Energy Consumption Factors Research Articles

Experimental Study on Impact Performance of Basalt-Polypropylene Fiber Reinforced High-Performance Concrete.

To enhance the impact resistance of high-performance concrete (HPC), a novel efficient solution was adopted by incorporating basalt fibers (BF) and polypropylene fibers (PF) as reinforcement materials in this work. To this end, the effects of single BF (BHC) and PF (PHC) as well as their combinations (BPHPC) on the impact energy consumption, ductility ratio, and toughness factor were explored through drop weight impact test of concrete considering fiber volume contents (0.1%, 0.15%, 0.2%) to evaluate the impact resistance of the concrete. The Weibull distribution function model is used to fit the drop weight impact test results and predict the probability of failure. Moreover, the fracture-resistance enhancement mechanism of fiber is analyzed at a microscopic level. Test results showed that the number of impacts resisted by the HPC can follow well the two-parameter Weibull distribution. Compared with the single BF and single PF, the combination of 0.15% BF and 0.1% PF yields favorable impact resistance, thus exhibiting a positive hybrid effect.

Enhancing Machinability and Sustainability: The Effects of Hybrid MQL+CO2 Cooling on the Drilling of AA7075T6 with TiO2 and C-Reinforced Composites

This study investigates the impact of various auxiliary cooling techniques on machinability, energy consumption, carbon emissions, and economic factors in the drilling process of AA7075T6 aluminium alloy using TiO2 and C-reinforced composites. The study employed various cooling conditions (dry, MQL, CO2, and hybrid MQL+CO2), with different cutting speeds and feed rates, to evaluate their effects on drilling characteristics. The findings indicated that the combined MQL and CO2 cooling notably enhanced the drilling process by reducing cutting forces by 32% and surface roughness by 65% compared to dry cutting. This synergy between lubrication and cooling significantly improves machinability, resulting in higher-quality machining outputs with smoother surfaces and more precise circularity. Energy analysis revealed that the MQL+CO2 method reduces energy consumption to 64% observed under dry conditions, underscoring its efficiency through better heat dissipation and reduced friction. Furthermore, this method demonstrates a significant reduction in carbon emissions, contributing to environmental sustainability. Economically, although initial costs associated with the implementation of cooling systems are higher, they are offset by reduced tool wear and energy costs, making it a viable solution for sustainable manufacturing practices.

Tourism energy consumption estimation and driving factors of carbon emissions based on LMDI and panel data models

Tourism has had some negative effects while generating positive results. The carbon emissions produced by tourism, which is not a “smokeless industry” in traditional cognition, account for a certain proportion of the global greenhouse gas emissions. Tourism transportation, tourist accommodation, and other tourism activities all contribute to the carbon emission of tourism, and various tourism activities not only stimulate the economy but also increase air pollution. As a big industry, tourism’s growth and development have continuously increased energy consumption, and the pressure on energy conservation and emission reduction has also been greatly aggravated. In this study, the tourism carbon emissions in each province of China were estimated using a “top-down” calculation model, the tourism energy consumption factors were decomposed using a logarithmic mean Divisia index model, and the driving factors of tourism carbon emissions were analyzed through a panel data model. Results show that the tourism carbon emissions in China rapidly increased from 360.74 million tons in 2006 to 853.28 million tons in 2021. The driving factors of tourism energy consumption in China are economic development, energy efficiency, and population, while the inhibiting factors are tourism intensity and energy structure. The per capita GDP, the proportion of the tertiary industry, the turnover of tourists, and the level of urbanization all significantly promote the growth of tourism carbon emissions in China at 1%. The research results are of great significance to the proposal of measures for tourism carbon emission reduction in combination with the situation of various provinces and cities, promoting regional economic development and boosting the development of tourism in China under the background of a low-carbon economy.

