Energy Consumption Cost Research Articles

Fractional social optimization-based migration and replica management algorithm for load balancing in distributed file system for cloud computing

ABSTRACT Effective management of data is a major issue in Distributed File System (DFS), like the cloud. This issue is handled by replicating files in an effective manner, which can minimize the time of data access and elevate the data availability. This paper devises a Fractional Social Optimization Algorithm (FSOA) for replica management along with balancing load in DFS in the cloud stage. Balancing the workload for DFS is the main objective. Here, the chunk creation is done by partitioning the file into a different number of chunks considering Deep Fuzzy Clustering (DFC) and then in the round-robin manner the Virtual machine (VM) is assigned. In that case for balancing the load considering certain objectives like resource use, energy consumption and migration cost thereby the load balancing is performed with the proposed FSOA. Here, the FSOA is formulated by uniting the Social optimization algorithm (SOA) and Fractional Calculus (FC). The replica management is done in DFS using the proposed FSOA by considering the various objectives. The FSOA has the smallest load of 0.299, smallest cost of 0.395, smallest energy consumption of 0.510, smallest overhead of 0.358, and smallest throughput of 0.537.

A Comprehensive Overview of Network Slicing for Improving the Energy Efficiency of Fifth-Generation Networks.

The introduction of fifth-generation (5G) mobile networks leads to an increase in energy consumption and higher operational costs for mobile network operators (MNOs). Consequently, the optimization of 5G networks' energy efficiency is crucial, both in terms of reducing MNO costs and in terms of the negative environmental impact. However, many aspects of the 5G mobile network technology itself have been standardized, including the 5G network slicing concept. This enables the creation of multiple independent logical 5G networks within the same physical infrastructure. Since the only necessary resources in 5G networks need to be used for the realization of a specific 5G network slice, the question of whether the implementation of 5G network slicing can contribute to the improvement of 5G and future sixth-generation networks' energy efficiency arises. To tackle this question, this review paper analyzes 5G network slicing and the energy demand of different network slicing use cases and mobile virtual network operator realizations based on network slicing. The paper also overviews standardized key performance indicators for the assessment of 5G network slices' energy efficiency and discusses energy efficiency in 5G network slicing lifecycle management. In particular, to show how efficient network slicing can optimize the energy consumption of 5G networks, versatile 5G network slicing use case scenarios, approaches, and resource allocation concepts in the space, time, and frequency domains have been discussed, including artificial intelligence-based implementations of network slicing. The results of the comprehensive discussion indicate that the different implementations and approaches to network slicing pave the way for possible further reductions in 5G MNO energy costs and carbon dioxide emissions in the future.

Enhanced Whale Optimization Algorithm for task scheduling in cloud computing environments

The escalation of cloud services, driven by their accessibility, improved performance, and cost-effectiveness, has led cloud service providers to consistently seek methods to expedite job completion, thereby boosting profits and reducing energy consumption expenses. Despite developing numerous scheduling algorithms, many of these techniques address only a specific objective within the scheduling process. To efficiently achieve better optimization results for the cloud task scheduling problem, a novel approach, the Enhanced Whale Optimization Algorithm (EWOA), is proposed. EWOA integrates the WOA with the Lévy flight. The incorporation of Lévy flight is tailored to broaden the search space of WOA, expediting convergence with adaptive crossover. The EWOA model is simulated using the Cloudsim tool and evaluated under diverse test conditions. The effectiveness of EWOA is assessed by employing various parameters and comparing them with existing algorithms. The results demonstrate that EWOA outperforms other algorithms in resource utilization, energy consumption, and execution cost, establishing its superiority in addressing the complexities of multi-objective cloud task scheduling.

