Boost Energy Efficiency Research Articles

Thermal Management Enhancement of Building-Integrated Photovoltaic Systems using Coupled Heat Pipe and Evaporative Porous Clay Cooler

The building-integrated photovoltaic (BIPV) panel systems often lead to a notable decline in PV panels efficiency and lifetime due to their temperature rise because of inadequate cooling. This study investigates the performance of innovative cooling strategy of using a coupled heat pipes and porous clay cooler and compare it with other related three BIPV cooling systems configurations including a conventional BIPV cooling with and without air gap and a pure evaporative porous clay cooling. The aim is to improve the PV panel cooling, ultimately reduce the PV temperature and enhance the overall performance of the BIPV system as well as reducing the building indoor temperature and boosting energy efficiency within the building. The system model, comprising sets of transient equations for the different system configurations, was solved using MATLAB and confirmed with previous experimental findings. The results indicate that comparing with the traditional BIPV system, using BIPV/Clay cooling systems and the hybrid BIPV/Clay-heat pipe cooling systems achieved peak PV temperature reductions of up to 14 °C and 14.7 °C, respectively. Additionally, these cooling methods led to a maximum interior room temperature reduction of approximately 14°C and an average decrease of 8°C. Moreover, the hybrid BIPV/Clay-heat pipe cooling system demonstrates superior performance compared to traditional BIPV system, achieving the highest improvements in PV electrical efficiency, output power, and exergy efficiency, with gains of 7.8%, 6.4%, and 8.4%, respectively. Further, when the hybrid BIPV/Clay-heat pipe cooling system was employed, the clay cooling efficiency and clay exergy efficiency values improved on average by 30.2% and 29.7%, respectively, compared to the BIPV/Clay cooling system in addition to the increase of the lifetime of the PV panels due to isolating it from the contact with the water/water vapor of the clay cooling system. However, this hybrid approach resulted in a rise in electricity production costs from 0.077 $/kWh to roughly 0.138 $/kWh and extended the payback time from 7.38 years to 13.6 years. But, despite its initial economic drawbacks, the hybrid BIPV/Clay-HP cooling system demonstrates considerable effectiveness and long lifetime compared to other cooling configurations.

Navigating the green shift with innovative techniques in petrochemical emission control.

This analysis delves into the profound influence of the petrochemical sector on carbon emissions, highlighting the insufficient data that impedes the formulation of effective emission reduction strategies. By concentrating on the unique challenges within this industry, the study employs a sophisticated decision-making model to explore the complex interconnections between carbon emissions, mitigation approaches, and investment choices. The primary objectives include boosting energy efficiency, refining CO2 reduction initiatives, and cutting associated costs. To navigate the intricacies of carbon footprint assessment, the research introduces a novel hybrid framework that merges scientific modeling with a code-based prototype to aid in strategic planning. The efficacy of this approach is validated by experts, demonstrating its versatility and adaptability through CNHS mapping, which enables the customization of emission reduction strategies across different scenarios.

AI-Driven Numerical Optimization for Carbon Footprint Reduction and Sustainable Supply Chain Management in the Fashion Industry

This study explores how AI-driven optimization can help lower the carbon footprint of the fashion industry's supply chain. By using advanced AI models, the research delves into sustainable strategies across core areas—sourcing, production, logistics, and retail—focusing on reducing emissions, energy use, water consumption, and waste. Techniques like linear programming, genetic algorithms, and reinforcement learning showcase how AI can bring about real, measurable environmental benefits. The findings reveal that these optimized processes boost energy efficiency, save water, and cut down waste, making them valuable tools for sustainable supply chain management. The study wraps up with practical recommendations for adopting AI-based models to help the fashion industry embrace sustainability more effectively.

