Enhance Energy Efficiency Research Articles

Multidisciplinary Review of Induction Stove Technology: Technological Advances, Societal Impacts, and Challenges for Its Widespread Use

Induction stoves are increasingly recognized as the future of cooking technology due to their numerous benefits, including enhanced energy efficiency, improved safety, and precise cooking control. This paper provides a comprehensive review of the key technological advancements in induction stoves, while also examining the societal and health impacts that need to be addressed to support their widespread adoption. Induction stoves operate based on the principle of eddy currents induced in metal cookware, which generate heat directly within the pot, reducing cooking times and increasing energy efficiency compared with conventional gas and electric stoves. Moreover, induction stoves are considered an environmentally sustainable option, as they contribute to improvements in indoor air quality by reducing emissions associated with fuel combustion during cooking. However, ongoing research is essential to ensure the safe and effective use of this technology on a broader scale.

Heat recovery from hot water drainage: CFD simulation, energy efficiency and exergoeconomic analysis of concentric tube heat exchangers

This study investigates the performance and economic viability of a heat recovery concentric tube heat exchanger (HR-CTHE) integrated with water heating systems using either electricity or natural gas. The system recovers heat from hot water drainage to preheat cold water, thereby enhancing energy efficiency. Computational Fluid Dynamics (CFD) simulations are conducted, examining cold and hot water drain Reynolds numbers ranging from 1,000 to 20,000, hot water drain inlet temperatures from 303 K to 333 K, diameter ratios from 1.25 to 3, and inner diameters from 0.01 m to 0.05 m. Results indicate that at an inlet temperature of 333 K and a hot water Reynolds number of 20,000, the heat recovery rate reaches approximately 20,000 W, and the preheated cold water temperature can achieve 316 K. Exergetic efficiency is highest at 0.4392 for a diameter ratio of 1.25 and an inner diameter of 0.01 m. Economic evaluations using Net Present Value (NPV) and discounted payback period indicate that coupling HR-CTHE with electric heaters is more attractive, providing higher savings and quicker returns on investment compared to natural gas heaters. For instance, integrating HR-CTHE with electric water heaters yields a discounted payback period as short as 0.2 years. In contrast, for natural gas heaters, the payback period is around 2.2 years under similar conditions. The exergoeconomic analysis further demonstrates that natural gas heaters achieve cost-effectiveness only under high hot water Reynolds numbers and inlet temperatures, while electric heaters show significant benefits at moderate to high Reh. The study concludes with recommendations for optimizing HR-CTHE performance and cost-effectiveness, emphasizing higher hot water inlet temperatures, lower diameter ratios, and appropriate Reynolds numbers for both fluids.

Green Order Sorting Problem in Cold Storage Solved by Genetic Algorithm

This study investigates the efficiency of cold storage warehouses and contributes to sustainable supply chain management by integrating eco-friendly practices into storage operations. In facilities for milk and its derivatives, unregulated order handling significantly increases energy consumption due to frequent door openings in the cooler. To address this challenge, we developed a novel mathematical model aimed at optimizing order sequences and minimizing energy costs, addressing a previously unexplored gap in the literature. A genetic algorithm (GA) was employed to solve this model, with careful consideration of carbon emissions generated during the algorithm’s execution. We utilized the Yates notation, an experimental design technique, to systematically optimize the GA’s parameters, ensuring robust and statistically valid results. This methodology enabled a thorough analysis of the factors influencing energy consumption. The findings enhance energy efficiency in cold storage warehouses, leading to reduced carbon dioxide emissions and fostering sustainable practices within supply chain management. Ultimately, this study successfully integrates green practices into cold storage operations, supporting broader sustainability objectives.

