Analysis of Dimensionless Numbers for Graphite Purification in the Electromagnetic Induction Furnaces

Analysis of the Dependence on Economic Growth in China to Energy Consumption

Impact of the green credit policy on external financing, economic growth and energy consumption of the manufacturing industry

Green finance is an important practice of China’s high-quality economic development in the new era, which is closely related to economic development, environment, and energy conditions. However, few studies systematically analyze the impact of green finance on economic development, environmental pollution, and energy consumption, especially on China which is turning to high-quality economic development. In order to fill the gap, based on the annual data on 30 provinces (autonomous regions and municipalities) in China from 2008 to 2018, we construct a comparatively comprehensive green finance index system and use a panel regression model to explore the impacts of green finance on high-quality economic development, environmental pollution, and energy consumption. We find that green finance can significantly promote high-quality economic development, mitigate environmental pollution, and reduce energy consumption. There is spatial and temporal heterogeneity in the impact of green finance on China’s economic quality, environmental pollution, and energy consumption. In the eastern region, green finance has a remarkable positive impact on high-quality economic development and a significant negative impact on energy consumption, but the impact on environmental pollution is inconspicuous. In the central region, green finance has a prominent effect on reducing environmental pollution, but the impact on high-quality economic development and energy consumption is not significant. In the western region, green finance has not been able to significantly promote high-quality economic development, mitigate environmental pollution, and reduce energy consumption. After the clear proposal of green finance, the role of green finance in promoting a high-quality economy has enhanced, and the role of green finance in reducing environmental pollution and energy consumption has decreased. This study can provide a useful decision-making reference for promoting high-quality economic development, reducing environmental pollution and energy consumption, and spurring sustainable development.

The COVID-19 pandemic has caused a drop-in economic activity and energy consumption of the United States. This work aims to investigate the spillover effects of the United States’ COVID-19 economic recession on economic growth and energy consumption in other nations using a global vector autoregressive (GVAR) approach and quarterly data between 1990 and 2013 from 41 major countries/regions. On the one hand, the simulation results indicate that the US COVID-19 recession has a negative impact on other countries’ economic growth through trade ties, reducing the economic growth of other countries, especially for countries which have a close trade relationship with the US. In addition, the spillover effects of the US economic recession have different impacts on other countries’ energy consumption. Countries with the closest trade ties to the US are most affected, such as Japan and China. In addition, the impact of the US’ economic shock on energy consumption in developing countries is significant in the short term, while its impact on developed countries is significant in the long term. On the other hand, the simulation results of energy spillover effects indicate a reduction in US energy consumption slightly reduces economic growth in other nations. In addition, a reduction in energy consumption in the US does not have a significant negative impact on energy consumption in other developed countries. Furthermore, the spillover effect of declining energy consumption in the US on energy consumption in developing countries is significant in the short term. However, the spillover effects of falling energy consumption in the US on developing countries are different. The spillover effect of the decline in energy consumption in the US causes a slight decline in energy consumption in China and Brazil, whereas the spillover effect of the decline in energy consumption in the US does not cause a decline in energy consumption in India and Brazil.

Energy intensity is one of the most important energy feature that has a dramatic value in energy system of Iran. Indeed, Iran is one of the most energy intensive countries in the world and its main reason is related to high energy consumption in household section. In this article, we present a non-linear model that considers three scenarios in management of household energy demand reducing. Therefore, a rational percentage of energy consumption reduction in the household sector, which firstly eliminates the imbalance between energy production and consumption, and secondly, derives a rational amount of profit from various reduction scenarios, will be presented. The mentioned rational profits in this article are obtained from three scenarios. In the first scenario, it is assumed that the percentage reduction in household energy consumption will be allocated to reducing energy demand in the industrial sector, resulting in profits from value-added creation in this sector. In the second scenario, all benefits from reducing energy consumption in the household sector will be devoted to energy exports, yielding profits from this source. Finally, in the third scenario, the reduction in energy consumption will lead to a decrease in energy supply and consequently a reduction in energy supply costs. To conduct a comprehensive study, a combination of the mentioned scenarios has also been modeled and investigated. The model results indicate that with a 25% reduction in household energy consumption in the 2024-2034 timeframe, the energy imbalance will be eliminated, and allocating 5% of this reduction entirely to the industrial sector will result in profits equivalent to $164.18 billion. However, it should be noted that in the considered combined scenarios, the greater the share of the first scenario, the higher the resulting profit, and the optimal point is achieved in the first scenario.