Transforming wastewater-derived algae biodiesel with ammonium hydroxide emulsion for enhanced energy efficiency and emission reduction

The growing demand for sustainable energy has prompted a search for alternative fuels, with microalgae-based biodiesel emerging as a promising option. However, improving its energy efficiency and minimizing environmental impacts remain key challenges. This study presents a novel approach by integrating wastewater-derived algae cultivation with ammonium hydroxide emulsion to enhance biodiesel performance. Chlorella vulgaris microalgae were grown in a medium of diluted human urine, where nutrient removal efficiency was evaluated, and biooil was extracted via solvent extraction. The resulting biodiesel was analyzed for elemental, chemical, and physicochemical properties. A 30 % blend of Chlorella vulgaris algae biodiesel (CVAB) with normal diesel fuel (NDF) was prepared. To further boost energy efficiency and emissions performance, ammonium hydroxide emulsion was added at concentrations of 5 % and 10 %. Results confirmed that wastewater-sourced algae cultivation is a viable method for sustainable biodiesel production. While CVAB's combustion characteristics were similar to NDF's, it exhibited a 7 % decrease in brake thermal efficiency and a 5 % increase in nitrogen oxide emissions. The addition of ammonium hydroxide emulsion notably improved both energy efficiency and emission profiles. This study highlights a breakthrough in using ammonium hydroxide emulsion to enhance algae biodiesel, making it a more competitive alternative to fossil fuels. Economically, this approach offers a dual advantage: utilizing low-cost wastewater for biodiesel production while improving fuel performance and reducing emissions.

Research on Intelligent Energy System and Power Metering Optimization Based on Multi-objective Optimization Decision Algorithm

In this paper, the intricate problem of optimizing power metering within an intelligent energy system, utilizing a multi-objective optimization decision-making algorithm, is thoroughly explored. Given the current energy landscape, achieving efficient energy utilization and environmental sustainability has become a focal point of research. As a pivotal aspect of future energy management, the precision and optimization of power metering in intelligent energy systems directly influence the effectiveness and cost of energy consumption. To begin, this paper delves into the fundamental principles and application backdrop of intelligent energy systems, highlighting the significance of power metering in such systems. Subsequently, addressing the multi-objective optimization challenges in power metering, a novel optimization method based on a multi-objective optimization decision algorithm is introduced. This algorithm achieves comprehensive optimization of power metering, encompassing multiple objectives such as power cost reduction, enhanced energy efficiency, and environmental protection. The experimental results underscore the remarkable performance of this algorithm, which not only elevates the precision of power metering but also achieves substantial savings in energy costs and significantly boosts energy efficiency. Furthermore, the algorithm exhibits robust adaptability, making it capable of addressing power metering optimization challenges across diverse scenarios. Finally, this paper discusses the practical application prospects of the optimization algorithm in intelligent energy systems, and points out the direction of future research. The research in this paper provides new ideas and methods for the optimization of power metering in intelligent energy systems, and has important theoretical and practical significance for promoting the intelligence and refinement of energy management.

HOW DOES THE DIGITAL ECONOMY AFFECT ENERGY EFFICIENCY?

As a new way of promoting high-quality economic development, whether the digital economy can enhance energy use efficiency by improving resource allocation is of great practical significance to global energy conservation and emission reduction efforts. This research employs principal component analysis and slacks-based measure data envelopment analysis in a case study of 30 provinces in China between 2014 and 2020 to evaluate the digital economy and energy efficiency. The analysis explores how the digital economy impacts energy efficiency, considering direct impacts, mediating effects, and their nonlinear relationship. The findings indicate a declining trend in China’s energy efficiency before it starts to moderate. The digital economy can boost energy efficiency by encouraging industry upgrades and reducing labor mismatches. The threshold regression model reveals that when the capital mismatch level exceeds 0.8871, the digital economy adds considerably to energy efficiency. The study’s findings confirm the linear and nonlinear energy efficiency impact trajectories of the digital economy, offering useful references for sustainable development policymaking.

