Quantify Energy Consumption Research Articles

A semi-automatic analytical methodology for characterizing the energy consumption of MRI systems using load duration curves.

Magnetic resonance imaging (MRI) scanners are a major contributor to greenhouse gas emissions from the healthcare sector, and efforts to improve energy efficiency and reduce energy consumption rely on quantification of the characteristics of energy consumption. The purpose of this work was to develop a semi-automatic analytical methodology for the characterization of the energy consumption of MRI systems using only the load duration curve (LDC). LDCs are a fundamental tool used across various fields to analyze and understand the behavior of loads over time. An electric current transformer sensor and data logger were installed on two 3T MRI scanners from two vendors, termed M1 (outpatient scanner) and M2 (inpatient/emergency scanner). Data was collected for 1 month (7/11/2023 to 8/11/2023). Active power was calculated, assuming a balanced three-phase system, using the average current measured across all three phases, a 480V reference voltage for both machines, and vendor-provided power factors. An LDC was constructed for each system by sorting the active power values in descending order and computing the cumulative time (in units of percentage) for each data point. The first derivative of the LDC was then computed (LDC'), smoothed by convolution with a window function (sLDC'), and used to detect transitions between different system modes including (in descending power levels): scan, prepared-to-scan, idle, low-power, and off. The final, segmented LDC was used to measure time (% total time), total energy (kWh), and mean power (kW) for each system mode on both scanners. The method was validated by comparing mean power values, computed using the segmented 1-month LDC, for each nonproductive system mode (i.e., prepared-to-scan, idle, lower-power, and off) against power levels measured after a deliberate system shutdown was performed for each scanner (1 day worth of data). The validation revealed differences in mean power values<1.4% for all nonproductive modes and both scanners. In the scan system mode, the mean power values ranged from 29.8 to 37.2kW and the total energy consumed for 1 month ranged from 11106 to 14466kWh depending on the scanner. Over the course of 1 month, the portion of time the scanners were in nonproductive modes ranged from 76% to 80% across scanners and the nonproductive energy consumption ranged from 8010 to 6722kWh depending on the scanner. The M1 (outpatient) scanner consumed 99.9 and 183.9kWh/day in idle mode for weekdays and weekends, respectively, because the scanner spent 23% more time proportionally in idle mode on the weekends. A semi-automatic method for quantifying energy consumption characteristics of MRI scanners was introduced and validated. This method is relatively simple to implement as it requires only power data from the scanners and avoids the technical challenges associated with extracting and processing scanner log files. The methodology enables quantitative evaluation of the power, time, and energy characteristics of MRI scanners in scan and nonproductive system modes, providing baseline data and the capability of identifying potential opportunities for enhancing the energy efficiency of MRI scanners.

Energy consumption estimation and profiling for queries in distributed database systems based on a bottom-up comprehensive energy model

Quantifying energy consumption of database operations is the foundation of building energy-efficient database systems. Existing approaches only focused on stand-alone database servers, while we are interested in modeling energy consumption of database operations in the distributed environments. In this study, we aim at providing an accurate energy consumption model for queries executed in distributed database systems, profiling energy consumption characteristics of both the individual queries and the system as a whole, to guide the design of green database systems and to reveal opportunities for energy-efficient computing. As the execution of a distributed query is a combination of a set of subqueries that decomposed from it, we start from building energy models for individual subqueries by extracting basic operations that can effectively reflect their energy consumption. Then we use a bottom-up measuring and modeling method as the basis to provide a comprehensive energy estimation model for the entire distributed query. To validate the accuracy of the model, we use queries with a variety complexity that generated from three standard benchmarks (TPC-H, SSB, and Sysbench) on real distributed databases. Extensive experimental results show that our solution can achieve a high average accuracy of 94.63% for distributed queries. More importantly, based on these results we further explored energy consumption patterns for distributed queries and presented several important implications. And finally, a significant role of distributed joins has been discovered in saving both idle and dynamic energy cost of the system. We hope that taking advantage of our observations can help readers who wish to substantially improve energy efficiency for distributed database systems.

