Energy Efficient Techniques Research Articles

DFT insights on structural, electronic, optical and mechanical properties of double perovskites X2FeH6 (X = Ca and Sr) for hydrogen-storage applications

Hydrogen has been delivered as a magnificent alternative clean energy source to alleviate the growing energy dilemma and the harmful consequences of traditional energy sources on the environment and the population. This one-of-a-kind energy-efficient technique involves storing hydrogen in solid-state compounds and then releasing it as needed for utilization in possible energy applications. Metal hydrides have shown to be highly effective hydrogen storage mechanisms in this setting. To improve the efficiency of hydrogen storage and release at standard temperatures and pressures, however, the use of metal hydrides with high gravimetric, poor thermodynamic stability and high kinetics is required. X2FeH6 (X = Ca and Sr) has promising promise for hydrogen-storage applications, with gravimetric hydrogen densities of 4.28 wt% and 2.54 wt% weight, respectively. We used the DFT technique inside the GGA + PBE framework to undertake the first comprehensive study of the structural, electronic, optical and mechanical properties of X2FeH6 (X = Ca and Sr). Structural properties indicate that both the compounds are thermodynamically stable. The Ca2FeH6 and Sr2FeH6 are semiconductors with an estimated indirect band gap of 1.67 eV and 1.37 eV, respectively. Dielectric constants, absorption, reflectivity, energy loss functions and refractive indices are among the optical characteristics determined by the calculations. The results of mechanical properties suggest that both of these hydrides fall within the category of brittle materials. These findings are crucial for the future development of metal hydrogen storage materials.

Towards sustainable commercial-office buildings: Harnessing the power of solar panels, electric vehicles, and smart charging for enhanced energy efficiency and environmental responsibility

The value of reducing energy consumption in buildings has increased worldwide. This is because the consumption of fossil fuels in a building is as much as in other industries, also among buildings, the consumption of commercial-office buildings has a higher energy consumption; Therefore, the adoption of energy efficiency techniques in the construction and operation of buildings will play an essential role in creating sustainable cities in the future, and for this reason, green building standards such as IgCC, ASHRAE 189.1 and LEED are expanding. At the same time the advancement of technologies such as electric cars and solar cells, incredibly transparent solar cells for generating electricity are being witnessed. In this research, based on building energy simulation techniques, a commercial-office building has been investigated based on green building standards, considering the presence of electric cars and transparent solar cells. This research shows that the building with two scenarios of solar panels on the roof will be reduced by 9% and with transparent panels for electricity production by 18%. Also, the return rate of the first scenario is five years and nine months. The return rate of the second scenario is approximately six years and two months, which will decrease to 2 months and 20 days for the first scenario and three years and three months for the second scenario with the implementation of government incentive policies. Another finding was obtained through the intelligentization of electric car charging costs. It showed that if electric car charging stations are used in the building in three different states, including buildings without solar panels, building with the presence of solar panels only on the roof, and building with solar panels on the roof and transparent solar panels on the windows is 10%, 20% and 30% less than the cheapest commercial electric car charging station.

Reinforcement learning for radio resource management of hybrid energy cellular networks with battery constraints

Cellular networks are facing serious economic and ecological challenges due to the exponential increase in mobile traffic. As a promising direction, mobile operators are equipping base stations with renewable energy and battery systems along with energy efficiency techniques. In this paper, we study cellular networks equipped with batteries and powered by renewable energy sources and the Smart Grid. We exploit reinforcement learning to minimize the grid energy cost and maximize the users’ satisfaction considering variable price of grid energy, traffic variation and renewable energy generation. In contrast to existing studies, we take into consideration both heterogeneity of users and degradation of battery. We propose a Q-learning algorithm that decides the best number of active radio resources considering two cases: with and without battery constraints. Simulation results highlight the importance of imposing constraints on the battery operation. When the battery size is large enough, the battery life is extended with negligible degradation in system performance. In addition, while imposing constraints on the battery may lead to performance degradation on the short term, this is compensated on the long term as shown by simulating the system over one year.