An energy-aware cluster-based routing in the Internet of things using particle swarm optimization algorithm and fuzzy clustering

The effectiveness and longevity of IoT infrastructures heavily depend on the limitations posed by communication, multi-hop data transfers, and the inherent difficulties of wireless links. In dealing with these challenges, routing, and data transmission procedures are critical. Among the fundamental concerns are the attainment of energy efficiency and an ideal distribution of loads among sensing devices, given the restricted energy resources at the disposal of IoT devices. To meet these challenges, the present research suggests a novel hybrid energy-aware IoT routing approach that mixes the Particle Swarm Optimization (PSO) algorithm and fuzzy clustering. The approach begins with a fuzzy clustering algorithm to initially group sensor nodes by their geographical location and assign them to clusters determined by a certain probability. The proposed method includes a fitness function considering energy consumption and distance factors. This feature guides the optimization process and aims to balance energy efficiency and data transmission distance. The hierarchical topology uses the advanced PSO algorithm to identify the cluster head nodes. The MATLAB simulator shows that our method outperforms previous approaches. Various metrics have demonstrated significant improvements over DEEC and LEACH. The method reduces energy consumption by 52% and 16%, improves throughput by 112% and 10%, increases packet delivery rates by 83% and 15%, and extends the network lifespan by 48% and 27%, respectively, compared to DEEC and LEACH approaches.

Driving forces and typologies behind household energy consumption disparities in China: A machine learning-based approach

Establishing an intuitive link between driving factors of household energy consumption activities and inequalities is important for the understanding of household heterogeneity in energy consumption behaviours. This paper proposes a novel typology framework based on machine learning approaches and data from 3637 Chinese households in 2014 from 85 cities. Activity-based energy consumption was measured, highlighting inequalities across activities, regions and household types. The results showed significant energy consumption disparities between urban/rural and north/south households, especially in cooking, space heating and vehicle activities. By identifying driving factors of energy consumption, a new household typology classified samples into 6 (all), 6 (urban) and 7 (rural) types. Within these types, households with similar demographic structures, lifestyles and energy consumption habits were clustered. Demographic structure, region, and primary energy demand were used as the basis for the typology. The findings demonstrated how household lifestyle differences explained the cause and underlying driving factors of urban-rural energy consumption inequalities and provided suggestions for city-by-city and type-by-type measurements to support effective low-carbon transformation in cities.

Exploratory data analysis for electric vehicle driving range prediction: insights and evaluation

One of the biggest challenges of electric vehicle (EV) users has been predicting the amount of driving time their vehicles will have on one battery charge. Planning a trip and reducing range anxiety depends on an accurate range estimate. This study aims to anticipate the EV driving range using machine learning methods. In this research, several regression models for predicting EV driving range will be developed and compared. A real-world dataset comprising various factors affecting EV range, such as power, trip distance, energy consumption, driving style, and environmental factors, is used for analysis. The dataset is preprocessed using exploratory data analysis methods to manage missing values, outliers, and categorical variables. The findings of this study contribute to the expanding area of EV range prediction and provide EV buyers, producers, and regulators with insightful information. The user experience can be improved, EV adoption can be boosted, and effective charging infrastructure design is made possible with accurate range prediction. The study also highlights the importance of model selection and data pretreatment in making accurate predictions.

Understanding the influencing factors of energy consumption in China: A dual perspective of geographical space and economic “new normal”

Understanding the influencing factors of energy consumption in China: A dual perspective of geographical space and economic “new normal”

The role of renewable energy and total factor productivity in reducing carbon emissions: A case of top-ranked nations in the renewable energy country attractiveness index

On the one hand, economies, particularly developing ones, need to grow. On the other hand, climate change is the most pressing issue globally, and nations should take the necessary measures. Such a complex task requires new theoretical and empirical models to capture this complexity and provide new insights. Our study uses a newly developed theoretical framework that involves renewable energy consumption (REC) and total factor productivity (TFP) alongside traditional factors of CO2 emissions. It provides policymakers with border information compared to traditional models, such as the Environmental Kuznets Curve (EKC), being limited to income and population. Advanced panel time series methods are also employed, addressing panel data issues while producing not only pooled but also country-specific results.20 Renewable Energy Country Attractiveness Index (RECAI) nations are considered in this study. The results show that REC, TFP, and exports reduce CO2 emissions with elasticities of 0.3, 0.4, and 0.3, respectively. Oppositely, income and imports increase emissions with elasticities of 0.8 and 0.3. Additionally, we show that RECAI countries are commonly affected by global and regional factors. Moreover, we find that shocks can create permanent changes in the levels of the factors but only temporary changes in their growth rates.The main policy implication of the findings is that authorities should implement measures boosting TFP and REC. These factors are driven mainly by technological progress, innovation, and efficiency gains. Thus, they can simultaneously reduce emissions while promoting long-run green economic growth, which addresses the complexity mentioned above to some extent.

