Energy Consumption In Industrial Sector Research Articles

Dynamic analysis and energy consumption modeling of a composite spindle with active magnetic bearings for a sustainable manufacturing

Modern life is marked with the extensive use of machine tools. The latter stand for high consumers of electrical energy which increase carbon emissions. For this reason, various solutions aiming to reduce energy consumption in industrial sector are elaborated. One solution lies in the use of economical materials for the spindle rotation system supported by active magnetic bearings (AMBs), which corresponds to a problem that has never been undertaken so far. A new conception for machine tool spindle rotation system is elaborated for sustainable design. The spindle material is composed of a carbon epoxy hollow shaft wrapped with a steel layer and supported by AMBs. A Finite Element Method (FEM) modeling of the spindle rotation system was carried out to assess its dynamic consumed energy and power during the acceleration and cutting phases. For an accurate estimation, the power consumed by the spindle system was calculated taking into account the dynamic behavior of the cutting force and losses generated by the bearings. A comparison between energies consumed by the proposed spindle system and the steel spindle system was enacted.

Comparison of Three-Phase Induction Motors Testing Methods using ABNT 17094-3:2018 and IEC 60034-2-1:2014

Electric motors are considered the most important equipment among those that consume final electric energy in Brazil. It is estimated that the induction motors and the systems driven by them are responsible for approximately 70% of theBrazilian industrial sector's energy consumption. Therefore, obtain its losses is a relevant challenge for energy efficiency. Any initiative developed to increase these devices' performance brings environmental benefits, energy benefits, and cost reduction for the industrial, commercial, and other sectors. Nowadays, several standards can be used to determine the performance characteristics of induction motors. However, their application results in variations in the performance calculations. Thus, this paper evaluated two tests on three-phase induction motors comparing the different methods applied between the standards ABNT 17094-3:2018 and IEC 60034-2-1:2014. These standards present different normalized equations. Thus, this paper seeks to obtain engine performance variation when testing it using different standards, comparing and discussing the results. The results obtained confirm that the methods have different results and the parameters can be compared.

Comparative life-cycle cost (LCC) study of green and traditional industrial buildings in Sri Lanka

The demand for green buildings is growing, but the trend is below the expected level due to the perceived higher construction cost required by building investors. In addition, the industry or the practitioners knowledge and awareness of major operational cost savings of facilities is questionable. on that note, this study aims to establish the cost implications of green buildings via a comparative life-cycle cost analysis of two green certified industrial and one traditional building. The industrial sector is one of the largest energy consuming sectors in the world with over 50% of the world’s total delivered energy is absorbed by the sector. Energy consumption of industrial sector is likely to increase more due to the economic and population growth.The data for the analysis were extracted from construction, operation, and maintenance expenditure budget records of the selected organisations. The analysis shows that in terms of life-cycle costs, green industrial buildings are 17% cheaper than that of traditional buildings. Though the initial construction cost of a green industrial building is 29% higher, the operation and maintenance costs of green buildings result in 23% and 15% overall savings throughout the life cycle. The findings provide further empirical proof of the benefits of green buildings, especially in industrial manufacturing where it is expected to improve future uptakes and consequently the achievement of sustainable development initiatives.

Estimating the energy consumption of Indian refineries: an empirical analysis based on panel data econometrics

The oil refinery sector is regarded as the leading energy-consuming industry that shares 33.1% of the total energy consumption in industrial sectors in India. Therefore, the present study estimates the actual energy consumption by using panel data of 12 Indian oil refineries for the period from 2011-2012 to 2016-2017. Using pooled OLS, fixed and random effect models, feasible generalised least squares and panel-corrected standard error models the study observed that that distillate yield, high sulphur crude processed, refinery age and refinery structure (dummy variable) are significant and positively affected to specific and actual energy consumption. Therefore, policies should be adopted for judicious use of these variables to reduce energy consumption in Indian refineries. The study recommends that Indian refiners have to adopt waste heat recovery, flare gas recovery, overall site heat integration and best operating practices to reduce energy consumption.

Estimating the energy consumption of Indian refineries: an empirical analysis based on panel data econometrics

The oil refinery sector is regarded as the leading energy-consuming industry that shares 33.1% of the total energy consumption in industrial sectors in India. Therefore, the present study estimates the actual energy consumption by using panel data of 12 Indian oil refineries for the period from 2011-2012 to 2016-2017. Using pooled OLS, fixed and random effect models, feasible generalised least squares and panel-corrected standard error models the study observed that that distillate yield, high sulphur crude processed, refinery age and refinery structure (dummy variable) are significant and positively affected to specific and actual energy consumption. Therefore, policies should be adopted for judicious use of these variables to reduce energy consumption in Indian refineries. The study recommends that Indian refiners have to adopt waste heat recovery, flare gas recovery, overall site heat integration and best operating practices to reduce energy consumption.