Simulation of Energy Dissipation in BLDC Motor and Analysis of Speed-Acoustic Characteristics

The hybrid electric vehicle combines dual propulsion systems: one employs the traditional internal combustion engine, while the other utilizes a PMDC or BLDC motor. Generally, BLDC motors exhibit an efficiency of 85% to 90%. Conversely, the PMDC motor inherently achieves an efficiency of 88% under optimal running conditions. Moreover, improving the efficiency of both motors is impeded by their design constraints. Power losses in these motors primarily stem from factors such as heat generation in the rotor (I²R losses), bearing friction, vibrations, and noise. This paper aims to investigate energy loss in a 4 kW BLDC motor manufactured by Bosch, attributable to speed-induced acoustic noise effects. Harmonic analysis of the phase current reveals the presence of odd-order harmonics, specifically those below the 11th order, resulting in current ripples. These dominant harmonic frequencies interact, ultimately generating noise and consuming a certain amount of power. These predominant frequencies correlate with fundamental electrical parameters: current frequency (Hz), RMS current (A), and threshold (%r), all associated with the motor's input power source. The 'K' factor, a parameter dependent on these three electrical factors, is calculated to determine harmonic stability. Variations in stable speeds (ranging from 1280 to 7500 RPM) are used to record 'K' factor readings. Based on IEEE Standard 85-1980 guidelines, motors are classified as either acceptable (OK) or not acceptable (not OK) according to their harmonic stability 'K' factor. Readings for each speed step are captured at a specific predominant frequency, facilitating subsequent comparisons. To validate the experimental findings, MATLAB/Simulink and a Fluke scope meter are employed to conduct power analyses for each motor, thus assessing energy loss attributed to noise. The waveform of the phase current proves instrumental in quantifying energy consumption in the BLDC motor. Notably, higher-frequency noise waves with shorter wavelengths entail greater energy consumption. Evaluating energy loss significantly contributes to the analysis of BLDC motor speed acoustics, serving to inform the design of energy-efficient hybrid electric vehicle models powered by BLDC motors, which are intended for customer service.

Carbon Emission Efficiency and Reduction Potential Based on Three-Stage Slacks-Based Measure with Data Envelopment Analysis and Malmquist at the City Scale in Fujian Province, China

Increased carbon emissions led to extreme weather, global warming, and other environmental problems. In order to control energy input and reduce carbon emissions, this study first combines a three-stage Slacks-Based Measure with Data Envelopment Analysis (SBM-DEA) and uses the Malmquist index to quantify energy consumption at the city scale and the related carbon emission efficiency in Fujian Province for the period 2015–2020. Second, we explore the carbon reduction potential on the city scale from the perspective of improving carbon emission efficiency. Our results demonstrate that (i) the carbon emission efficiency of the nine cities increases overall in the first stage, when technical efficiency approaches the efficiency frontier state and efficiency shortage is mainly caused by the lack of pure technical efficiency. (ii) Regression by stochastic frontier analysis in the second stage reveals that the secondary industry correlates positively at 1% significance with fossil energy consumption and power consumption, indicating that the carbon emission efficiency decreases as the secondary industry increases. (iii) Putian and Xiamen reduced their carbon emission efficiency in the third stage due to (a) the input redundancy of fossil energy and social power consumption and (b) excessive undesirable output carbon emissions. (iv) There were improvements in carbon emission efficiency peaks in 2015, with Longyan, Ningde, and Sanming improving by about 50%. This improvement then decreased up to the year 2020, when the improvement in the carbon emission efficiency of Ningde and Zhangzhou was 6.02% and 9.50%, respectively, and that of all other cities was less than 1%. Therefore, we suggest that carbon emission reduction in the future can be further improved by improving technology, optimizing industrial structure, and various other ways to further improve carbon emission efficiency.