Harnessing Quantum Materials for Enhanced Energy Efficiency and Storage: A Comprehensive Study of Electronic Properties and Performance Optimization

This research explores the exciting potential of various quantum materials to enhance energy efficiency and storage capabilities, filling in a crucial gap in our understanding of their electronic properties and interactions. A major hurdle in this field is the lack of empirical data on key factors such as conductivity, charge mobility, and thermal stability—elements that are vital for optimizing the performance of these materials in energy applications. Our study aims to blend experimental data with computational simulations to create a robust framework for modelling how quantum materials behave under different conditions. This comprehensive approach helps lay a solid foundation for their practical use in energy technologies. We focus on an array of fascinating quantum materials, including topological insulators, transition metal dichalcogenides, and superconductors. To investigate these materials, we employ advanced techniques like scanning tunnelling microscopy and spectroscopy, which allow us to delve deep into their unique properties. While pursuing this research, we do encounter challenges. Variations in sample quality, environmental factors during experimentation, and the complexities of theoretical modelling can affect the consistency of our results and the interpretation of our data. Despite these obstacles, our findings suggest that certain quantum materials hold remarkable potential for enhancing energy storage and conversion systems through improved charge mobility and thermal stability. In fact, some materials have demonstrated conductivity levels that surpass those of traditional options, hinting at transformative applications in energy technologies. These insights highlight the importance of continuing our exploration of quantum materials, positioning them as promising candidates for tackling today’s energy challenges and paving the way for advancements in sustainable energy solutions.

Incorporation of Phase Change Materials in Buildings

This review paper explores the integration of phase change materials (PCMs) in building insulation systems to enhance energy efficiency and thermal comfort. Through an extensive analysis of existing literature, the thermal performance of PCM-enhanced building envelopes is evaluated under diverse environmental conditions. This review highlights that PCMs effectively moderate indoor temperatures by absorbing and releasing heat during phase transitions, maintaining a stable indoor climate. This paper also delves into the detailed concepts of PCMs, including their classification and various applications within building insulation. It is noted that different types of PCMs have unique thermal properties and potential uses, which can be tailored to specific building requirements and climatic conditions. Furthermore, cost–benefit and environmental assessments presented in the reviewed studies suggest that incorporating PCMs into building materials offers significant potential for reducing energy consumption and mitigating environmental impacts. These assessments indicate that PCMs can lead to substantial energy savings by decreasing the reliance on heating and cooling systems, thereby lowering overall energy costs and carbon emissions. However, despite the promising outlook, this review identifies a need for further research to optimize PCM formulations and integration methods. This optimization is essential for overcoming current challenges and facilitating the widespread adoption of PCMs in the construction industry. Addressing issues such as long-term durability, compatibility with existing building materials, and cost-effectiveness will be crucial for maximizing the benefits of PCMs in enhancing energy efficiency and sustainability in buildings. Overall, this review underscores the transformative potential of PCMs in building insulation practices. By providing a comprehensive overview of PCM classifications, applications, and their impacts on energy efficiency and environmental sustainability, this paper lays the groundwork for future advancements and research directions in the field of PCM-enhanced building technologies.

GraphSmart: a method for green and accurate IoT water monitoring

Water scarcity is nowadays a critical global concern and an efficient management of water resources is paramount. This paper presents an original approach for monitoring Water Distribution Systems (WDSs) through Internet of Things (IoT) that involves the integration of multiple sensors placed across the distribution network to accurately measure water flow. To enhance energy efficiency for green monitoring and communication process, we harness the power of graph theory and graph signal processing to represent in a tunable and accurate way the water flow and simultaneously minimize the number of IoT sensors communicating those measurements. We propose a graph model where water flow is represented as signal on graph and we introduce an algorithm, named GraphSmart, designed to reconstruct the graph signal when certain measurements are unknown or missing. Our framework is applied on synthetic realistic environment within the context of LoRaWAN (Long Range Wide Area Network), an infrastructure and protocol designed for ultra-low-power IoT devices. Our findings show that GraphSmart significantly reduces energy consumption while ensuring precise flow estimation. Our research demonstrates high potential for energy-efficient and accurate water flow monitoring, paving the way to improve the management of WDSs and enabling water operators to address water scarcity challenges.