Assessing the influence of environmental variables on energy efficiency changes in the provision of drinking water services

Within the context of the water-energy nexus in drinking water provision, it is crucial to evaluate temporal shifts in energy efficiency. Utilizing the Stochastic Nonparametric Envelopment of Data (StoNED) approach, this research estimates longitudinal variations in the energy efficiency of drinking water services integrating multiple environmental variables. The analysis, conducted in England and Wales from 2008 to 2020, indicates that the source and quality of raw water, as well as population density, influenced the energy performance of water utilities. Quantitative findings evidenced marked disparity in energy efficiency among different companies, with an average efficiency of 0.617 and a range from 0.437 to 0.944. The dynamic assessment indicates an average annual enhancement of 1.1% in the sector, predominantly propelled by technical advancements (0.9%) rather than by improvements in efficiency (0.2%). This study emphasizes the critical need for bespoke policies and incentives to boost energy efficiency and support sustainable urban water management.

Impact of Environmental Degradation on Economic Growth: Testing the Environmental

Looking at the contribution of economic activity in the country, it is seen to add robustness to growth and development, but in contrast give birth to greenhouse gases (GHGs), such as CO2 CO2 which is also called carbon dioxid, CH4 which is also called methane, and N2O represent nitrous oxide. The factors influencing Greenhouse gas emissions include rising of population, upward movement of per capita output and consumption, infrastructural development made about infrastructures, human character, and innovation. However, if mitigation efforts are not enough, climate change would most likely lead to a slowdown the upward movement. Thus, the study examined the relationship between economic growth and environmental degradation using the Autoregressive Distributed Lag model and examined the impact of environmental degradation on economic growth in Nigeria from 1990 to 2022 using that lens. Based on the estimated ARDL regression result, environmental degradation as determined by carbon dioxide emissions (kt) and economic growth are negatively correlated. The negative indication indicated that economic growth will decline by roughly -0.455% and -0.893% over the medium and long terms, respectively, as carbon emissions rise. The findings indicated that there is validity to Nigeria’s Environmental Kuznets Curve, which demonstrates how environmental deterioration impedes economic progress. Similarly, a positive indication indicating a one-period lag in carbon emissions showed that, in the short term, economic growth increases by around 0.948% in tandem with rising carbon emissions. Thus, the paper illustrated an inverse U-shaped interaction between environmental degradation and economic growth, with healthy economy initially related with increased emissions. However, the majority of the control variable estimates deviate from theoretical expectations. On the other hand, a positive and statistically significant one-period lagged coefficient of trade openness suggests that trade openness looks to support and encourage economic growth in the short run. Similarly, during the period under consideration in Nigeria, the short-run economic growth is positively and strongly correlated with the one-period lagged coefficient of gross fixed capital formation. The report suggested boosting energy efficiency in the world’s energy mix in order to lower greenhouse gas emissions, which are the main contributors to climate change. Therefore, in order to prevent the development of closed-form relationships that could result in a slowdown in economic growth, Nigerian government agencies that are responsible for implementing national and international environmental rules should proceed with caution.

The impact of digital economy on energy conservation and emission reduction: evidence from prefecture-level cities in China

In the face of global climate challenges and energy crises, China aimed to enhance energy conservation and emission reduction (ECER). This study explored the role of the digital economy in boosting energy efficiency and supporting sustainable development by constructing an index system, applying principal component analysis, and using spatial econometric models to assess the impact of digital economic development in China's prefecture-level cities on ECER. Findings revealed regional development disparities and complex spatial interactions that influence ECER. The digital economy showed potential to reduce emissions after reaching a particular scale, suggesting policy recommendations for balanced growth.