Household energy and comfort impacts under teleworking scenarios via a zoned residential HVAC system

Teleworking is a prevalent example of partial building occupancy and prevents homeowners from using a setback or set up temperature setpoint to save energy. Due to a lack of certainty on how teleworking affects houses, this study aimed to quantify energy consumption and thermal comfort under different teleworking scenarios. Likewise, the effect of HVAC zoning by comparing two-zone houses with one-zone houses is investigated. Three teleworking scenarios across six Canadian climate zones were used to assess two house models with two HVAC zoning configurations. The results of this study indicate that teleworking increases energy consumption by up to 9% in houses. By employing the two-zone house instead of the one-zone house, energy performance benefits from using two thermostats in a house. The results show that two-zone HVAC systems can reduce energy consumption by up to 31% and thermal discomfort by up to 24% compared to traditional one-zone houses.

An approach for VLE model development, validation, and implementation in Aspen Plus for amine blends in CO2 capture: the HS3 solvent case study

Carbon Capture and Storage (CCS) using chemical absorption is a viable method to significantly cut CO2 emissions in the industrial and energy sectors. However, further development of improved absorbents is necessary to reduce the costs and environmental impact of current CCS technologies. To design the process and quantify energy consumption and costs through process simulation, it is necessary to implement an accurate and robust thermodynamic model. This article describes in details how to develop, regress, and validate a VLE model using ELECNRTL model in Aspen Plus V11 for the novel HS3 solvent, a blend of 3-amino-1-propanol and 1-(2-hydroxyethyl) pyrrolidine, which is currently being characterized in Realise (H2020-funded project). The VLE model is validated over a wide range of temperatures and loadings. Deviations on CO2 partial pressures and heat of absorption are lower than 15% and 8%, respectively. The proposed procedure to regress ELECNRTL parameters can be used as a general guideline for implementing VLE models in Aspen Plus for generic amine blends or electrolyte solutions.

The influence of built environment and socio-economic factors on commuting energy demand. A path analysis-based approach

Transport is the second energy consumer sector after housing in Algeria. In this article, we explore the ener- gy implication of commuting by considering a panel of socio-economic (SE) and built environment (BE) driving factors. The method is based on four steps: (i) The first step is to identify the main and potential drivers from the literature review and to propose a model that summarises the main assumptions that could explain the volume of commuting and the resulting energy consumption. (ii) In the second step, we designed and distributed 700 questionnaires in the municipality of Djelfa and retained 184 valid questionnaires in the final study sample. (iii) In the third step, we developed a method adapted to urban areas to quantify energy consumption as a function of the distance travelled, the type and density of occupation by means of transport and the type of fuel. (iv) The fourth step is to check the fit of the hypothetical model with a path analysis-based approach. The model developed identifies 15 factors, of which five have a direct impact and 10 have an indirect impact on the energy consumption of commuting. The model shows that building density and the age of the respondent can reduce the energy consumption of commuting by up to −15% and&#x0D; −12% respectively; whereas the number of cars by housing and the round-trip frequency could increase the energy consumption up to 38% and 27% respectively. Our results suggest a structuring role of the socio-economic characteristics of households in explaining the energy consumption of commuting.

Environmental Impact of Polymer Fiber Manufacture

AbstractThis review focuses on the effects on the environment due to the production of polymer‐solvent solutions and the manufacture of polymeric fibers of thicknesses from a nanometer up to a millimeter using these solutions. The most common polymeric fiber manufacture methods are reviewed based on their effects on the environment, particularly from the use of hazardous materials and energy consumption. Published literature is utilized to analyze and quantify energy consumption of the manufacturing methods electrospinning, phase separation, self‐assembly, template synthesis, drawing and pressurized gyration. The results show that during the manufacturing stage of the lifecycle of polymeric fibers, pressurized gyration is more environmentally efficient primarily due to its mass‐producing features and fast processing of polymeric solutions into fibers, it also works best with water‐based solutions. Further green alternatives are described such as the use of sustainable polymers and solvents to enhance the environmental benefit. Overall, it is shown that the most effective method of curbing the environmental impact of manufacturing polymeric fibers is the use of nontoxic, water‐soluble polymers along with the evasion of toxic solvents.