Effect of Potassium on the Co-Combustion Process of Coal Slime and Corn Stover

In this study, the combined combustion characteristics and gaseous product emissions of coal slime and corn stover were compared at different blending ratios. The TG-DTG curves indicate that the optimal performance is achieved when the corn straw blending ratio is 20%. Furthermore, the TG-FTIR coupling results demonstrated an increase in gas species as the blending ratio increased. The composition analysis of ash samples formed at various combustion temperatures using XRD and XRF indicated that a portion of KCl in the fuel was released as volatile matter, while another part reacted with Al2O3 and SiO2 components in the slime to form silica–aluminate compounds and other substances. Notably, interactions between the components of slime and potassium elements in corn stover primarily occurred within the temperature range of 800–1000 °C. These findings contribute to a comprehensive understanding of biomass and coal co-firing combustion chemistry, offering potential applications for enhancing energy efficiency and reducing emissions in industrial processes.

Synthesis and Performance Evaluation of Multialkylated Aromatic Amide Oligomeric Surfactants as Corrosion Inhibitor/Drag Reducing Agents for Natural Gas Pipeline.

Drag reducing agents (DRAs) including amphiphiles and polymers can enhance energy efficiency and transmission volume in natural gas pipelines. However, the correlation between DRA molecular structure and drag reduction efficiency remains unclear. In this paper, the multialkylated aromatic amides (MAA) oligomeric surfactants with different numbers of amide group/n-dodecane chain (from 1 to 3) were first synthesized and characterized. Then, the potential efficiency of MAA as the corrosion inhibitors (CIs)/DRAs for natural gas pipelines was investigated by interfacial activity analysis, film-forming property test, electrochemical polarization curve measurement, and in-door loop test. The results showed that the molecular structure is the key factor influencing the performance of MAA. By increasing the number of amide group/n-dodecane chains, the interfacial activity of MAA improves greatly, thus outstandingly affecting the film-forming property of the MAA on the carbon steel sheet. For MAA-1, the formed film is too thin to cover the surface roughness, and then the corrosion inhibiting (78.64%)/drag reducing (0-2%) rates are the lowest. For MAA-3, the formed film is the thickest and smooth, thus imparting the largest corrosion inhibiting (93.88%)/drag reducing (10-14%) rates to MAA-3. For MAA-2, the formed film is thicker but not smooth, and the corrosion inhibiting (89.88%)/drag reducing (4-6%) rates are intermediate. We speculate that the structure of the MAA greatly influences the adsorption and self-assembly of MAA on the inner pipe wall and then generates different performances. This work is helpful for guiding the development of natural gas DRAs with high efficiency.

Optimizing edge server placement and allocation for enhanced energy efficiency: a multi-objective approach based on decision space and elitism

Optimizing edge server placement and allocation for enhanced energy efficiency: a multi-objective approach based on decision space and elitism

Optimizing economic feasibility of CO2 capture processes with rotating packed bed (RPB): Strategies for scale and modularization

Rotating Packed Bed (RPB) technology is increasingly recognized in the field of carbon capture for its potential to broaden application scopes and significantly reduce capital expenditures by minimizing packing volume. However, the commercialization of RPB-based CO2 capture presents several challenges at the process level, including enhancing energy efficiency, refining equipment design, and achieving scalability. Given the mechanical constraints, designing excessively large RPB units may be impractical. Thus, the economic scalability of RPB-based CO2 capture under practical constraints remains an unresolved issue. This study explores the cost-optimal CO2 capture scale through rigorous process simulation using 30-70 wt% monoethanolamine (MEA) solvents. It optimizes the process design and operating parameters to minimize the specific CO2 avoidance costs, within the bounds of practical RPB design limitations. The findings suggest that a capture scale of 100–200 Tons Per Day (TPD) is cost-optimal when using 50 wt% MEA for flue gases with CO2 concentrations above 14.5 mol%. However, cost-effective RPB design becomes challenging at CO2 concentrations as low as 4 mol%. The analysis suggests that the optimal scale might increase with future advancements in RPB design technologies. Current design constraints necessitate a modular approach as the most economically viable strategy for scaling up RPB-based CO2 capture. This modularization strategy supports the wider adoption of CO2 capture technologies at small- to medium-scales, facilitating industry-wide implementation and sustainable progress by reducing initial investment and allowing for phased scaling.