Thermography measurement for bridge displacement in the darkness using power-free target

Vision-based displacement measurement has become increasingly popular in bridge health assessment. However, this technology faces challenge that a complicated power-consuming supply system must be installed in the darkness, making it unable to meet the needs of low energy consumption and low cost. This paper proposes a bridge displacement measurement method using power-free target and thermography to address it. First, a thermography measurement system for bridge displacement in the darkness is developed. The power-free target with a hollowed-out circular feature is adopted to form the contrast to the background, and graphene is coated on the target surface to enhance the infrared emissivity and contrast further. Second, a sub-pixel elliptic center positioning algorithm based on thermography enhancement and data optimization is proposed to improve the accuracy and the ability to resist occlusion. The feasibility of the method is verified by laboratory tests and bridge field tests. When the measurement distance is within 15 m, the results meet the engineering error requirements of 5%, with the prospect of popularization in small and medium-sized bridges displacement measurement.

Rapid Hydrolysis Precipitation of BiOBr Nanosheets for Boosting Photocatalytic N2 Fixation

AbstractIn this paper, BiOBr nanosheets were synthesized through a novel hydrolysis precipitation method, which significantly reduced energy consumption and time cost compared to traditional hydrothermal synthesis methods. The subjected hydrothermal and high‐temperature calcination process promote the generation of surface oxygen vacancies, which inhibited the recombination of photogenerated electron hole pairs and improve the photocatalytic performance of BiOBr. Additionally, by adjusting the addition amount of KOH in the synthesis process, BiOBr can grow towards more complex crystal structure, which also provides a new path for the fabrication of 2D materials.

Thermal comfort in university classrooms in humid subtropical climate: Field study during all seasons

Ensuring students thermal comfort has complex implications involving student performance and energy consumption costs. Existing models may not be suitable for predicting the thermal comfort of university students. This study aimed to compare the thermal comfort responses of university students in air-conditioned and naturally ventilated classrooms located in a humid Brazilian subtropical climate with the analytical and adaptive models. The study was conducted in 28 classrooms of two universities located in a humid subtropical climate in southern Brazil. Environmental variables were measured using a microclimatic station, while personal variables were obtained through student responses in electronic questionnaires between September 2022 and July 2023. In classrooms operating with natural ventilation, the mean thermal neutrality temperatures were 19.8 °C and 21.4 °C in Joinville and São José, respectively. When operating with air-conditioning, the mean thermal neutrality temperatures were 21.8 °C and 22.1 °C in Joinville and São José, respectively. Even when they answered “neutral” on the thermal sensation questionnaire, students reported that they preferred the cooler environment. Classroom occupant densities less than 1.00 did not influence the students’ thermal sensation. In general, the PMV/PPD model was not able to predict the thermal comfort of university students. The adaptive comfort model proved adequate to predict the thermal comfort of students when classrooms were naturally ventilated.

The home health care routing with heterogeneous electric vehicles and synchronization

AbstractThis paper studies the problem of heterogeneous electric vehicles, fast chargers, and synchronized jobs that have time windows in home healthcare routing and scheduling. We consider a problem that aims to establish daily routes and schedules for healthcare nurses to provide a variety of services to patients located in a scattered area. Each nurse should be assigned to an electric vehicle (EV) from a heterogeneous fleet of EVs to perform the assigned jobs within working hours. We consider three different types of EVs in terms of battery capacity and energy consumption. We aim to minimize the total cost of energy consumption, fixed nurse cost, and costs arising from the patients that cannot be served within the working day. We model the problem as a mixed integer programming formulation. We develop a hybrid metaheuristic based on a greedy random adaptive search procedure heuristic, to generate good quality initial solutions quickly, and an adaptive variable neighborhood search algorithm to generate high quality solutions in reasonable time. The hybrid metaheuristic employs a set of new advanced efficient procedures designed to handle the complex structure of the problem. Through extensive computational experiments, the performance of the mathematical model and the hybrid metaheuristic are evaluated. We conduct analyses on the robustness of the metaheuristic and the performance contribution of employing adaptive probabilities. We analyze the impact of problem parameters such as competency requirements, job duration, and synchronized jobs.