Power Reduction Sleep Scheduling Technique for Cloud Integrated Green Social Sensor Network

The wireless sensor network is the maximum appropriate technology nowadays with such awesome applications and areas including Infrastructure tracking, environment tracking, health care tracking, etc. Cloud Computing has fantastic data collecting skills and effective data processing ability. Social Network is a group of People or organizations of human beings with similar intentions. Social Sensor Cloud is one type of expertise-sharing mechanism wherein similar types of human beings can connect. Energy Consumption is nowadays the largest challenge as far as the concern with green environment. Because the battery life of the sensor is so limited, the Social Sensor Cloud must be energy efficient. As a result, this article will concentrate on Energy-Efficient Techniques for the Social Sensor Cloud. According to our findings, findings, the majority of energy-saving measures will cope with not unusual place Parameters including Network Lifetime, Network Work rate, Throughput, Energy, Bandwidth, etc. We will Summarize current Technology and we Will Provide Our Architecture for Energy Reduction in Social Sensor Cloud.

Outage Analysis of UAV-Aided Networks With Underlaid Ambient Backscatter Communications

Ambient backscatter communication is an energy efficient technique for massive Internet of Things (IoT). Combining with flexibly deployed unmanned aerial vehicles (UAVs), the UAV-aided ambient backscatter communication can establish wireless links for isolated IoT nodes efficiently. In this paper, we investigate the outage performance of the UAV-aided air-ground network with underlaid ambient backscatter communications, where the emitted signals from the air-ground link are leveraged as radio frequency (RF) carrier for ambient backscattering. The air-ground channel is modeled as a probabilistic line-of-sight (LoS) channel with Nakagami- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> fading. Then, the ground communication is modeled as a non-line-of-sight (NLoS) channel with Rayleigh fading. For the downlink, we derive the expressions of the outage probabilities of the backscatter link and the air-ground link. In addition, the asymptotic cases of infinite transmit power and infinite fading parameter are analyzed. For the uplink, the outage probabilities of the backscatter link and air-ground are analyzed, with the cases of infinite transmit power and fading parameter discussed. Simulation results show that the analytical results match well with the Monte Carlo results, which verifies the effectiveness of the proposed scheme.

Location Based Power Reduction Cloud Integrated Social Sensor Network

It is great to hear about the advancements in wireless sensor networks and their applications, as well as the integration of cloud computing to enhance data analysis and storage capabilities. Indeed, these technologies have opened up numerous possibilities across various fields, including infrastructure tracking, environmental monitoring, healthcare, and more. The concept of a social sensor cloud, as you mentioned, brings an interesting dimension to this technology landscape by focusing on knowledge-sharing and connecting like-minded individuals or organizations. This could potentially lead to more collaborative and efficient solutions across a wide range of domains. Energy efficiency is a critical consideration in the design and operation of wireless sensor networks and the cloud infrastructure that supports them. The limited battery life of sensors necessitates careful management of energy consumption to ensure optimal functionality and longevity. Sleep scheduling methods are a common technique used to manage energy consumption in these networks. By coordinating when sensors are active and when they are in a low-power sleep mode, energy consumption can be significantly reduced without compromising the network's overall effectiveness. In the context of the Social Sensor Cloud, managing energy efficiency becomes even more crucial due to the shorter battery life of the sensors involved. This is particularly relevant given the growing concerns about environmental sustainability and the need to reduce energy consumption across technological systems. It's clear that your research paper addresses these challenges head-on, by exploring energy-efficient techniques for the Social Sensor Cloud. Sleep scheduling is just one of the many strategies that researchers and engineers are working on to strike a balance between functionality and energy consumption. Other methods might include optimizing data transfer protocols, developing energy-harvesting mechanisms, and enhancing sensor hardware efficiency. As technology continues to evolve, the integration of wireless sensor networks, cloud computing, and social networks will likely pave the way for innovative solutions and transformative applications. Addressing energy efficiency concerns will undoubtedly play a crucial role in ensuring the long-term viability and positive impact of these technologies.