Application of DEA Models to Develop Strategies for Improving the Energy Efficiency of National Economies: Focusing on SDGs and Best Practices in European Countries

Objective: This study aims to develop a strategy for improving the energy efficiency of national economies through the application of DEA (Data Envelopment Analysis) models. The research focuses on adopting the best practices from European countries in the context of SDGs. Theoretical Framework: The research is grounded in the framework of sustainable development and energy efficiency, focusing on the UN Sustainable Development Goals (SDG 12). DEA models are utilized to assess energy efficiency across various European economies, considering economic, environmental, and energy-related factors. Method: DEA models were applied to assess energy efficiency by analyzing input variables such as energy intensity, CO₂ emissions, and energy consumption. The model used a comparative analysis of 38 European countries, with special attention to the weights (λ) in the VRS model, allowing for variable returns to scale. The data were sourced from Eurostat and the World Bank. Results and Discussion: The results indicated that countries like Ireland and Albania serve as key reference units for Ukraine, highlighting the effectiveness of transitioning to renewable energy sources and reducing dependence on fossil fuels. The analysis demonstrated that inefficient countries could improve their performance by following the strategies of their reference units, specifically in reducing energy consumption and emissions. Research Implications: The study provides practical insights into how less efficient countries, like Ukraine, can enhance their energy policies by adopting successful practices from European nations, ultimately contributing to global sustainability goals. Originality/Value: This research contributes to the literature by offering a structured methodology for enhancing energy efficiency in national economies through DEA modeling. It emphasizes the significance of renewable energy in sustainable development and presents a comparative framework applicable to a broad range of countries.

Estimation of Regional Electricity Consumption Using National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite Night-Time Light Data with Gradient Boosting Regression Trees

With the rapid development of society and economy, the growth of electricity consumption has become one of the important indicators to measure the level of regional economic development. This paper utilizes NPP-VIIRS nighttime light remote sensing data to model electricity consumption in parts of southern China. Four predictive models were initially selected for evaluation: LR, SVR, MLP, and GBRT. The accuracy of each model was assessed by comparing real power consumption with simulated values. Based on this evaluation, the GBRT model was identified as the most effective and was selected to establish a comprehensive model of electricity consumption. Using the GBRT model, this paper analyzes electricity consumption in the study area across different spatial scales from 2013 to 2022, demonstrating the distribution characteristics of electricity consumption from the pixel level to the city scale and revealing the close relationship between electricity consumption and regional economic development. Additionally, this paper examines trends in electricity consumption across various temporal scales, providing a scientific basis for the optimal allocation of energy and the effective distribution of power resources in the study area. This analysis is of great significance for promoting balanced economic development between regions and enhancing energy efficiency.

Quantitative simulation and validation of energy carbon emission efficiency changes in Chinese urban agglomerations

In the context of the global energy dilemma, enhancing energy efficiency has inevitably become a pivotal strategy for mitigating energy waste and promoting sustainable development. Urban agglomerations, as growth poles leading high-quality regional economic development, play a significant role in this process. Within these urban agglomerations, the aggregation of various factors and the substitution of energy resources substantially influence the energy carbon emission efficiency (ECEE) of surrounding cities during their development and maturation phases. This study initially employs a three-stage Data Envelopment Analysis (DEA) to quantitatively analyze the ECEE values of 19 urban agglomerations from 2006 to 2020. It then establishes a methodology for calculating the joint intensity (JI) and joint threshold (JT) of ECEE within these urban agglomerations. Finally, a validation process is executed using MATLAB simulation and verification methodology to fit and authenticate the evolutionary patterns of ECEE in Chinese urban agglomerations. The primary objective of this research is to model the evolution of ECEE, thereby exploring the sustainable development pathways for urban agglomerations in China. The research findings indicate that: (1) The results from the three-stage DEA demonstrate that, after removing the influences of external environmental factors and random errors, the comprehensive technical efficiency (TE) of energy carbon emissions for the 19 urban agglomerations in China experienced a wave-like increase, rising from 0.410 to 0.506 between 2006 and 2020. (2) The calculation results of the constructed JI model indicate that the JT values of ECEE for national-level, regional-level, and local-level urban agglomerations are 1006.92, 226.60, and 160.14, respectively. The national-level urban agglomerations have significantly higher JT values and JI than the other two levels of urban agglomerations. (3) The results of the Matlab simulation verification show that 16 urban agglomerations fit well with the wave-like ascending evolutionary pattern. However, four urban agglomerations exhibit an opposite fitting effect due to the influence of their pillar industries or the small number of cities within these accumulations, making them unrepresentative. Consequently, the evolutionary curve of ECEE in Chinese urban agglomerations generally exhibits a wave-like upward trend over time.

