This reformulation ensures global optimality and enhances computational efficiency. Two test systems are used for validation: a 6-node DC grid and an 11-node grid incorporating hourly photovoltaic generation. Comparative analyses show that the convex model achieves objective values with errors below 0.01% compared to the original non-convex formulation. For the 11-node system, the integration of photovoltaic generation led to a 24.34% reduction in operating costs (from USD 10.45 million to USD 7.91 million) and a 27.27% decrease in CO₂ emissions (from 9.14 million kg to 6.64 million kg) over a 24 h period. These results confirm the effectiveness of the proposed SOCP-based methodology and demonstrate the environmental and economic benefits of renewable integration in DC networks. Full article

Dark asphalt surfaces, absorbing about 95% of solar radiation and warming to 60–70 °C during summer, intensify urban heat while providing substantial prospects for energy extraction. This review evaluates four primary technologies—asphalt solar collectors (ASCs, including phase change material (PCM) integration), photovoltaic (PV) systems, vibration-based harvesting, thermoelectric generators (TEGs)—focusing on their principles, efficiencies, and urban applications. ASCs achieve up to 30% efficiency with a 150–300 W/m² output, reducing pavement temperatures by 0.5–3.2 °C, while PV pavements yield 42–49% efficiency, generating 245 kWh/m² and lowering temperatures by an average of 6.4 °C. Piezoelectric transducers produce 50.41 mW under traffic loads, and TEGs deliver 0.3–5.0 W with a 23 °C gradient. Applications include powering sensors, streetlights, and de-icing systems, with ASCs extending pavement life by 3 years. Hybrid systems, like PV/T, achieve 37.31% efficiency, enhancing UHI mitigation and emissions reduction. Economically, ASCs offer a 5-year payback period with a USD 3000 net present value, though PV and piezoelectric systems face cost and durability challenges. Environmental benefits include 30–40% heat retention for winter use and 17% increased PV self-use with EV integration. Despite significant potential, high costs and scalability issues hinder adoption. Future research should optimize designs, develop adaptive materials, and validate systems under real-world conditions to advance sustainable urban infrastructure. Full article

This article presents the development, integration, and experimental validation of a modular microgrid for sustainable hydrogen production, addressing global electricity demand and environmental challenges. The system was designed for initial validation in a thermoelectric power plant environment, with scalability to other applications. Centered on a six-compartment skid, it integrates photovoltaic generation, battery storage, and a liquefied petroleum gas generator to emulate typical cogeneration conditions, together with a high-purity proton exchange membrane electrolyzer. A supervisory control module ensures real-time monitoring and energy flow management, following international safety standards. The study also explores the incorporation of blockchain technology to certify the renewable origin of hydrogen, enhancing traceability and transparency in the green hydrogen market. The experimental results confirm the system’s technical feasibility, demonstrating stable hydrogen production, efficient energy management, and islanded-mode operation with preserved grid stability. These findings highlight the strategic role of hydrogen as an energy vector in the transition to a cleaner energy matrix and support the proposed architecture as a replicable model for industrial facilities seeking to combine hydrogen production with advanced microgrid technologies. Future work will address large-scale validation and performance optimization, including advanced energy management algorithms to ensure economic viability and sustainability in diverse industrial contexts. Full article

This study focuses on electrical stimulation for composting. Using the PSALSAR method, a comprehensive systematic review analysis identified 22 relevant articles. The examined studies fall into four main systems: electric field-assisted aerobic composting (EAAC), electrolytic oxygen aerobic composting (EOAC), microbial fuel cells (MFCs), and thermoelectric generators (TEGs). Apart from the main systems highlighted above, bioelectrochemically assisted anaerobic composting (AnC_{BE, III}) is discussed as an underexplored system with the potential to improve the efficiency of anaerobic degradation. Each system is described in terms of key materials, composter design, operating conditions, temperature evolution, compost maturity, microbial community, and environmental outcomes. EAAC and EOAC systems accelerate organic matter decomposition by improving oxygen distribution and microbial activity, whereas MFC and TEG systems have dual functioning due to the energy generated alongside waste degradation. These innovative systems not only significantly improve composting efficiency by speeding up organic matter breakdown and increasing oxygen supply but also support sustainable waste management by reducing greenhouse gas emissions and generating bioelectricity or heat. Together, these systems overcome the drawbacks of conventional composting systems and promote future environmental sustainability solutions. Full article