Analysis of energy consumption structure in China, the United States and Germany based on forecasting models

This study analyzes and models the energy consumption structure of China, the United States and Germany, and reveals the characteristics, trends and influencing factors of energy consumption by comparing the energy consumption of different countries. The grey GM(1,1), ARIMA, LSTM and GLVM models are used to model and forecast energy consumption, and the model results are evaluated. It is found that there are significant differences in the structure, transformation speed and influencing factors of energy consumption in different countries, which has important implications for formulating energy policies and promoting sustainable development.

Study on the evaluation model of the contribution of power industry expansion considering the influencing factors of new energy consumption

In the application of new energy consumption system engineering, in order to evaluate the contribution of electric power industry expansion, an evaluation model of electric power industry expansion contribution considering the influencing factors of new energy consumption is constructed. In the process of power industry expansion, the growth of new energy installed capacity, power system regulation ability, power grid interconnection and electricity demand are the core factors that affect the change of power contribution to power industry expansion. Using the characteristic extraction method of power consumption behavior of users with industrial expansion, after extracting two characteristics, namely, the utilization hours of user’s industrial expansion capacity and the proportion of new energy load put into operation under the change of four major factors, the monthly industrial expansion power consumption of typical users is predicted by the monthly industrial expansion power consumption forecasting method of users considering industrial expansion capacity, and then the growth curve of user’s industrial expansion power consumption is drawn. Based on the forecast method of monthly industry expansion electricity generated by industry expansion quantity, the industry expansion quantity of typical individual users is calculated, and the industry expansion quantity is input into RBF network model trained by particle swarm optimization algorithm to complete the forecast of monthly industry expansion electricity; Finally, the contribution ratio of each influencing factor is calculated, and the evaluation of power industry expansion contribution considering the influencing factors of new energy consumption is completed. After testing, this model can be used as an available model for evaluating the contribution of electric power industry under the condition of considering the influencing factors of new energy consumption.

What influences the performance of carbon emissions in China?-Research on the inter-provincial carbon emissions' conditional configuration impacts.

The severe global warming issue currently threatens humans' existence and development. Countries and international organizations have effectively implemented policies to reduce carbon emissions and investigate low-carbon growth strategies. Reducing carbon emissions is a hot topic that academics and government policy-making departments are concerned about.Through necessary condition analysis (NCA) and fuzzy set qualitative comparative analysis(fsQCA), this paper investigates local governments' configuration linkage effect and path choice to improve carbon emission performance from six dimensions: energy consumption, industrial structure, technological innovation, government support, economic development, and demographic factors. The research findings include the following: (1) Individual condition does not represent necessary conditions for the government's carbon performance. Among the two sets of second-order equivalence configurations(S and Q) (five high-level carbon performance configurations), those dominated by economic development or low energy consumption can produce high-level carbon performance. Therefore, the six antecedent conditions dimensions work together to explain how the government can create high levels of carbon performance. (2)According to the regional comparison, China's eastern, central, and western regions exhibit similarities and differences in the driving forces behind high carbon emission performance. All three regions can demonstrate carbon emission performance when all the factors are combined. However, when constrained by the conditions of each region's resource endowment, the eastern region emphasizes the advantage of economic and technological innovation, the central region favors government support and demographic factors, and the western region prefers upgrading industrial structure based on a specific level of economic development.