LEACH Protocol with Angular Area Routing: Boosting Energy Efficiency and QoS in Wireless Sensor Networks

LEACH Protocol with Angular Area Routing: Boosting Energy Efficiency and QoS in Wireless Sensor Networks

Pathways towards net-zero emissions in Indonesia's energy sector

This research provides a comprehensive analysis of the transition pathways towards net-zero emissions in Indonesia's energy sector by 2050, focussing on overcoming the country's fossil fuel dependency. Utilising EnergyPLAN and MultiNode simulations across three scenarios: Nationally Determined Contribution, High Electrification and Moderate Electrification, the paper presents thorough scenario development and cross-sectoral analysis to capture future energy systems from both supply and demand sides. It contributes notably by detailing scenario-specific energy demands, such as industry sector energy efficiency and electric vehicle penetration and introducing a novel modelling approach that segments the Indonesian energy system into five regional systems. Modelling results indicate the necessity of transitioning towards 100 % renewable energy, emphasising the central role of electricity in future energy systems and the importance of regional connectivity in achieving net-zero emissions. Results also reveal that the Moderate Electrification scenario offers a cost-effective route to net-zero emissions by reducing energy demand and fully exploiting renewable energy sources. The paper concludes with policy recommendations to boost energy efficiency, accelerate electrification, initiate interconnected regional energy systems, and leverage carbon capture and storage as a supplementary measure.

Aircraft Electrification: Insights from a Cross-Sectional Thematic and Bibliometric Analysis

Electrifying aircraft, a crucial advancement in the aviation industry, aims to cut pollutive emissions and boost energy efficiency. Traditional aircraft depend on fossil fuels, which contribute significantly to greenhouse gas emissions and environmental pollution. Despite progress in electric propulsion and energy storage technologies, challenges such as low energy density and integration issues persist. This paper provides a comprehensive thematic and bibliometric analysis to map the research landscape in aircraft electrification, identifying key research themes, influential contributors, and emerging trends. This study applies natural language processing to unstructured bibliographic data and cross-sectional statistical methods to analyze publications, citations, and keyword distributions across various categories related to aircraft electrification. The findings reveal significant growth in research output, particularly in energy management and multidisciplinary design analysis. Collaborative networks highlight key international partnerships, with the United States and China being key research hubs, while citation metrics highlight the impact of leading researchers and institutions in these countries. This study provides valuable insights for researchers, policymakers, and industry stakeholders, guiding future research directions and collaborations.

Thermal performance study of PCM embedded radiant cooling ceiling assisted by infrared-transparent silicon wafers

Phase change materials embedded radiant chilled ceiling (PCM-RCC) systems are great for cutting peak power demand and boosting energy efficiency, but they might not work as well in really humid areas due to condensation issues. Infrared-transparent silicon wafer-assisted PCM-RCC (ISW-assisted PCM-RCC) is proposed to improve the cooling capacity in this study. A heat transfer model of ISW-assisted PCM-RCC is established and its reliability is verified. Effects of thermal conductivity, phase change temperature, and control strategy on the thermal performance of ISW-assisted PCM-RCC are discussed by using the model. The simulation results show that the thermal conductivity of PCM is recommended to be above 0.6 W/(m·K), and the temperature difference between the lower limit of the selected PCM phase transition temperature range and the chilled water temperature should not be less than 1℃. The maximum cooling power of the system is more than 80 W/m2, 46 % higher than the non-condensing cooling capacity of traditional RCC at 70 % relative humidity conditions. Interestingly, the prolonged cold energy storage strategy at night should be avoided which is not friendly to the energy efficiency of the system. In short, the combination with ISW is an effective means to increase the cooling capacity of the PCM-RCC.