Energy optimization of the smart residential electrical grid considering demand management approaches

-Many researchers have investigated the optimal energy scheduling to meet the demand regarding sustainable development issues and the economic and technical indices. This study presents the day-ahead operation of a smart residential distribution electrical grid as a bi-stage-multiple-criteria decision-making modeling. The proposed energy optimization is implemented based on optimal demand management in upper-stage and multiple-criteria decision-making in lower-stage. The multiple-criteria problem is modeled from the viewpoint of the grid's operator to optimize energy consumption costs, power losses and demand side comfort. The optimal demand management in upper-stage is coordinated considering price traffic in the upstream grid. The load shifting approach and load interruption approach are presented as optimal demand management for residential consumers. The operation of demand management by using load shifting and load interruption approaches is done via deferrable loads and clippable loads in smart residential homes, respectively. The proposed energy optimization in both stages by improved sunflower optimization (IFSO) is handled, and the TOPSIS method is proposed for the best trade-off of the multiple-criteria decision-making. The day-ahead energy operation is applied on the 33-bus distribution network to investigate the effectiveness of the residential distribution electrical grid considering obtained results from mathematical modeling and demand management approaches.

Observer-based event-triggered time-varying formation control of linear multi-agent systems with distributed infinite delays

Compared with bounded delays, distributed infinite delays are more general in practical systems. Event-triggered control can effectively reduce energy consumption and communication costs. This paper addresses time-varying formation control of general linear multi-agent systems with distributed infinite delays in both of their inputs and outputs. An observer-based event-triggered formation control protocol considering distributed infinite delays is proposed, which is related to the combined observed information and some formation compensation signals at triggering time instants. By utilizing inequality techniques, the desired time-varying formation can be implemented while Zeno-behavior is excluded. Some numerical simulations are carried out for demonstrating the validity of theoretical results.

Cost-effective data classification storage through text seasonal features

Data classification storage has emerged as an effective strategy, harnessing the diverse performance attributes of storage devices and orchestrating a harmonious equilibrium between energy consumption, cost considerations, and user accessibility. As research on emerging storage media (e.g. Non-Volatile Memory (NVM)) delves deeper, in scenarios characterized by dynamically evolving storage demands, conventional paradigms of rigid classification strategy and Solid-State Drive (SSD) and Hard Disk Drive (HDD) storage architectures fall short of addressing such complex situations. In this paper, we propose an effective data classification storage method using text seasonal features based on the traditional access frequency analysis strategy. First, to procure richer semantic information, we employ external knowledge actualizing the short-text feature expansion. Then, we leverage the ensemble learning stacking method optimized models to improve the accuracy of data classification based on seasonal features. Additionally, following the seasonal feature, we further classify the data into hot, warm, and cold and place them separately on the NVM, SSD, and HDD, which conserves storage energy consumption and operational costs while ensuring the quality of user access. The experimental results demonstrate that data classification accuracy can reach more than 95.10%, and the energy consumption and operating cost can be reduced by more than 30.22% and 8.73%, respectively.

Analysis of Synergistic Effect of Energy Efficiency Improvement and Vocational Competence Development

INTRODUCTION: Growing global energy demand and constant competition for energy resources have made energy efficiency a focus of international attention. At the same time, occupational competency development is crucial for sustained individual and overall economic growth.OBJECTIVES: This study explores the synergistic effects of energy efficiency improvement and occupational competence development to inform policy-making and business strategies.METHODS: A literature review summarises relevant theories and research progress on energy efficiency improvement and occupational capability development. Then, the relationship between energy efficiency improvement and vocational ability development was analyzed in depth using econometric models and a large amount of empirical data.RESULTS: There is a significant positive association between energy efficiency improvement and vocational ability development. Specifically, improving energy efficiency not only reduces the cost of energy consumption and enhances productivity but also helps promote technological innovation and industrial upgrading, which in turn enhances the level of occupational ability of workers. In turn, improving occupational ability further encourages technological innovation and productivity in enterprises, forming a virtuous circle and promoting the sustainable development of the economy.CONCLUSION: This study reveals the synergistic effect between energy efficiency improvement and vocational competence development, emphasizing the critical role of energy efficiency improvement and vocational competence development in promoting economic growth and achieving sustainable development.