Identification of carbon responsibility factors based on energy consumption from 2005 to 2020 in China

In the process of rapid economic development, the relationship between energy consumption and economic development is close and the coupling relationship is complex. The study proposes a method for the assessment of integrated energy consumption factors. In particular, production and consumption driving factors are expressed in relation to the growth of primary energy consumption responsibility in the energy supply chain system. Specifically, based on previous research, the study builds a LMDI decomposition model based on the primary energy consumption responsibility - embodied energy (PECREE) formula. By analyzing the impact of changes in the final commodity quantity, value-added allocation factor, energy intensity, end-use energy consumption structure and primary energy consumption responsibility conversion factor on primary energy consumption responsibility, the study systematically reveals the linkage relationship between product production and consumption on energy consumption changes, and reveals the related driving mechanism of primary energy consumption responsibility growth. We choose China's energy supply system from 2005 to 2020 as a case study, focusing on the impact of changes in economic sectors on primary energy consumption responsibility, and provide a supplementary explanation from the commodity consumption side. The results show that the change of final use quantity is the most important factor causing the increase of primary energy consumption responsibility, but the impact is decreasing year by year. The impact of energy intensity change on primary energy consumption responsibility is mainly dominated by economic sectors with high energy consumption and high energy intensity. The impact of value-added allocation factor requires systematic consideration of energy intensity of economic sectors. The increase of the proportion of electricity in the end-use energy consumption structure of the economic sector promotes the growth of its primary energy consumption responsibility. The reduction of primary energy consumption responsibility conversion factor for electricity is one of the factors that inhibits the growth of primary energy consumption responsibility. Finally, we believe that while meeting the needs of social development and production and life of the people, China is already on the road of adjustment and transformation of its industrial structure and energy structure, and will continue to make progress in the future.

Optimizing Metal‐Surface Water Disinfection: CFD Study on Microorganism Collision Against a Triply Periodic Minimal Surface

AbstractThis research presents a point‐of‐use (POU) water treatment technology utilizing metal/metallic surfaces based on Triply Periodic Minimal Surface (TPMS) structured filtration infills. Computational Fluid Dynamics modeling using Ansys CFX software is employed to analyze the behavior of Escherichia coli bacteria within a continuous liquid phase moving through the filtration infill, assess particle collision dynamics, and evaluate the efficiency of filtration, considering pressure drop as a fundamental factor in process energy consumption. TPMS infill meshes are coded in a Schwarz P shape using Python, and the Darcy‐Forchheimer equation is employed to determine the permeability and resistance loss coefficient of the infill geometry. The results indicate that the TPMS infill efficiently captures particles while introducing a negligible pressure drop into the system. It is found that a single 40 mm infill configuration is the most efficient, exhibiting a higher collision rate compared to the smaller 20 mm infill configuration and a lower pressure drop compared to the two 20 mm infill configurations in the series. Additionally, this study provides insights into the behavior of continuous fluid flow through TPMS infill in view of scaled‐up implementation, including the presence of recirculation zones that can be exploited to further enhance the collision rate of particles.

Differences in Carbon Intensity of Energy Consumption and Influential Factors between Yangtze River Economic Belt and Yellow River Basin

The Yangtze River Economic Belt and the Yellow River Basin are significant economic and ecological zones in China, contributing over 70% of the nation’s total carbon emissions, crucial for achieving “peak carbon” and “carbon neutrality” targets. This study examines data spanning 2000 to 2020 from 19 provinces, employing time-series analysis and the Theil index to compare carbon intensity variations in energy consumption between the regions. Findings reveal mean Theil index values of 0.0482 and 0.1699 for the Yangtze and Yellow River Basins, respectively. While the Yangtze River basin displays modest carbon intensity differences with remaining intra-basin disparities, the Yellow River Basin exhibits substantial discrepancies, attributed to both inter-basin and intra-basin factors. Our geodetector underscores the significance of government regulation, population size, and economic development in influencing carbon intensity within the Yangtze River Economic Belt, with impact coefficients exceeding 0.75 while carbon intensity in the Yellow River Basin is influenced by population size, energy consumption, and government regulation, with impact coefficients surpassing 0.8. Additionally, interactions among these factors significantly affect disparities in carbon intensity, suggesting a synergistic effect. We propose leveraging key factors from both basins to orchestrate emissions reduction efforts.