How e-commerce development affects urban carbon emissions: Evidence from China

Under the dual constraints of ensuring economic growth and achieving the "dual carbon" goals, the impact of e-commerce development on urban carbon emissions has become a new frontier in this era. This study utilizes urban panel data from 2008 to 2021 to systematically investigate the specific effects and mechanisms of e-commerce on carbon emissions from both theoretical and empirical perspectives. The research reveals that pilot policies such as the construction of national e-commerce demonstration cities significantly reduce carbon emissions, a conclusion that holds true even after a series of robustness tests. Further exploration indicates that promoting green innovation and enhancing energy efficiency are vital mechanisms through which pilot policies of national e-commerce demonstration city construction drive carbon emission reductions. Regions in the eastern and southern parts of the country, along the Yangtze River Economic Belt, non-resource-based cities, and peripheral cities are better positioned to leverage the digital economy dividend to advance carbon emissions reduction. This study contributes to a deeper understanding of carbon reduction and the social impacts of e-commerce development.

Synergistic Effects of Polypropylene Fibers and Silica Fume on Structural Lightweight Concrete: Analysis of Workability, Thermal Conductivity, and Strength Properties

Structural lightweight concrete (SLWC) is crucial for reducing building weight, reducing structural loads, and enhancing energy efficiency through lower thermal conductivity. This study explores the effects of incorporating silica fume (SF), micro-polypropylene (micro-PP), and macro-PP fibers on the workability, thermal properties, and strength of SLWC. SF was added to all mixtures, substituting 10% of the Portland cement (PC), except for the control mixture. Macro-PP fibers were introduced alone or in combination with micro-PP fibers at volumetric ratios of 0.3% and 0.6%. The study evaluated various parameters, including slump, Vebe time, density, water absorption (WA), ultrasonic pulse velocity (UPV), thermal conductivity coefficients (k), compressive strength (CS), and splitting tensile strength (STS) across six different SLWC formulations. The results indicate that while SF negatively impacted the workability of SLWC mortars, it improved CS and STS due to the formation of calcium silicate hydrate (C-S-H) gels from SF’s high pozzolanic activity. Additionally, using micro-PP fibers in combination with macro-PP fibers rather than solely macro-PP fibers enhanced the workability, CS, and STS of the SLWC samples. Although SF had a minor effect on reducing thermal conductivity, the use of macro-PP fibers alone was more effective for improving thermal properties by creating a more porous structure compared to the hybrid use of micro-PP fibers. Moreover, increasing the ratio of micro- and macro-PP fibers from 0.3% to 0.6% resulted in lower CS values but a significant increase in STS values.

IFKC and Optimized DeTrac Deep Convolutional Neural Network Based Location Prediction in the Mobile IOT

Wireless Sensor Network (WSN) is a noteworthy technological advancement in the Internet of Things (IoT). Due to its severe resource constraints, this network necessitates the development of energy-efficient routing strategies. In the context of the Internet of Things, a cluster-based routing problems frequently struggle with unequal power usage. Therefore, this paper proposes an Improved Fractional Rough Fuzzy K-means Clustering and Optimized DeTraC Deep Convolutional Neural Network based Location Prediction in Mobile Internet of Things (IFKC-ODTN-LP-MIoT) system to address unequal power consumption in cluster-based IoT routing. By proposing Improved Fractional Rough Fuzzy K-means (IF-RFKM) for cluster head selection and an optimized DeTraC Deep Convolutional Neural Network (DT-DCNN) the system aims to enhance energy efficiency for location prediction. Then Adolescent Identity Search Algorithm (AISA) optimizes DT-DCNN parameters for improved accuracy. The experimental results demonstrate significant improvements in Computational efficiency, network lifetime and delay compared to the existing approaches.