China’s economy adopts an extensive growth method. This economic growth method relies on resource input, which consumes huge energy and causes excessive pressure on resources and the environment. The economic growth rate of Fujian Province ranks among the highest in the country in recent years, and its energy consumption is still on the high side. Industrial enterprises, especially the manufacturing industry, account for the majority of energy consumption. In order to ensure the sustainable development of enterprises and society, the government needs to make plans for energy conservation and consumption reduction, policy support and enterprise energy conservation and consumption reduction measures to promote energy conservation and consumption reduction in the whole society, and strive to achieve coordinated development of economic growth and environmental protection.

Additive manufacturing (AM) provides the ability to produce complex, fully functional, multi-material parts directly from its three-dimensional models, resulting in reduction of prototyping lead time and costs. In the literature it can be seen that various techniques have been developed to improve productivity of AM processes by focusing on reducing time and cost, and improving quality during part production. Despite these advantages of AM, it is characterized by high energy consumption that can decrease its sustainable benefits. Moreover, there is limited work in the literature that addresses productivity improvement in terms of energy consumption. This study addresses this shortcoming by introducing a framework for minimizing energy consumption during the production process. A novel optimization-based framework for part decomposition is presented that results in energy savings for AM processes thereby addressing an important research gap for decreasing energy consumption in manufacturing processes. The framework integrates a Genetic Algorithm (GA) based optimization approach to determine the optimal decomposition of a part into sub-parts and their corresponding orientation. This approach thus aims to achieve a reduction in energy consumption by reducing the energy required for both the build and assembly phases of production. In this study, the framework targets a reduction in energy consumption by at least 10% compared to the original, undecomposed part by decomposing a part into sub-parts and optimizing their orientation. The study thoroughly discusses the application of this framework to the Selective Laser Sintering (SLS) process, detailing the procedure from the initial calculation of energy consumption for a given part orientation to the iterative process of generating and evaluating decomposed parts. This decomposition process continues until the framework achieves the target energy consumption reduction, demonstrating the framework’s adaptability to different AM processes and energy savings goals. The effectiveness of the framework is validated by applying it to four diverse test cases, including both simple geometric objects and complex shapes like the Stanford Bunny. For each test case, the target of 10% reduction in energy consumption due to decomposition is exceeded which demonstrates the potential of this framework to improve the energy efficiency of AM processes. These results demonstrate the utility of this framework as a practical tool for designers and manufacturers aiming to optimize energy use in AM thereby supporting the ongoing efforts on sustainable manufacturing practices. This research thus focuses on energy consumption reduction through intelligent part design and orientation thereby mitigating the environmental impacts of AM. Additionally, by filling the gap in existing literature regarding energy savings in the design phase of an AM process, this work lays the foundation for further innovations in sustainable manufacturing. In future the framework’s application to other AM processes will be assessed, its impact on other production properties will be investigated, and energy savings from the assembly process will be quantified for a more comprehensive understanding of its utility.

Aim: Energy consumption in Turkey is increasing due to population and Gross Domestic Product (GDP) growth, increasing consumption and the widespread use of technological products. In Turkey, which imports more than half of its energy needs, in addition to meeting the energy need from domestic and renewable resources, energy efficiency studies in sectors with high energy consumption are of great importance. When the sectors with high energy consumption are examined, it is seen that the transportation sector has a high rate of 25%. By estimating how much energy Turkey, which is dependent on foreign energy, will consume in which sector in the coming years, it will guide the correct planning of renewable energy resources investments and energy efficiency studies. Method: The amount of energy to be consumed in the transportation sector until 2030 has been estimated by using the data on population, GDP and motor vehicle amounts and final energy consumption amounts in the transportation sector, using machine learning algorithms; Random forest, Support vector machine and artificial neural networks (ANN) methods. Results: As a result of the analysis, it was seen that the ANN method gave the most appropriate result for this study. The average training and test regression values obtained by ANN for five cross validations were 0.9968 ± 0.0019 and 0.9978 ± 0.0013, respectively. As a result of the estimation, it was calculated that 31.391 thousand Tons of Oil Equivalent (TOE) of energy will be consumed in the transportation sector in 2030. Conclusion: In Turkey, where energy consumption in the transportation sector is increasing every year and Turkey is a country that meets its energy needs from imported sources, shifting a significant portion of freight and passenger transportation in road transportation, which is used intensively among transportation types, to rail and maritime transportation, where energy consumption is more efficient, will reduce energy consumption. In addition, energy efficiency studies in every sector will contribute to the national economy. Reduced energy consumption will also reduce CO2 emissions.