Optimized scheme for cluster head selection and EAMPR routing protocol design in WSN

Wireless sensor networks (WSN) is a popularly emerging technology with several opportunities to sustain in various field that require multipurpose sensor nodes, less energy and non-expensive system. But in the WSN, the radio transmission needs high amount of energy and this creates the critical problem. Hence consumption of energy has to be decreased to extend the network durability. Even though there are so many techniques existing for clustering approach of WSN, they have limitations like increased energy consumption, less delivery rate of data, redundancy and unbalanced network load. Hence, these problems are solved by introducing the energy efficient deep learning techniques for clustering and finding the optimal route. Initially the initialization process of system model is performed with the implementation of energy model. In WSN, energy consumption should be reduced to enhance the QoS and balance the network traffic. Hence clustering method is used to group up the sensor nodes and the optimal cluster head is selected with the proposed technique of hybrid cuckoo search and particle swarm optimization (CSO-PSO). As the CH is chosen, the optimal path of routing data should be found in addition with the procedure of optimization and it is done through the proposed model of Optimization based routing protocol that incorporates the Energy Aware Multi Point Routing (EAMPR) protocol along with the Improved Tuna Search Optimization (ITSO) algorithm. Finally, by the use of ITSO-EAMPR technique the energy consumption will get reduced with the decrease in relative mobility and high stability of nodes would be achieved. The simulations are proceeded and the outcomes are validated. The result obtained is compared with the traditional methods to show the effectiveness of proposed technique. As per the results obtained the proposed ITSO-EAMPR attains maximized PDR and Throughput, higher energy efficiency with extension in lifetime of WSN along with decrease in BER, end-to-end latency as compared to the existing techniques.

ESEIABS: Energy and spectrum efficient hybrid technique for 5G and beyond networks using interference aware beam selection

ESEIABS: Energy and spectrum efficient hybrid technique for 5G and beyond networks using interference aware beam selection

A hybrid RSM-GA-PSO approach on optimization of process intensification of linseed biodiesel synthesis using an ultrasonic reactor: Enhancing biodiesel properties and engine characteristics with ternary fuel blends

The depletion of fossil fuels necessitates the development of sustainable and energy-efficient techniques for biodiesel production. In recent years, cavitation reactors have emerged as a viable alternative to conventional biodiesel synthesis methods due to their superior conversion rates and shorter processing times. These reactors possess a high surface-to-volume ratio and facilitate efficient heat and mass transfer. This study aims to optimize the production of biodiesel from linseed oil using a novel ultrasonic cavitation reactor through a hybrid approach. In order to achieve this, an L50 orthogonal array with five factors and three levels was developed using a Box-Behnken design based on response surface methodology (RSM). These factors included the molar ratio (4:1, 6:1, and 8:1), ultrasonic power (100, 125, and 150 W), temperature (25, 35, and 45 °C), time (3, 6, and 9 min), and ultrasonic frequency (25, 30, and 35 kHz). The parameters were optimized using RSM-based desirability, genetic algorithm (GA), and particle swarm optimization (PSO) approaches. The results indicated that the RSM-based optimization approach outperformed the other methods. The optimal combination of parameters obtained through RSM consisted of molar ratio of 6.58:1, ultrasonic power of 133.65 W, temperature of 37.44 °C, time of 7.71 min, and pulse frequency of 26.29 kHz. This combination resulted in a biodiesel yield of 95.25%. Furthermore, this study explored the impact of different linseed oil methyl ester, octanol, and diesel blends (B10, B20, B30, B10 (O-10), and B20 (O-10)) on engine performance and emission characteristics. The B20 (O-10) blend exhibited significant potential for simultaneously reducing emissions and enhancing engine performance. When used as engine fuel, the B20 (O-10) blend increased brake thermal efficiency (BTE) by 0.848%, decreased brake specific fuel consumption (BSFC) by 0.607%, and decreased CO, HC, and NOx emissions by 18.75%, 6.55%, and 0.72%, respectively, compared to pure diesel at rated power.