Ice formation on critical infrastructure such as wind turbine blades can lead to severe performance degradation and safety hazards. This study investigates the use of hyperspectral imaging (HSI) combined with machine learning to detect and classify ice on various coated and uncoated surfaces. Hyperspectral reflectance data were acquired using a push-broom HSI system under controlled laboratory conditions, with ice and rime ice generated using a thermoelectric cooling setup. Support Vector Machine (SVM) and Random Forest (RF) classifiers were trained on uncoated aluminum samples and evaluated on surfaces with different coatings to assess model generalization. Both models achieved high classification accuracy, though performance declined on black-coated surfaces due to increased absorbance by the coating. The study further examined the impact of spectral band reduction to simulate different sensor types (e.g., NIR vs. SWIR), revealing that model performance is sensitive to wavelength range, with SVM performing optimally in a reduced band set and RF benefiting from the full spectral range. A multiclass classification approach using RF successfully distinguished between glaze and rime ice, offering insights into more targeted mitigation strategies. The results confirm the potential of HSI and machine learning as robust tools for surface ice monitoring in safety-critical environments. Full article

This study investigates the application of additive manufacturing (AM) in fabricating transverse thermoelectric (TTE) composites, demonstrating the feasibility of this methodology for TTE material synthesis. Zinc oxide (ZnO), a wide-bandgap semiconductor with moderate thermoelectric performance, and copper (Cu), a highly conductive metal, were selected as base materials. These were formulated into stable paste-like feedstocks for direct ink writing (DIW). A custom dual-nozzle 3D printer was developed to precisely deposit these materials in pre-designed architectures. The resulting structures exhibited measurable transverse Seebeck effects. Unlike prior TE research primarily focused on longitudinal configurations, this work demonstrates a novel AM-enabled strategy that integrates directional compositional anisotropy, embedded metal–semiconductor interfaces, and scalable multi-material printing to realize TTE behavior. The approach offers a cost-effective and programmable pathway toward next-generation energy harvesting and thermal management systems. Full article

This study focuses on the challenge of short-term economic dispatch in hybrid generation systems, specifically under scenarios where energy constraints arise due to reduced water availability. The primary aim is to compare various generation scenarios to evaluate the influence of renewable energy-based power plants on the overall operating cost of an Electric Power System. The hybrid generation system under analysis comprises hydroelectric, thermoelectric, photovoltaic solar, and wind power plants. The latter two, in particular, play a crucial role, yet their performance is highly dependent on the variability of their primary resources—solar radiation, wind speed, and ambient temperature—which are inherently stochastic. To estimate their behavior, the Monte Carlo method is applied, utilizing probability distribution functions to predict resource availability throughout the planning horizon. Once the scenarios are established, the problem is formulated as a hydrothermal dispatch optimization, which is then tackled using heuristic and metaheuristic approaches, with a strong focus on the Differential Evolution algorithm. Full article

Understanding how crystal structure influences electronic properties is crucial for discovering new functional materials. In this study, we utilized Kernel Principal Component Analysis (KPCA) to classify and analyze the Density of States (DOS) of transition metal sulfide (TMS) compounds, particularly copper-based sulfides. By mapping high-dimensional DOS data into a lower-dimensional space, we identify clusters corresponding to different crystal systems and detect outliers with significant deviations from their expected groups. These outliers exhibit unusual electronic configurations, suggesting potential applications in semiconductors, thermoelectric devices, and optoelectronic devices. Projected Density of States (PDOS) analysis further reveals how orbital hybridization governs the electronic structure of these materials, highlighting key differences between structurally similar compounds. Additionally, we analyze phase stability through inter-cluster distance measurements, identifying potential phase transformations between closely related structures. The implications for this work in terms of modifying chemistries and generalized DOS predictions are discussed. Full article

In this study, the effects of different heat sink designs on the cold side of the modules in a thermoelectric generator (TEG) system placed between the compressor and the intercooler of a turbocharged tractor on the system performance were numerically analyzed. In the current literature, heat sinks used in TEG modules generally consist of plate fins. In this study, by using perforated and slotted fins, the thermal boundary layer behaviors were changed and there was an attempt to increase the heat transfer from the cold surface compared to plate fins. Thus, the performance of the TEG system was also increased. When looking at the literature, it is seen that there are studies which aim to increase the performance of TEG modules by changing the dimensions of p and n type semiconductors. However, there is no study aiming to increase the performance of TEG modules by making changes on the plate fins of the heat sinks used in these modules and thus increasing the heat transfer amount. In this respect, this study offers important results for the literature. According to the numerical analysis results, the total TEG output power, output voltage, and thermal efficiency obtained for S0.5H15 were 6.2%, about 3%, and about 5% higher than those for PF, respectively. In addition, the pressure drop values obtained for different heat sinks, except for aluminum foam, were approximately close to each other. In cases with TEG systems where different heat sinks were used, the intercooler inlet air temperatures decreased by approximately 3.4–3.5% compared to the case without the TEG system. This indicates that the use of TEG will positively affect the improvement in engine efficiency. Full article