Can Energy-Consuming Rights Trading Policies Help to Curb Air Pollution? Evidence from China

Energy-consuming rights trading policies (ECRTPs) represent a significant institutional innovation for China aimed at achieving the dual control targets of total energy consumption and energy consumption intensity. However, the effectiveness of these policies in curbing air pollution remains uncertain. This study treats ECRTPs as a quasi-natural experiment to empirically analyze their impact on air pollution, utilizing panel data encompassing 277 prefecture-level cities in China covering the period from 2011 to 2021. Analytical methods applied include a Difference-in-Differences model, a mediation effects model, and a triple differences model to explore the effects of ECRTPs on air pollution. The findings reveal that ECRTP can significantly suppress air pollution, and this conclusion remains valid even after conducting robustness tests. Mechanism analysis indicates that ECRTPs suppress air pollution by boosting energy efficiency, advancing industrial structure upgrading, and facilitating technological innovation. Further heterogeneous studies show that ECRTPs have a more pronounced inhibitory effect on air pollution in cities that are economically and socially developed, exhibit greater energy-saving potential, are characterized as resource-based cities, and serve as key regions for the prevention and control of air pollution. The research conclusion provides empirical evidence and policy implications for evaluating the environmental effects of ECRTPs and further improving China’s energy-consuming rights trading system, as well as offering references and guidance for other developing countries to put forward ECRTPs.

NOx Prediction of Supersonic Coherent Jets for Electric Arc Steelmaking Furnace

Supersonic coherent jets are widely in practice in steelmaking processes including electric arc furnaces (EAF). Injecting oxygen through such coherent jets plays a vital role in enhancing the liquid gas mixing and reaction rates leading to boosting energy efficiency. Several experiments and numerical simulations have been carried out to understand the physical phenomenon of the coherent jets and to predict the behavior of the jets. However, the research on pollutant formation in the coherent jets for steelmaking is limited. As the industry transitions toward reducing emissions, prediction of pollutant formation is crucial. This numerical study analyzes both methane shrouding coherent jets and hydrogen shrouding coherent jets and predicts the NOx (oxides of nitrogen) formation. It has been found that NO concentration dominates over N2O and NO2 and has the highest concentration around the jet. Although the NO concentration reaches as high as 1070 ppm at high-temperature region around the jet, it remains below 225 ppm along the fuel inlet axis. Also, it has found that the NOx concentrations increase along the radial direction up to 1.5De\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1.5{\ ext{De}}$$\\end{document} from the jet central line and gradually decreases, whereas the NO concentration peaks at 15De\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$15{\ ext{D}}_{{\ ext{e}}}$$\\end{document} along the jet axis in the downstream direction.

Assessment of HVAC Performance and Savings in Office Buildings Using Data-Driven Method

Enhancing energy efficiency within the building sector is imperative to curbing energy losses, given that this sector alone contributes to over 34% of global energy consumption. Employing a building management system, along with its regular updates, presents a strategic avenue to decrease energy usage, enhance building energy efficiency, and more. Tailored control strategies, aligned with the unique characteristics and usage patterns of each building, are essential for achieving energy savings. This article presents an evaluation of HVAC system efficiency in office buildings, utilizing a data-driven approach coupled with simulations conducted in building performance simulation software. The research explores the control strategy of an office building equipped with a constant air volume HVAC system, featuring a regularly controlled air handling unit. The objective is to boost energy efficiency while striking a balance between occupant comfort and energy consumption. The findings indicate that by analyzing measured data and adjusting the configurable parameters, the energy consumption of buildings can be significantly reduced. The close monitoring of indoor parameters by building operators and making corresponding adjustments to the HVAC system can yield energy savings of up to 16%. Leveraging these insights, this paper suggests integrating data-driven and dynamic simulation methods into building management system models to optimize HVAC systems, enhance energy efficiency, and advance ambitious carbon neutrality objectives.