Research on road transport planning aiming at near zero carbon emissions: Taking Ruicheng County as an example

The rapid development of the urban and rural transport sectors has increased focus on energy and the environment. To support the transport sector in its efforts to achieve carbon neutrality targets, low-carbon road transport planning for specific regions has become a necessity. Using Ruicheng County, Shanxi, China as an example, county-level planning of the road transport sector is conducted in terms of quantifying energy consumption and CO2 emissions; the applicable forms and economic costs of technologies including rooftop photovoltaics and smart chargers in different regions are also discussed. A bottom-up model is built and a scenario analysis is adopted to predict the effects of different policy measures by 2035. Under an aggressive scenario, the demand for fossil fuel will be reduced, while the electricity demand will increase. 91% of CO2 emissions can be reduced. The results show that measures including replacing conventional vehicles with electric vehicles and encouraging public transport could effectively facilitate the road transport sector to gradually approach zero carbon emissions. However, the consumption of diesel by agricultural vehicles is the greatest obstacle to achieving carbon neutrality. Electric vehicles and building-related charging infrastructure can potentially have an important role in the future transport sector and electricity systems.

Multi-aspect engineering properties and sustainability impacts of geopolymer pervious concrete

This study aimed to comprehensively evaluate multi-aspect engineering properties and sustainability impacts of geopolymer pervious concrete. Geopolymer binder was prepared using metakaolin (MK) or fly ash (FA) with granulated blast furnace steel slag (GBFS) and used to fabricate pervious concrete; while conventional pervious concrete was made with Portland cement (PC). The strength and durability results show that the MK based pervious concrete exhibited superior performance in mechanical property (3200 psi compressive and 520 psi splitting tensile strength) and lowest mass loss (3%) after 70 cycles of freeze & thaw test. The FA based pervious concrete had about 10% higher splitting tensile strength and better raveling resistance than the PC based pervious concrete, but its freeze & thaw resistance was the lowest (8% mass loss after freeze & thaw test). The hydraulic conductivity test results show that the porosity was the major influence factor for permeability coefficient. The permeability coefficients increased from 3.5 to 12 mm/s with the increase of porosity from 13.5% to 24.5%. However, the binder type had no obvious effect on the permeability and clogging resistance. In addition, life-cycle assessment (LCA) was applied to quantify energy consumption and greenhouse gas (GHG) emission of pervious concrete with different raw materials. The FA based pervious concrete showed the lowest energy consumption, while the MK based pervious concrete had slightly greater energy consumption than conventional pervious concrete. Compared with PC based one, the GHG emissions of MK and FA based pervious concrete could be reduced by 33% and 53%, respectively.

An Agent-Based Model of Heterogeneous Driver Behaviour and Its Impact on Energy Consumption and Costs in Urban Space

By 2020, over 100 countries had expanded electric and plug-in hybrid electric vehicle (EV/PHEV) technologies, with global sales surpassing 7 million units. Governments are adopting cleaner vehicle technologies due to the proven environmental and health implications of internal combustion engine vehicles (ICEVs), as evidenced by the recent COP26 meeting. This article proposes an agent-based model of vehicle activity as a tool for quantifying energy consumption by simulating a fleet of EV/PHEVs within an urban street network at various spatio-temporal resolutions. Driver behaviour plays a significant role in energy consumption; thus, simulating various levels of individual behaviour and enhancing heterogeneity should provide more accurate results of potential energy demand in cities. The study found that (1) energy consumption is lowest when speed limit adherence increases (low variance in behaviour) and is highest when acceleration/deceleration patterns vary (high variance in behaviour); (2) vehicles that travel for shorter distances while abiding by speed limit rules are more energy efficient compared to those that speed and travel for longer; and (3) on average, for tested vehicles, EV/PHEVs were £233.13 cheaper to run than ICEVs across all experiment conditions. The difference in the average fuel costs (electricity and petrol) shrinks at the vehicle level as driver behaviour is less varied (more homogeneous). This research should allow policymakers to quantify the demand for energy and subsequent fuel costs in cities.