Parametric optimization of removing iron from solid waste melts based on analysis of real-time coupled two-phase interface in an induction heating furnace

The multi-power source coupled transmission system is a high-performance and energy-saving potential power transmission system, and most of the commonly used pure electric vehicles in the market that use multi-power source coupled drive adopt the motor dual-axis distributed independent drive scheme. The configuration design method for multi-power source fusion hybrid systems mainly focuses on the search and selection of power split hybrid systems based on planetary gear mechanisms. But it has not yet covered the configuration design of transmission systems, resulting in a lack of universal expression and generation methods for the configuration of multi-power source fusion hybrid systems in pure electric vehicles. Therefore, to solve the configuration optimization design problem of a dual-motor single-planetary-array power system, an improved general matrix topology design method is proposed to generate all feasible topology structures. And energy consumption, economy, and the dynamic performance of alternative configurations are optimized and simulated through the control strategy based on a dynamic programming algorithm. Under comprehensive testing conditions, 25 alternative options that met the screening criteria were selected, and, ultimately, five optimized configuration options were obtained. Configuration 1 has the best economy, reducing energy consumption by about 6.3%and increasing driving range by about 6.7%. Its 0–100 km/h acceleration time is about 31.4% faster than the reference configuration. In addition, the energy consumption economy during actual driving is almost the same as the theoretical optimal energy consumption economy, with a difference of only 0.3%. The success of this study not only provides an innovative method for optimizing the configuration of dual-motor single-row star train power systems, but also has a positive impact on improving energy utilization efficiency, reducing energy consumption, and improving the overall performance of electric vehicles.

A municipal wastewater treatment plant plays an important role in treating urban sewage and reducing the quantity of pollutants discharged into rivers. However, the energy consumption of the municipal wastewater treatment industry is large. High energy consumption indirectly produces ecological damage, accelerates the energy crisis, and increases carbon emissions. For energy conservation and emission reduction in wastewater treatment plants, it is first necessary to identify the main factors influencing energy consumption. Electricity consumption accounts for more than 80% of the energy consumption of wastewater treatment plants. Wastewater quantity and wastewater quality have become the key influencing factors of energy conservation and consumption reduction in wastewater treatment plants. In this study, a municipal wastewater treatment plant in Northeast China was selected as the research object, and the measured data, such as air temperature, wastewater quantity, wastewater quality, and electricity consumption of the plant from 2017 to 2020 were statistically analyzed to explore the influences of temperature and wastewater quantity and wastewater quality indicators of influent and effluent on energy consumption. Firstly, the range of influent quantity in the wastewater treatment plant was large. The influent quantity in summer was high because some rainwater entered the sewage treatment plant. In winter, average daily electricity consumption (ADEC) was higher than that in summer. The relationship between ADEC and the wastewater quantity showed a positive correlation, and ADEC slowly increased with the increase in wastewater quantity. Electricity consumption per unit of wastewater (UEC) was negatively correlated with the wastewater quantity, but the correction coefficient in winter was larger than that in summer. Secondly, the ranges of chemical oxygen demand (CODCr) and ammonia nitrogen in influent were large, and the ranges of CODCr and ammonia nitrogen in effluent were small. Influent CODCr concentration was negatively correlated with influent ammonia nitrogen concentration. ADEC increased slightly with the increase in influent CODCr concentration. In winter, the increasing trend of ADEC with the influent CODCr concentration was higher than that in the summer. The increasing trend of UEC with the increase in influent COD concentration in summer was more significant than that in winter. Thirdly, influent CODCr in 11.6% of the samples exceeded the corresponding designed value, and influent ammonia nitrogen concentration in 41.4% of the samples exceeded the corresponding designed value. Effluent CODCr in 10.6% of the samples exceeded the First Level Class B standard in “Discharge Standard of Pollutants for Municipal Wastewater Treatment Plants (GB18918-2002)”, and unqualified CODCr in 94% of the effluent samples was ascribed to the unqualified ammonia nitrogen concentration in the influent samples. The electricity consumption level under abnormal conditions was higher than that under normal conditions. Fourthly, ADEC was positively correlated with the average daily CODCr reduction. The correction coefficient of ADEC with average daily CODCr reduction was greater in winter than that in summer. Fifthly, the average electricity consumption per unit of wastewater was close to the national average energy consumption, displaying the characteristics of high energy consumption in winter and low energy consumption in summer. The correlation analysis results of unit electricity consumption and temperature showed that when it was below 0 °C, the lower the temperature, the higher the electricity consumption. In Northeast China, the influences of seasons and temperatures on the electricity consumption of sewage plants were obvious. Accordingly, it is necessary to implement the diversion of rainwater and sewage, reduce the discharge of unqualified wastewater from enterprises, and take thermal insulation measures in winter. In addition, activated sludge microorganisms suitable for a low temperature area and the optimal scheduling of sewage pipe networks can also improve the operation and management of sewage treatment plants.