Integrating project management with advanced cooling solutions for data centers: A path to enhanced energy efficiency

Data centers play a crucial role in modern digital infrastructure, but their energy consumption and associated carbon footprint are significant concerns. Among the various energy-consuming components of data centers, cooling systems stand out as major contributors. This paper explores the integration of project management principles with advanced cooling solutions to enhance energy efficiency in data centers. By effectively managing projects and implementing innovative cooling technologies, data center operators can achieve significant reductions in energy consumption and operational costs. The integration of project management practices with advanced cooling solutions involves several key steps. First, project management methodologies such as Agile or Waterfall are applied to plan and execute the implementation of advanced cooling technologies. This includes assessing current cooling systems, identifying suitable advanced solutions, and developing a timeline for implementation. Next, advanced cooling solutions such as liquid cooling, containment systems, or free cooling are deployed to reduce the energy required for cooling data center equipment. One of the primary benefits of integrating project management with advanced cooling solutions is enhanced energy efficiency. Advanced cooling technologies can significantly reduce the energy consumed by data center cooling systems, leading to lower operational costs and a smaller carbon footprint. Additionally, the use of project management methodologies ensures that the implementation of these technologies is carried out efficiently and effectively. Furthermore, this integration can lead to improved data center performance and reliability. By optimizing cooling systems, data center operators can ensure that IT equipment operates within optimal temperature ranges, reducing the risk of overheating and downtime. Additionally, the implementation of advanced cooling solutions can future-proof data centers against increasing heat loads from high-density computing equipment. In conclusion, integrating project management with advanced cooling solutions offers a promising path to enhance energy efficiency in data centers. By adopting this approach, data center operators can achieve significant energy savings, reduce their environmental impact, and improve overall operational efficiency.

A Novel Quantitative Evaluation Method for Viscosity Reduction Effect of Heavy Oil Cold Recovery Based on The Area Diffusion Process

After thermal recovery, the emulsification viscosity reduction technology for heavy oil cold recovery exhibits low energy consumption and low cost, making it a promising technology for ongoing heavy oil reservoir development. The evaluation and optimization of viscosity reducer is very important for the cold recovery of heavy oil. Prior research mostly employed the high-speed shear technique to measure viscosity following emulsion formation in order to evaluate viscosity reducers. This method ignores the seepage emulsification process under low dynamic conditions of formation, resulting in the selected viscosity reducer ' qualified but not ideal '. Based on the regional diffusion process, a quantitative evaluation method for cold recovery of heavy oil viscosity reduction is put forward. This method is used to simulate the static emulsification effect of heavy oil under real formation conditions, and quantitative evaluation is carried out. The outcomes demonstrate that, in comparison to the conventional assessment approach, this method is more accurate in selecting a viscosity reducer. Appropriately increasing the concentration of the viscosity reducer can improve the efficiency of viscosity reduction. The viscosity reduction effect improves with reservoir temperature. Heavy oil and viscosity reduction agent may be quickly matched using the prediction model of viscosity reduction in cold recovery of heavy oil developed by orthogonal experiment. At the same time, through field practice, the viscosity reducer chosen by this way has a good oil increase impact on oilfield production.

Application and effect evaluation of membrane bioreactor technology in municipal wastewater treatment

Due to the increasing amount of urban sewage and improved standards in sewage discharge, simple traditional biological sewage treatment fails to meet the needs of municipal sewage treatment. Therefore, to treat wastewater more efficiently, the process of combining biofilm technology with traditional biological wastewater treatment appeared. This paper analyzes the advantages and problems of the combined processes of membrane separation technology and traditional biological sewage treatment technology from and perspective of complementary processes, and puts forward relevant measures to alleviate membrane fouling. The study found that Membrane Bioreactor (MBR) technology occupies a small area, and has a better sewage effluent quality and strong stability, also the sludge output is low. Anaerobic Membrane Bioreactor (AnMBR) technology can increase the effectiveness of removing organic matter and enable resource reuse, which satisfies the demand for low energy consumption. Membrane Aeration Bioreactor (MABR) technology involves bubble-free aeration that greatly improves the quality of sewage effluent, and the production of sludge in MABR technology decreases. Anaerobic ammonium nitrogen membrane bioreactor technology can enhance the efficiency of Anammox bacteria, and the energy consumption and economic cost of this technology are low. Although these membrane combination processes can effectively treat sewage, they can easily be limited by factors such as temperature, microbial communities, and membrane fouling. This paper provides guiding opinions for decision makers in the field of urban sewage treatment so that they can apply appropriate sewage treatment technology to local municipal sewage treatment, promoting the sustainable development of sewage treatment.