Clustering Algorithms and Optimization Strategies in WSNs: A State-of-the-Art Review

Wireless Sensor Networks (WSNs) have emerged as a groundbreaking technology for collecting data on various physical phenomena, offering the potential to revolutionize infrastructure systems. The paper provides an exhaustive exploration of the existing literature on clustering algorithms within WSNs and the diverse optimization techniques proposed for cluster head selection. A comprehensive analysis of various routing protocols, mobility-based solutions, and energy conservation strategies is presented, shedding light on their respective strengths and limitations. The examination encompasses a wide spectrum of approaches, including hierarchical cluster-based routing, mobile sensor integration, and energy-efficient techniques inspired by industry standards like ZigBee. The paper further explores ecological monitoring applications, redundancy reduction mechanisms, and power- efficient hierarchical routing protocols, offering valuable insights into their contributions to the field. This review paper aims to consolidate and synthesize the wealth of knowledge in the domain of clustering algorithms and optimization techniques for WSNs. By assessing the strengths and weaknesses of these methodologies, it seeks to provide a comprehensive resource for researchers and practitioners in the field, ultimately contributing to the advancement of energy-efficient and reliable wireless sensor networks. Keywords— APTEEN, EARP, ECCP, GPS, HEED, LEACH, PEGASIS, TEEN, WSN.

Energy Efficient in Cloud Computing

Abstract: Today, both parallel and distributing computing have became ubiquitous in the forms of clouds and cyber physical systems, The size of cloud data center is growing rapidly. The larger the data center is, the more energy it consumes. Therefore, more and more researchers focus on energy efficient resource management techniques in the data center. In which manage the load factor and cpu utilization. we consider about cloud computing, its working, its problem definition, virtual machine requirements( executing VMs with similar execution time), objectives and future development of cloud computing. This has led to immense usage of energy, power consumption, usage of e-equipment’s and so on. To overcome this, efforts and techniques are reinforced to save energy, power consumption and the usage of equipment’s to save the planet. Energy efficiency is increasingly important for future ICT. To reduce the power consumption of cloud data centers we aim to reduce the efficient usage of power consumed by IT equipments. EEICC attainable by using eco-friendly computing resources.

A Lightweight Energy-Efficient Technique for QoS Enhancement in Urban VFC for Intelligent Transportation System

A Lightweight Energy-Efficient Technique for QoS Enhancement in Urban VFC for Intelligent Transportation System

A Systematic Study of Clustering Techniques for Energy Efficiency in Wireless Sensor Networks

A Systematic Study of Clustering Techniques for Energy Efficiency in Wireless Sensor Networks

Pulsed electric field processing of green tea-infused chardonnay wine: Effects on physicochemical properties, antioxidant activities, phenolic and volatile compounds

Pulsed electric field (PEF) was used to develop a novel Chardonnay wine infused with green tea, where the physicochemical characteristics (pH, titratable acidity, color) were modulated. The PEF processing conditions were optimized via response surface methodology by evaluating antioxidant activity (assessed by DPPH, FRAP and ABTS assays), total free phenolic content (TPC), total free flavonoid content (TFC), and volatile content. The optimum conditions were determined as electric field strength 1.3 kV/cm and pulse number 10. The PEF-processed green tea-infused wines were also analyzed for the phenolic and volatile profiles. A total of 178 phenolics and non-phenolics were identified by HPLC-DAD-ESI-QTOF-MS/MS in the PEF-treated wines. The application of PEF resulted in additional 20 phenolic compounds and 14 non-phenolic compounds absent in either Chardonnay or green tea. Catechin was the most abundant phenolic compound, while the enhanced antioxidant power was mainly driven by epigallocatechin. There were 49 volatiles detected by HS-SPME-GC-MS in the PEF-treated wines, with 15 volatiles as odor-active compounds. Under both artificial saliva spiked and non-spiked conditions, green tea infusion diminished fruity, floral and spicy odors, and intensified citrus and green odors. The PEF treatment (0.1 and 0.5 kV/cm) could impart more intense citrus and green aromas from tea to wine. Saliva addition decreased the release of volatile acids, alcohols, esters and aldehydes, and increased the release of benzenoids and terpenes. This study forecasted a promising future of PEF as a time and energy efficient technique in the production of flavored wine with enhanced functionalities and desired aromas.