In Italy, a large part of the buildings classified as historical–artistic heritage is in a state of degradation and requires urgent interventions. Among the needs, a reduction in the high energy consumption and an improvement of the indoor comfort are mandatory. The case study of the “Ex Institute of Zoology of Palermo” shows how it is possible to achieve a reduction in energy consumption and preserve the building’s historicity through the estimation of energy loads and the subsequent processing. To quantify the energy savings of the structure, it was necessary to follow a few steps, namely an inspection of the building and an evaluation of its consumption. To elaborate a correct energy analysis, the thermal conductance was calculated by considering measurements over a period of 60 days and proceeding with the calculation of the transmittance. The resulting data are imported in the energy model developed with the TERMUS BIM software. In the case study, a potential energy saving equal to 35% of the current energy consumption can be avoided by replacing the lamps with LED ones, installing photovoltaic panels, replacing the heat pumps with a centralized system and the current fixtures with double thermal break fixtures. These interventions are implemented in compliance with the architectural peculiarities.

At present, there is much literature on economic growth and energy consumption, but there is little literature combined with the industry perspective. This paper aims to clarify whether the development of energy-intensive industries is an indirect way for economic growth to affect energy consumption, which can provide a reference for the coordination of economic growth goals, industry development and reducing energy consumption. Based on China’s provincial panel data from 2000 to 2019, this paper measures the scale of provincial energy-intensive industries by entropy method and uses the panel regression model to test its transmission effect on energy consumption. The results show that 23.96% of the effects of economic growth on energy consumption are indirectly generated through the transmission of energy-intensive industries. Moreover, the transmission effects are only established in the eastern and western regions but are not significant in the central region. Therefore, controlling the rapid development of energy-intensive industries is an effective way to curb the expansion of China’s energy consumption scale. Green technology innovation, new-type urbanization construction and other supportive measures should be taken in accordance with local conditions. This research contributes to the coordinated and sustainable development of the economy, industry, and energy.

The COVID-19 pandemic threatened the world. As an important transportation hub connecting countries and regions, airports have played a critical role in COVID-19 prevention and control. This study developed an infection risk-human comfort-energy consumption model to calculate the COVID-19 transmission and energy consumption for epidemic prevention and control in seven different functional areas of an airport during different seasons (winter, summer, and transition season) and actual passenger movement. When considering dynamic passenger flow, the energy consumption needed to prevent and control transmission of the epidemic in each area of the airport could be reduced by 71–85 %. The waiting, dining, and shopping areas were the areas with the highest energy consumption, accounting for 25–47 %, 15–32 %, and 11–38 % of the total energy consumption of epidemic prevention at the airport, respectively. The dining area had the highest energy consumption per square meter, reaching 14.2 kWh/m2 at its highest. After closing the dining area, energy consumption was reduced by 14–20 %. Compared with strict epidemic prevention and control, energy consumption in the airport was reduced by 70–85 % considering both optimized intervention and dynamic passenger flow. The results of this study provide a scientific basis for energy-saving and emission reduction in airports under an epidemic situation.

Legged robots have demonstrated significant achievements in recent years. Some legged robots have considerable flexibility and movement abilities. However, certain obstacles restrict the practical application of legged robots, such as their high energy consumption. The energy consumption of a legged robot is significantly higher than that of a wheel robot of the equivalent size for the same walking distance. Reducing the energy consumption of legged robots is important for their practical application and further development. This study proposes a quadratic-programming force-distribution controller, which minimizes the energy consumption of hexapod robots. The controller reduces the energy consumption by optimizing the instantaneous power of the robot at each time step. In a simulation environment, the proposed method reduced the energy consumption by up to 9.43% and 6.30% in flat terrain and sloped terrain, respectively, compared with two other methods. In hexapod robot experiment, the proposed method can reduce energy consumption by 5.72% compared with position control.