Energy Consumption and Scope 2 Emissions for Fused Deposition Modelling

Additive Manufacturing (AM) is a layer-by-layer manufacturing process and gaining importance for applications in digital manufacturing, repair and remanufacturing. Although there are many studies on life cycle assessment in AM, the existing studies do not focus on quantifying Scope 1 and Scope 2 emissions which are required for reporting to support net-zero pathway. It is timely to model and optimise additive manufacturing processes for the net-zero carbon agenda. This study discusses energy state monitoring and energy studies to quantify and understand the energy consumption and Scope 2 carbon emissions for fused deposition modelling (FDM) based on the evaluation of the energy states of the FDM machine and energy consumption when printing an exemplar product. The proposed approach can assist manufacturers in identifying key modules of the machine design and process responsible for significant energy consumption, quantifying energy consumption of fused deposition and its contribution to Scope 2 emissions.

Quantitative evaluation of the building energy performance based on short-term energy predictions

Building energy prediction is a potential tool for benchmarking the future energy uses of individual buildings. However, inevitable gaps between predicted and actual energy uses and discrepancies between buildings make it challenging to quantify energy consumption. Therefore, this paper proposes a systematic methodology of quantitative building energy evaluation based on short-term energy prediction. First, the 24-h ahead building energy prediction model is developed based on a recurrent neural network via the Multi-Input Multi-Output strategy. Second, the quantitative energy evaluation strategy is proposed to quantify prediction gaps based on the 1-D k-means clustering. Third, case studies are conducted on five real buildings to verify the reliability of the proposed methodology. Results show that the energy prediction models achieved outstanding accuracies. Besides, it is necessary to analyze the absolute percentage error (APE) variation of each time step to deeply understand the building energy performance rather than the overall prediction performance evaluation index, such as CV-RMSE and MAPE. Further, customized energy quantification systems are established for buildings per their specific, individual energy performance. The building energy is quantified by labeling APEs into multiple levels. Moreover, building operation characteristics can be further understood by quantifying energy uses.

Bulk Acoustic Wave Acoustofluidics to Quantify Healthspan in Caenorhabditis Elegans

Across species, a decline in muscular strength is observed during aging, however, a non-invasive in-vivo method to quantify the muscular strength of C. elegans is missing. Here, we developed a method that uses bulk acoustic wave acoustofluidics to confine fully-active C. elegans in an acoustophoretic field within microfluidic channels. C. elegans tries to escape and push against the acoustic force field, and the distance moved against the force field is proportional to its muscular strength. We show how to quantify energy consumption and maximum muscle strength during aging as a highly interpretable healthspan metric.

Energy consumption model of plasma spraying based on unit process life cycle inventory

Plasma spraying is an energy intensive manufacturing process to provide function coatings. As such, energy consumption analysis always is promising for the development of green plasma spraying. In this study, a systematic energy consumption model of plasma spraying was presented considering various working states and processing parameters with unit process life cycle inventory method. Verification showed that energy consumption could be accurately calculated with this model within 5% errors. In light of this model, an L9 Taguchi test was carried out to investigate the relationship between energy consumption and coating quality (i.e., coating thickness, hardness, and porosity) with different processing factors (i.e., power, powder feeding rate, spraying jet velocity, and scanning step). It was indicated that power was the most influential factors for the energy consumption and the following factors were scanning step and spraying jet velocity. Besides, Energy analysis showed that the coating thickness and hardness are closely related to the unit energy consumption with a linear and quadratic function respectively. This work provides a basic guidance for quantifying energy consumption and could be extended for the sustainable development of plasma spraying.

Modeling of electrochemical deionization across length scales: Recent accomplishments and new opportunities

Theoretical models have recently been used to simulate deionization technology by capturing electrochemical processes at atomistic, electrode, and plant length scales in electrodialysis, capacitive deionization using electric double layers, and Faradaic deionization using intercalation materials and redox-active polymers. We review the salient features of such models, identifying their major accomplishments in quantifying energy consumption and ion removal, analyzing the feasibility of large-scale systems, and discovering new electrode materials and understanding their deionization mechanisms. After summarizing strengths and weaknesses of recent modeling strategies, we identify research directions to expand modeling capabilities that can be used to inform electrode material/microstructure design, to assign energy losses to electrode-scale mechanisms, to bridge length scales, and to capture Faradaic kinetic/diffusion processes.