Smart Streetlight Energy Saving System Based on mmWave Radar

Streetlights serve as fundamental infrastructure to meet the lighting needs of people on every road. However, their extensive deployment often results in unnecessary energy waste, with many streetlights maintaining high brightness despite minimal usage during the night. This study aims to develop a smart energy-efficient streetlight system that automatically adjusts lighting levels based on the absence of vehicles and pedestrians, detected after a 3-minute countdown. Specifically, the study utilizes mmWave radar to collect point cloud data, which undergoes denoising through Doppler, DBSCAN, and XYZ techniques. Additionally, the mmWave radar assists in training an LSTM model to identify pedestrian pathways. The implementation of the proposed system significantly reduces energy consumption and annual costs by automatically dimming or turning off streetlights in areas with minimal pedestrian activity during nighttime.

Advancements in Bipolar Membrane Electrodialysis Techniques for Carbon Capture.

Bipolar membrane electrodialysis (BMED) is a promising technology for the capture of carbon dioxide (CO2) from seawater, offering a sustainable solution to combat climate change. BMED efficiently extracts CO2 while generating valuable byproducts like hydrogen and minerals, contributing to the carbon cycle. The technology relies on ion-exchange membranes and electric fields for efficient ion separation and concentration. Recent advancements focus on enhancing water dissociation in bipolar membranes (BPMs) to improve efficiency and durability. BMED has applications in desalination, electrodialysis, water splitting, acid/base production, and CO2 capture and utilization. Despite the high efficiency, scalability, and environmental friendliness, challenges such as energy consumption and membrane costs exist. Recent innovations include novel BPM designs, catalyst integration, and exploring direct air/ocean capture. Research and development efforts are crucial to unlocking BMED's full potential in reducing carbon emissions and addressing environmental issues. This review provides a comprehensive overview of recent advancements in BMED, emphasizing its role in carbon capture and sustainable environmental solutions.

A preliminary test for nitrogen recovery potential of nitrogen fixing cyanobacteria and its granules treating wastewater containing different nitrogen species

Nitrogen compounds in wastewater can be regarded as both troublesome pollutants and nutrients/resources that may be reused and recycled. Currently, nitrogen (N) removal from wastewater in most wastewater treatment plants (WWTPs) is implemented by the destructive methods like nitrification/denitrification processes, and little attention has been paid to recovery and recycle of N resource in WWTPs. In this preliminary study, the granulation of cyanobacteria (i.e., Aphanizomenon flos-aquae Ralfs ex Bornet &Flahault) was achieved, and comparison was made in terms of N recovery/fixation potential when treating wastewater containing different N species. During the granulation process, the settleability indicated by sludge volume index at 5 min (SVI5) and biomass concentration varied from initially > 3000 mL/g and 0.2 g/L to 59 mL/g and 1.2 g/L, respectively. This observation suggests biogranulation may be utilized to effectively and simultaneously cultivate and harvest cyanobacteria, greatly reducing energy consumption and operation costs. Both cyanobacteria and its granules were found to remove 57–100 % of NO3–-N in 5 days under N2 recirculation at an initial NO3–-N of 15.0 mg/L. Compared to the seed cyanobacterial cells, after 3-day batch test the granular biomass contained 83 % higher total N content, about 73.3 mg N in comparison to 40.0 mg N for per gram of biomass under the same N2 recirculation condition, 236 % more proteins, and 115 % times higher N fixation capability. Results from this work can provide a promising solution for N recovery/fixation with reduced N loss/emission from WWTPs and then create nitrogen bioeconomy in wastewater industry.