IoT-enabled user agency for building sustainability

Buildings are a significant source of greenhouse gas emissions, and many organizations recognize that reducing commercial building-related emissions is crucial to achieving a carbon-neutral future. However, leveraging state-of-the-art building energy-efficiency techniques can be expensive and require installing new equipment, resulting in only a small percentage of energy-efficient commercial buildings. To address this challenge, our paper focuses on empowering building occupants to become informed change-makers that drive building efficiency through an IoT-based solution. By leveraging the data collected from low-cost sensors, we envision creating personalized dashboards and interventions for individuals, fostering energy transparency and empowering people to optimize their space usage while reducing emissions. Moreover, we can leverage human flexibility and flexible environments to further improve space usage and realize energy and emissions reduction opportunities. Thus, by enabling human-building interaction, our approach will be more effective in achieving the full potential of reducing energy and emissions in existing buildings.

Energy Consumption and Economic Growth in India

The study examined the dynamics and quantitative relationship between carbon emission, energy consumption, and economic growth in India using annual data from 1990 to 2019. First, to determine the extent and magnitude of the relationship between variables, this study used unit root tests (ADF &amp; KPSS) where all variables were integrated of order one. Cointegration was confirmed through Johansen &amp; Julesius test for Co-integration and Bounds test, and then Vector Error-Correction modelling was done. The finding of causality demonstrated that carbon emissions were related to economic growth in both the short and long run. Since the growth hypothesis, that is, Environmental Kuznet Curve (EKC), was correct, it can be suggested that India can certainly reduce pollution by accelerating its growth trajectory. This growth pattern should include energy-efficient techniques; hence, more investments in research and innovations are required to take benefit of the inverted U relation of growth with pollution.

Mechanically-reinforced biocompatible hydrogels based on poly(N-isopropylacrylamide) and star-shaped polycaprolactones

The aim of this work was to improve the properties of hydrogels based on poly(N-isopropyl acrylamide) (PNIPAAm) in terms of mechanical features and functionality by combining the polymer with a star-shaped tetrafunctional polycaprolactone (PCL), which was synthesized ad-hoc and introduced into the reaction mixture. The synthesized PCL (coded as PCL-COOH), selected to maintain the biocompatibility of the final material, was designed with a star structure characterized by four strong arms ending with maleic groups to increase the functionalization potential capable of participating in the radical polymerization and adding acid groups to the system. Specifically, various hydrogels were prepared by partially replacing pentaerytritol tetraacrylate (PE-TA), a commercial crosslinker, with increasing concentrations of PCL-COOH in the presence of trihexyl(tetradecyl)phosphonium persulfate (TETDPPS) as non-gas-releasing radical initiator. In addition, frontal polymerization (FP) was employed, a fast and energy efficient technique, potentially capable of promoting the dispersibility of PCL in the polymer matrix.The influence of the star-shaped PCL on the polymerization process as well as on the hydrogel properties in terms of chemical structure, swelling behaviour, thermal and mechanical features was investigated. Indeed, it was found that the presence of PCL allowed the formation of a stable polymerization front up to a concentration of 25 wt%. FT-IR measurements allowed to evaluate the fraction of PCL effectively bonded to the hydrogel structure, while the morphological analysis performed by FE-SEM characterization revealed that the hydrogel pore size tended to decrease as the amount of PCL in the system increased. In addition, the star-shaped PCL was found to affect the material swelling properties, by reducing the swelling ratio without affecting the thermoresponsive behavior. Mechanical tests performed on the neat PNIPAAm hydrogels and on PCL-containing hydrogels, showed a relevant increase in the material stiffness due to the PCL-COOH addition. The DSC characterization results showed a decrease in the glass transition temperature with increasing PCL-COOH content, indicating a partial miscibility of the two polymers likely due to a compatibilization effect of a copolymer formed during the polymerization process. To demonstrate the capability of the hydrogels to retain positively charged molecules, the prepared systems were contacted with solutions containing pararosaniline hydrochloride. The retention capacity as well as the kinetic release were investigated. The measurements evidenced a higher retaining capacity and a lower release rate in the PCL-containing hydrogels. Finally, the biocompatibility and the low citoxicity of the PNIPAAm/PCL hydrogels were confirmed by a cell viability assay using the SH-SY5Y cell line.