Quantifying Energy Consumption in Household Surveys: An Alternative Device-based Accounting Approach

The exercise of quantifying the energy consumption data assembled through household surveys, either by the recall-based approach or the meter-based approach, remains a challenging task, especially in rural areas of developing countries. In this article, we propose a device-based bottom-up accounting method for estimating household energy consumption. This method provides microlevel disaggregated estimates at the intensive margin and documents other difficult-to-measure energy consumption such as biomass at the extensive margin. Even though measurement errors of the household survey might still exist, the structured questionnaire of daily routine behavior questions should greatly alleviate the problem. The new method supplements the existing household energy statistical system, improves its flexibility, and is particularly applicable in developing countries and/or rural areas. We apply the method to a Chinese rural household survey and discuss its differences and similarities with the conventional methods.

A Heuristic Transferring Strategy for Heterogeneous-Cached ICN

The in-network caching is a considerably significant feature of Information-Centric Networking (ICN), especially the heterogeneous-cached ICN has been widely investigated since it accords with the actual network deployment. For the heterogeneous-cached ICN, although there have been many proposals to improve network performance, it is very difficult for these approaches to reach the optimal network performance with multiple metrics consideration. Therefore, in this paper, we propose a heuristic transferring strategy which selects some Content Routers (CRs) and combines them to facilitate the optimal network performance under a constrained total cache budget. At first, we quantify energy consumption, CR load, cache hit ratio and throughput, because the optimal network performance depends on four objects, i.e., minimizing energy consumption and CRs load as well as maximizing cache hit ratio and throughput. Then, based on the given network constraints and objects, we convert the CR transferring problem into 0-1 Knapsack Problem (KP01). Finally, in order to effectively obtain the optimal solution, we propose a heuristic approach based on Ant Colony Optimization (ACO) and expectation efficiency to solve KP01. The simulation is driven by the real YouTube dataset from campus network measurement over GTS and Deltacom topologies, and the experimental results demonstrate that the proposed strategy is more efficient compared to three baselines.

A MOO-EDS Approach for Quantifying Energy Consumption and CO2 Emissions for Manufacturing System Design and Evaluation

A MOO-EDS Approach for Quantifying Energy Consumption and CO2 Emissions for Manufacturing System Design and Evaluation

Energy modeling and eco impact evaluation in direct metal laser sintering hybrid milling

This paper presents an analytical model of energy consumption for Direct metal laser sintering hybrid milling (DMLS-HM) additive manufacturing (AM) of stainless steel 316L. The model is used to quantify energy consumption during production of a defined geometry with DMLS-HM process and compared with energy consumption in electron beam melting (EBM) and conventional machining (CM). The solid-envelope ratio (α) was used to quantify energy consumption and eco impact of the three manufacturing processes on three different geometry models. The Gabi database and other published literature were used to establish energy consumption during primary metal production and material shaping processes. It was found that solid-envelope ratio (α) has more impact on the energy model of the additive processes than on the subtractive machining. On average, the percentage change in α is equal to the percentage change in energy consumed by DMLS-HM and EBM. The CM process had very little average change of 1.5% compared to the major changes in α. The DMLS-HM process showed dominant energy consumption during the part production stage with an average 84% more than EBM and CM processes. However, the CM was dominant in energy consumption during the primary production stage with an average 70% more energy than DMLS-HM and EBM processes. The novel outcomes of this research will contribute to the understanding of basic physics of energy consumption in AM and can be used in setting sustainable manufacturing goals. Moreover, energy consumption in metal AM also influences mechanical properties and microstructure of produced parts, so this work will further enhance prediction of their quality and service life. DMLS-HM is recently being introduced for industrial applications such as mold and tool manufacturing due to its capabilities in building free form and complex shapes that are otherwise challenging to manufacture by conventional methods.