Techno-economic analysis of small-scale ammonia production via sorption-enhanced gasification of biomass

Biomass gasification is a green and efficient pathway for lowering the carbon footprint of ammonia production. Decentralized, small-scale biomass gasification plants (<150 t NH3/day output) are more practical than large plants due to sustainable large-scale biomass supply constraints. Small-scale biomass gasification for ammonia production faces the challenges of high energy consumption and production costs. Process intensification through sorption-enhanced gasification (SEG) integrates steam gasification and in-situ CO2 separation using regenerable CaO sorbents, directly producing hydrogen-rich syngas (∼70 vol%), which streamlines syngas conditioning and increases cost-effectiveness. Two SEG process configurations for small-scale biomass-to-ammonia (∼45000 t of agricultural residues/year) were proposed: SEG process A, which produced low-carbon ammonia without air separation, and SEG process B, which employed an air separation unit to deliver carbon-negative ammonia by capturing CO2. The two processes were compared against a dual fluidized bed gasification (DFBG) process, which generated green ammonia without an air separation, like SEG process A. Of the three cases, SEG process B showed the lowest biomass (2.14 t biomass/t NH3) and water consumption (0.47 t H2O/t NH3) and avoided 2.8 t CO2/t NH3, making it carbon-negative. While the DFBG process and SEG process A had higher energy efficiencies, SEG process B posted the lowest net energy consumption (36.6 GJ/t NH3). Furthermore, SEG process B had the lowest ammonia production cost ($692/t NH3) and payback time (9.4 years), making it the most economically viable. Sensitivity analysis showed that favorable carbon credit policies could boost the economics of SEG process B to match large-scale plants.

Refractory dissolved organic matters in sludge leachate trigger the combination of anammox and denitratation for advanced nitrogen removal

The cost-effective treatment of sludge leachate (SL) with high nitrogen content and refractory dissolved organic matter (rDOM) has drawn increasing attention. This study employed, for the first time, a rDOM triggered denitratation-anammox continuous-flow process to treat landfill SL. Moreover, the mechanisms of exploiting rDOM from SL as an inner carbon source for denitratation were systematically analyzed. The results demonstrated outstanding nitrogen and rDOM removal performance without any external carbon source supplement. In this study, effluent concentrations of 4.27 ± 0.45 mgTIN/L and 5.58 ± 1.64 mgTN/L were achieved, coupled with an impressive COD removal rate of 65.17 % ± 1.71 %. The abundance of bacteria belonging to the Anaerolineaceae genus, which were identified as rDOM degradation bacteria, increased from 18.23 % to 35.62 %. As a result, various types of rDOM were utilized to different extents, with proteins being the most notable, except for lignins. Metagenomic analysis revealed a preference for directing electrons towards NO3−-N reductase rather than NO2−-N reductase, indicating the coupling of denitratation bacteria and anammox bacteria (Candidatus Brocadia). Overall, this study introduced a novel synergy platform for advanced nitrogen removal in treating SL using its inner carbon source. This approach is characterized by low energy consumption and operational costs, coupled with commendable efficiency.

Robust two‐level market coordinated transaction optimal model and benefit allocation strategy for a novel shared‐energy aggregator

AbstractShared‐energy storage (SES) can break the energy interaction barrier between the demand side and the supply side, which is becoming an option for improving the flexibility of energy systems. This study designed a novel structure for the shared‐energy aggregator (SEA), incorporating a microgrid (MG), prosumer and SES. Then, a two‐level market transaction optimization model for SEA, considering the robust stochastic optimization method, is proposed; namely, at the day‐ahead stage, a robust stochastic operation optimization model is constructed to achieve the dispatching plan from the demand side under the objective of achieving the minimum energy consumption cost for the prosumer, and at the real‐time market stage, a robust stochastic operation optimization model from the supply side is constructed to arrange the optimal dispatching strategy under the objective of the maximum aggregator benefits of MG and SES. Finally, an industrial park micro‐grid in Qinghai Province, China, is selected as an example for analysis.