Advanced Energy Performance Modelling: Case Study of an Engineering and Technology Precinct

The global demand for energy is significantly impacted by the consumption patterns within the building sector. As such, the importance of energy simulation and prediction is growing exponentially. This research leverages Building Information Modelling (BIM) methodologies, creating a synergy between traditional software methods and algorithm-driven approaches for comprehensive energy analysis. The study also proposes a method for monitoring select energy management factors, a step that could potentially pave the way for the integration of digital twins in energy management systems. The research is grounded in a case study of a newly constructed educational building in New South Wales, Australia. The digital physical model of the building was created using Autodesk Revit, a conventional software for BIM methodology. EnergyPlus, facilitated by OpenStudio, was employed for the traditional software-based energy analysis. The energy analysis output was then used to develop preliminary algorithm models using regression strategies in Python. In this regression analysis, the temperature and relative humidity of each energy unit were used as independent variables, with their energy consumption being the dependent variable. The sigmoid algorithm model, known for its accuracy and interpretability, was employed for advanced energy simulation. This was combined with sensor data for real-time energy prediction. A basic digital twin (DT) example was created to simulate the dynamic control of air conditioning and lighting, showcasing the adaptability and effectiveness of the system. The study also explores the potential of machine learning, specifically reinforcement learning, in optimizing energy management in response to environmental changes and usage conditions. Despite the current limitations, the study identifies potential future research directions. These include enhancing model accuracy and developing complex algorithms to boost energy efficiency and reduce costs.

Direct conversion of lignin to liquid hydrocarbons for sustainable biomass valorization

Lignin, a principal biomass component, is a promising resource for manufacturing value-added products, tackling environmental concerns, and fostering sustainable energy alternatives. However, its intricate structure and recondensation reactions pose substantial conversion challenges. This study introduces an innovative one-pot catalytic technique for lignin’s direct conversion into liquid hydrocarbons over a Ru/C catalyst. The method integrates lignin depolymerization and the hydrogenolysis of resultant phenolics, achieving an 84.3 % carbon yield mainly constituting benzene, toluene, xylene, and cycloalkanes. The solvent-free concept in this system is proposed and tested, it streamlines downstream separation and boosts energy efficiency. These findings highlight the viability of repurposing waste lignin into liquid hydrocarbons and high-value chemicals, thereby supporting sustainable biomass utilization advancements.

Electrification reconstruction design and analysis of the coal-to-ethylene glycol process

Abstract Industrial electrification is an important way to reduce greenhouse gas emissions and address the issue of renewable energy consumption. Using coal-to-ethylene glycol as an example, the methodology and techno-economic analysis of integrating renewable energy sources with the chemical industry are examined. The findings demonstrate that if electricity prices decline, a reconfiguring electrification process for coal-to-ethylene glycol that uses renewable energy can lower CO2 emissions by 90.88%, boost energy efficiency by 2.21%, and result in cost savings. The electrification reconstruction in chemical industry can aid in the energy transition.

Single imbalanced domain generalization network for intelligent fault diagnosis of compressors in HVAC systems under unseen working conditions

Effective fault diagnosis of compressors in heating, ventilation, and air conditioning (HVAC) systems is critical to ensure service reliability and boost energy efficiency. HVAC compressors are distributed in different areas and work under heterogeneous conditions, which poses emerging challenges to their data-driven modeling. Most existing methods assume multiple category-balanced source domains for model training. Although domain adaptation and generalization methods have emerged to address the data distribution discrepancies in cross-domain fault diagnosis, limited source domains and imbalanced fault categories across domains still constrain the real-world applicability of data-driven models in HVAC compressor fault diagnosis under unseen working conditions. Therefore, this paper studies a significant fault diagnosis problem named single imbalanced domain generalization (SIDG) and proposes a corresponding network (SIDGNet) for intelligent HVAC compressor fault diagnosis. Specifically, a rare fault diagnosis module combined with focal loss is introduced to tackle the class imbalance problem. To achieve better diagnostic boundaries and resist unknown data distribution discrepancies, joint supervised contrastive learning and adversarial learning with specialized data augmentation are introduced as auxiliary modules to improve the robustness and generalizability of SIDGNet. An improved uncertainty-based dynamic weighting mechanism is developed to intelligently balance the weights of module-specific losses during training, which ensures an efficient and stable optimization process. Extensive SIDG fault diagnosis experiments conducted on HVAC compressors demonstrate the superiority of SIDGNet over existing models in SIDG scenarios.