Thermoelectric generator (TEG) has emerged as a critical technology for automotive exhaust energy recovery, yet there is still a lack of reviews analyzing automotive TEG structure design and optimization methods simultaneously. Therefore, this review consolidates structure design and methods for improving thermoelectric conversion efficiency, focusing on three core components: thermoelectric module (TEM), heat exchanger (HEX), and heat sink (HSK). For TEM, research and development efforts have primarily centered on material innovation and structural optimization, with segmented, non-segmented, and multi-stage configurations emerging as the three primary structural types. HEX development spans external geometries, including plate, polygonal, and annular designs, and internal enhancements such as fin, heat pipe, metal foam, and baffle to augment heat transfer. HSK leverages active, passive, or hybrid cooling systems, with water-cooling designs prevalent in automotive TEG for cold-side thermal management. Optimization methods encompass theoretical analysis, numerical simulation, experimental testing, and hybrid methods, with strategies devised to balance computational efficiency and accuracy based on system complexity and resource availability. This review provides a systematic framework to guide the design and optimization of automotive TEG. Full article

Conventional methods for transporting biological materials typically use dry ice or ice for preservation but often overlook important aspects of temperature monitoring and metrological control. These methods generally do not include temperature sensors to track the thermal conditions of the materials during transport, nor do they apply essential metrological practices such as regular sensor calibration and stability checks. This lack of precise monitoring poses significant risks to the integrity of temperature-sensitive biological materials. This study presents a statistical analysis of DS18B20 digital temperature sensors used in an experimental refrigeration system based on thermoelectric modules. The aim was to verify sensor consistency and investigate sources of measurement error. The research was motivated by a prior phase of study, which revealed significant discrepancies of approximately 3 °C between experimental temperature data and numerical simulations. To investigate a potential cause, we conducted a case study analyzing measurements from three identical temperature sensors (same model, brand, and manufacturer). Statistical analyses included ANOVA (analysis of variance) and Tukey’s test with a 95% confidence interval. Since the data did not follow a normal distribution (p-value < 0.05), non-parametric methods such as the Kruskal–Wallis and Levene’s procedures were also applied. The results showed that all sensors recorded statistically significant different temperature values (p-value < 0.05). Although experimental conditions were kept consistent, temperature differences of up to 0.37 °C were observed between sensors. This finding demonstrates an inherent inter-sensor variability that, while within manufacturer specifications, represents a source of systematic error that can contribute to larger discrepancies in complex systems, highlighting the need for individual calibration. Full article

A vital procedure for eliminating moisture from the air, dehumidification is necessary for processes like desalination and air conditioning. The Peltier dehumidifier, sometimes referred to as a thermoelectric dehumidifier, removes moisture using the Peltier effect to generate a temperature differential across a Peltier module. Nevertheless, inadequate heat removal from the hot side of the module and a low coefficient of performance (COP) are common problems with Peltier-based dehumidifiers. By combining baffles or turbulators with Peltier plates to increase heat transfer rates, this study overcomes these drawbacks and raises the dehumidifier’s COP and thermal enhancement factor (TEF). On the hot side of the Peltier module, airfoil-shaped baffles are used in the experimental setup to enhance heat dissipation and speed up turbulence. Performance significantly improved, as evidenced by the findings, with the TEF rising to 3.2. Furthermore, the COP improved from 0.06 to 0.45, and the water condensation rate rose to a high of 35 mL per hour. These improvements are ascribed to the higher heat transfer rates made possible by the baffles, which enable the more effective cooling of the Peltier module’s cold side. This study demonstrates how turbulators can increase Peltier-based dehumidifiers’ effectiveness and make them more practical for industrial settings, especially in areas with limited water supplies. According to the results, thermoelectric dehumidification systems can function much better overall if heat transmission on the Peltier module’s hot side is optimized. Full article

This paper presents a novel real-time overload detection algorithm for distributed control systems (DCSs), particularly applied to thermoelectric power plant environments. The proposed method is integrated with a modular multi-functional processor (MFP) architecture, designed to enhance system reliability, optimize resource utilization, and improve fault resilience under dynamic operational conditions. As legacy DCS platforms, such as those installed at the Tae-An Thermoelectric Power Plant, face limitations in applying advanced logic mechanisms, a simulation-based test bench was developed to validate the algorithm in anticipation of future DCS upgrades. The algorithm operates by partitioning function code executions into segment groups, enabling fine-grained, real-time CPU and memory utilization monitoring. Simulation studies, including a modeled denitrification process, demonstrated the system’s effectiveness in maintaining load balance, reducing power consumption to 17 mW under a 2 Gbps data throughput, and mitigating overload levels by approximately 31.7%, thereby outperforming conventional control mechanisms. The segmentation strategy, combined with summation logic, further supports scalable deployment across both legacy and next-generation DCS infrastructures. By enabling proactive overload mitigation and intelligent energy utilization, the proposed solution contributes to the advancement of self-regulating power control systems. Its applicability extends to energy management, production scheduling, and digital signal processing—domains where real-time optimization and operational reliability are essential. Full article