Retraction Note: Optimizing optical network longevity via Q-learning-based routing protocol for energy efficiency and throughput enhancement

Retraction Note: Optimizing optical network longevity via Q-learning-based routing protocol for energy efficiency and throughput enhancement

Modeling of Induction Motor Direct Starting with and without Considering Current Displacement in Slot

This article presents a mathematical model of three-phase induction motor (IM) with a squirrel cage rotor and investigates its starting modes. Specifically, two scenarios are considered: direct starting of an IM and direct starting considering the current displacement effect in the rotor slots. Analyzing the starting modes of an IM without the use of automatic control systems is crucial for ensuring reliable, efficient, and safe operation of equipment across various industrial and commercial sectors. Understanding and accounting for the processes occurring during the starting mode of an IM allows for minimizing risks, enhancing energy efficiency, and reducing operational costs. This article details the mathematical modeling methods used for analyzing these starting modes and the results obtained from the modeling. These results were compared with data obtained experimentally, allowing for the assessment of the accuracy and reliability of the proposed model. The conducted research highlights the importance of considering current displacement in the rotor slots for accurate modeling and analysis of induction motor starting modes, particularly in capturing the differences in the amplitudes of the starting current and the faster transition to steady-state operation. Conclusions drawn from the comparison of modeling and experimental data provide valuable insights for the further development of control and operation methods for induction motors.

Improving indoor air quality and cooling efficiency: Indirect dew-point evaporative cooling in South China summers

The escalating impact of global warming and the increase in respiratory illnesses have heightened the demand for fresh air systems. However, the substantial power consumption and environmental harm associated with traditional air conditioning systems conflict with current international aspirations for carbon neutrality. To address these issues, a high-performance and compact counterflow indirect dew-point evaporative cooler have developed. This innovative design eliminates the U-turn of the working air seen in conventional M-cycle coolers, resulting in reduced pressure drop and enhanced energy efficiency. This study evaluates the performance of the newly developed dew-point cooler and explores its potential use in providing cool and fresh air for indoor spaces during the sub-tropical summer season in Zhenjiang city, Jiangsu province, China. The results indicate that despite the relatively low fan efficiency (ranging from 5% to 36%), a high cooling coefficient of performance (COP) between 5.2 and 9.3 was achieved. The cooler has demonstrated its effectiveness in improving both thermal comfort and air quality indoors. In Zhenjiang, the cooler can function as a standalone cooling device in June. However, during the hotter months of July and August, it is more suitable as an efficient pre-cooling device when used in conjunction with a conventional air conditioner. The developed cooler exhibited highly stable performance over a three-month test period, consistently maintaining effective cooling and fresh air supply. This study confirms the potential of the counterflow indirect dew-point evaporative cooler as a sustainable and efficient solution for improving indoor air ventilation and thermal comfort.

Optimizing building energy efficiency with a combined cooling, heating, and power (CCHP) system driven by boiler waste heat recovery

In light of increasing challenges related to fossil fuel consumption, global warming, and rising energy costs, optimizing energy systems has become essential. This study introduces a multi-purpose system designed to capture and utilize waste heat from boiler flue gases at the Tous power plant for simultaneous power generation, cooling, and heating. The system integrates an Organic Rankine Cycle (ORC) for power generation and an Absorption Refrigeration Cycle (ARC) for cooling, with the added function of recovering waste heat to preheat natural gas for the boiler and power plant. The system's performance was rigorously analyzed through energy, exergy, and exergoeconomic assessments. To determine the optimal operating conditions, a multi-objective optimization was conducted using the TOPSIS technique, focusing on critical parameters such as flue gas exit temperature, natural gas inlet temperature, and turbine inlet pressures. The results identified optimal conditions with a flue gas exit temperature of 532.5 K, a natural gas inlet temperature of 285 K, and turbine inlet pressures of 2.54 MPa and 14.62 MPa for the first and second turbines, respectively. Under these conditions, the system achieved an exergy efficiency of 41.1 %, cooling capacity of 133.1 kW, power generation of 210.4 kW, heat transfer to natural gas of 979.6 kW, and operational costs of 8.53 $/h. This study highlights the potential of the proposed system to significantly enhance energy efficiency and reduce operational costs in practical applications, offering a sustainable solution for energy management.