Comprehensive performances investigation of a modular hydronic thermal barrier system for low-energy buildings with filler cavity and filling material design

The hydronic thermal barrier is a promising energy-efficiency technique for opaque envelopes of low-energy buildings. A modular hydronic thermal barrier (MHTB) wall with filler cavities and thermal diffusive material is designed to solve the capacity mismatch in the process of heat injection and thermal diffusion. To quantify its dynamic thermal behaviors and the influence of several key variables, a parametric study regarding thermal barrier formation and performance enhancement is carried out. Results show that the inlet velocity range can be divided into high-influence and low-influence regions, and 0.2–0.3 m/s is recommended to achieve a good performance at relatively small flow rates. Besides, heat load at inner surface of MHTB walls in studied orientations and cities can be further reduced by 35.79% and 34.02–35.98% respectively, and primary energy consumption savings of 11.24% and 8.81–11.56% can be achieved under the assumed energy-supply scheme. Furthermore, increasing a:b-value of filler cavities in the range of 1:1–1:3 is effective to form a more continuous and stable thermal barrier zone, heat load at inner surface of MHTB walls can be reduced by 72.89–91.87% at a charging temperature of 22 °C compared with CW wall, and this effect is more obvious at larger pipe spacings or higher charging temperatures. Meanwhile, increasing filler's thermal conductivity in the range of 1-12λ0 also possesses positive effects in enhancing the thermal diffusion in the direction parallel to the wall surface, the maximum further reduction ratio in heat load at inner surface of 42.26% and a further primary energy consumption savings of 13.30% can be obtained at pipe spacing of 300 mm and charging temperature of 22 °C. The results verified the effectiveness of MHTB concepts and could provide useful references for further research and application of MHTB walls.

Advanced data analytics modeling for evidence-based data center energy management

Over the past few decades, the demand for Data Center (DC) services has significantly increased due to the world’s growing need for internet access, social networking, and data storage. Data Centers are among the most energy-intensive businesses, so optimizing IT operations in DC requires energy-efficient techniques. This paper presents AI based modeling strategies for effective energy management with a particular emphasis on DC’s two most energy intensive systems (i.e., cooling and IT systems). This study addresses the issues of IT equipment performance degradation, inappropriate IT room thermal conditions, inefficient workload placement, and excessive energy waste. This research entails the application of machine learning for DC thermal classification, and deployment of deep learning models to predict resource utilization and energy consumption in DC. Furthermore, a comparative analysis is performed with existing relevant methods to demonstrate the effectiveness and accuracy of the proposed AI techniques. The findings of this study also provide evidence-based recommendations for DC efficient energy management.

Environmental sustainability in Vietnam: evaluating the criticality of economic globalisation, renewable energy, and natural resources.

Vietnam's goal of achieving a certain level of decarbonisation by 2030 is difficult despite its awareness of the threat posed by climate change. However, the country is endowed with natural resources and the increasing dependence on the global economy coupled with greater investment in alternative energy sources are some of the factors responsible for economic expansion in recent years. Hence, the question arises "what are the environmental impacts of economic globalisation, economic growth, natural resources, and renewable energy in Vietnam?", which constitutes a major policy problem. In this study, a time series dataset stretching from 1984 to 2019 is employed to scrutinise the impact of economic globalisation, economic growth, natural resources, and renewable energy on Vietnam's CO2 emissions. This goal is achieved by employing the ARDL bounds testing procedure, dynamic ARDL, and spectral Granger-causality test. Moreover, the outcomes from the dynamic ARDL showed that economic globalisation and economic growth lead to environmental deterioration, whereas it is mitigated by renewable energy. Lastly, the outcomes from the spectral Granger-causality test indicate that a feedback causality association exists between CO2 emissions and the regressors, namely economic globalisation, renewable energy, and economic growth, while no causality connection exists between CO2 emissions and natural resources. Hence, we suggest thatactionsfor reducing emissions should involve the implementation of energy-efficient techniques and renewable technologies within the energy value chain.