Research on Multi-UAV Task Assignment Based on a Multi-Objective, Improved Brainstorming Optimization Algorithm

In response to the practice of rescue channel blocking and a shortage of emergency materials in the event of sudden significant disasters, a multi-UAV collaborative distribution scheme was designed based on the demand for rapid and accurate distribution of materials. This paper constructed a multi-UAV collaborative task assignment and routing problem with simultaneous delivery and pick-up and time windows (MVTARPSDPTW), considering the factors of UAV load, energy consumption, cargo quality, and volume to minimize the total cost of UAV distribution and the full penalty of the task, as well as optimizing the balance of UAV efficiency. This paper proposes a multi-objective, improved brainstorming optimization algorithm based on Pareto dominance (MIBSO) to solve the MVTARPSDPTW problem. With DTLZ4, DTLZ5, and DTLZ6 benchmarks, this work tests the algorithm performance according to the characteristic attributes of the model sought, selecting the four indicators of GD, the Spacing metric, HV, and IGD, concerning convergence, solution distribution, and comprehensive performance. Case validation is based on a COVID-19 scenario in Changchun, China, and the results show that the model algorithm designed in this paper has good performance and feasibility in convergence and distribution of reconciliation. Finally, the multi-UAV emergency material distribution solution provides practical, theoretical support for rescue tasks in sudden significant disasters.

An innovative environmental regulation tool for regional air pollution based on DEA-RFR

Environmental regulation tools are one of the conventional means for pollution control in various countries. For large-scale urbanization regions, reasonable pre-allocation of pollutant emission amount for each area is an effective regulation to improve air quality. Treating pollutant emission quota as a resource element, a comprehensive input-output model has been established based on Data Envelopment Analysis (DEA) method, to evaluate the rationality of pollutant emission allocation and provide adjustment scheme, thus developing an environmental regulation tool. The index system includes factors of economy, environment, energy consumption, demographic and land-use as input indicators, regional GDP and environmental air quality index as output indicators. The regulation tool is applied to the current air pollutant emission management in Henan Province, China. The results show that 7 cities need to further adjust the allocation scheme, among which, Anyang, Xinxiang, Nanyang and Shangqiu are suggested to reduce the emission allocation quotas; Puyang and Zhumadian are suggested to increase the emission allocation quotas; Pingdingshan shall seek optimization paths through collaborative improvement. Further, to verify the effectiveness of the tool, by joint using Random Forest Regression (RFR), a DEA efficiency reassessment is conducted on the obtained new allocation scheme of Nanyang City. The results show that the efficiency of the new allocation scheme has reached optimal. This study provides a practical and effective regulation tool for regional management of pollutant emission, to balance economic growth and environmental protection.

Multi-Objective Optimization of Building Energy Saving Based on the Randomness of Energy-Related Occupant Behavior

Given the escalating global energy demand driven by building energy consumption, this study is dedicated to meticulously investigating efficient energy-saving strategies in buildings, with a keen focus on the impact of occupant behavior’s randomness on energy efficiency and multi-objective optimization. The methodology encompassed a thorough analysis of various energy consumption factors, including building envelope and architectural form. We employed Latin Hypercube Sampling for in-depth sampling studies across each factor’s reasonable range. Utilizing Sobol sensitivity analysis, we pinpointed variables of high sensitivity and embarked on multi-objective optimization targeting two primary indicators: energy consumption and thermal comfort. Leveraging the NSGA-II algorithm, we adeptly identified optimal solutions, culminating in the proposition of building energy-saving strategies anchored on the Pareto frontier. Through stochastic modeling simulations of occupant behavior in window opening and air conditioning usage, a comparison was made with models that do not consider occupant behavior. It was found that incorporating occupant behavior into energy-saving designs can reduce energy consumption by up to 20.20%, while ensuring thermal comfort. This approach can achieve improved energy efficiency and indoor comfort.

Comparison of energy and environmental performance between warm and hot mix asphalt concrete production: A case study

The use of additives to produce warm bituminous mixtures in asphalt pavements gives the possibility to decrease temperatures with positive implications on energy consumption, and on emissions of greenhouse gases and airborne pollutants. This study investigates the changes in energy and environmental performance of an asphalt plant switching from hot to warm asphalt concrete production. A full-scale trial section was constructed in an Italian motorway with recycled bituminous mixtures containing SBS polymer modified bitumen. Reference hot mix asphalts (HMAs) mixed at 170 °C were compared with warm mix asphalts (WMAs) mixed at 130 °C with different chemical additives. Calculations and analysis were performed considering three production phases during which the production technologies (HMA and WMA) and the mixture types varied in temperatures, mixing duration, and quantity of virgin materials employed. Energy performance was calculated through values provided by the asphalt plant operator and thermodynamic equations. Emissions of CO2 were calculated based on energy consumption and emission factors reported in the literature for the Italian energy mix. Airborne pollutants were measured at the stack of the dryer drum. The results showed that for each mixture type, a reduction of 40 °C in the production temperature corresponds to 15% lower thermal energy values for the drying/heating of the aggregates, with consequent lower consumption of fuel oil. The drying/heating of aggregates for WMA lead to lower emissions of particulate matter, NOx, and VOCs compared to HMA.

Estimating of energy consumption of electric vehicles under different road characteristics: a case study for Nanjing, China

ABSTRACT With the continuous growth of electric vehicle ownership, energy consumption has become an important factor restricting its development. Real-time and accurate energy consumption estimation is essential to alleviate range anxiety and help with energy-efficient route planning. This study aims to explore the factors affecting energy consumption, and estimate the energy consumption of electric vehicles under different road characteristics. Based on the real-world driving data of electric vehicles in Nanjing over a long period, this study analyzed the influencing factors of energy consumption, including trip conditions, weather, driving behavior, and road characteristics. Based on feature selection and correlation analysis, an energy consumption estimation framework for electric vehicles was proposed to estimate energy consumption under different road characteristics using extreme gradient boosting. The results show that energy consumption is significantly affected by trip conditions, weather, driving behavior, and road characteristics. The modeling of different road characteristics helps improve the accuracy of model estimation. The error of the model proposed in this paper is within the reasonable range (−0.25–0.25 kWh/km), which is better than the random forest and k-nearest neighbor models.

County-Level Land Use Carbon Budget in the Yangtze River Economic Belt, China: Spatiotemporal Differentiation and Coordination Zoning

The local land use carbon budget (LUCB) balance is an important factor in achieving regional carbon neutrality. As the basic unit of China’s economic development and social governance, the county level is an important part of the realization of the “double carbon” goal. This paper focuses on 1069 county units within the Yangtze River Economic Belt (YREB). It utilizes data on land use, nighttime light, energy consumption, and social and economic factors to construct carbon emission models. The spatiotemporal characteristics of LUCB in these county units are analyzed using standard deviational ellipse (SDE) and spatial autocorrelation methods. Additionally, a zoning study is conducted by examining the economic contribution coefficient (ECC) of carbon emissions, the ecological support coefficient (ESC), and their coupling relationship. The results show that (1) the total land use carbon emissions (LUCE) increased significantly during the research period, and the total carbon sink was relatively stable. (2) The LUCB is spatially high in the east and low in the west, with the center of gravity moving to the southwest as a whole. (3) The LUCB shows positive spatial autocorrelation and has significant spatial agglomeration characteristics, which are mainly high–high and low–low regional agglomeration types. (4) The ECC is high in the east and low in the west, the ESC is high in the west and low in the east, and the coordination and coupling degrees of the two are low. (5) According to the ECC and ESC, the county unit is divided into a low-carbon conservation area, an economic development area, a carbon sink development area, and a comprehensive optimization area. This study is helpful in promoting the sustainable development of carbon neutrality and low carbon in the YREB.