The transmission mechanism of household lifestyle to energy consumption from the input-output subsystem perspective: China as an example

In recent years, China's rapid economic development and steady growth of household consumption have contributed to the continuous growth of energy consumption. In addition to considering direct household energy consumption, the indirect effects of people's consumption activities on energy consumption in other productive sectors can't be ignored. According to the input-output analysis framework and consumer lifestyle analysis, this paper mainly studies the direct and indirect consumption of energy by Chinese household consumption ways and the relationship between energy structure and lifestyle. From the perspective of structural analysis, the input-output subsystem model (scale component, own internal component, feed-back component and spillover component) is used to analyze the energy supply and demand relations among 36 sectors of the national economy related to the lives of household in 2012 and 2017, revealing the change trend and transmission mechanism. This paper also focuses on the impact of household consumption ways on energy consumption of industrial sector and changing consumer lifestyles on energy saving, meanwhile it also forecasts China's future household energy consumption under four scenarios. The results show that in 2012 and 2017, total consumption of energy which is influenced by household consumption ways in China is 28461.82 PJ and 44759.96 PJ, respectively, accounting for 26.9% and 34.1% of total consumption of final energy. The indirect energy consumption is 1.3 times and 1.65 times of direct, respectively. The coal consumption accounts for the majority of indirect energy consumption. The smelting and pressing of ferrous metals is the industrial sector which is most affected by the household consumption ways. The scale component, own internal component, feed-back component and spillover component has accounted for 18.5%, 1.3%, 0.1% and 80.0%, from 17.5%, 2.0%, 0.2% and 80.3% of energy consumption in total, respectively. Under the four scenario development modes (NN, NA, LN and LA), it is predicted that the indirect energy consumption in household life in 2025 is 43740.56PJ, 34922.93PJ, 39149.37PJ and 31257.28PJ, respectively. The implementation of low-carbon consumption mode and the accelerated decline of energy intensity will help to reduce energy consumption.

Assessing net energy consumption of Australian economy from 2004–05 to 2014–15: Environmentally-extended input-output analysis, structural decomposition analysis, and linkage analysis

This paper provides a comprehensive analysis of Australian net energy consumption between 2004–05 and 2014–15. Results from environmentally-extended input-output (EEIO) analysis show that the Transport sector has the largest direct effect on net energy consumption in industrial sectors, which decreased by about 35% for net energy consumption per million $AUD in the period. The Export sector has the largest direct net energy consumption while Households consumption results in the largest net energy consumption embodied in different categories of Final demand. The structural decomposition analysis (SDA) decomposes the change of net energy consumption into five drivers, in which net energy intensity mainly reduces Australian net energy consumption by about 8000 Petajoules, while the level effect of Final demand increases it by about 10,000 Petajoules. Analysis of forward and backward linkages highlights the Manufacturing sector as the key industrial sector with the largest energy consumption reduction potential via minor changes in its input and Final demand. This indicates that more attention should be given to the reduction of energy demand from the consumption patterns of Households consumption, the improvement of energy intensity, and the application of cleaner technologies in the Transport and Manufacturing sectors. The Australian Environmental-Economic Accounts is combined with Australian input-output tables to construct the EEIO tables for net energy consumption. The combination of economic and environmental data sets provides a depth of understanding their potential to inform environmental policy decisions. The novelty of the research is the combination of economic and energy data sets, the application of EEIO model, the implementation of the additive SDA method, and the use of forward and backward linkages for the Australian energy system.

Peak energy consumption and CO2 emissions in China's industrial sector

China's industrial sector accounts for more than half of the country's final energy demand. Thus, controlling its energy consumption and carbon dioxide (CO2) emissions is critical for achieving China's Paris Agreement target. We developed two scenarios for this sector's energy consumption and CO2 emissions up to 2050 by inputting China's current macroeconomic, industrial de-capacity, energy efficiency, and policy requirements into a modified global change assessment model. Our quantitative analysis indicated that the industrial sector's energy consumption and CO2 emissions growth will peak by 2025, subsequently declining. In the first reference scenario, peak energy consumption and CO2 emission values in 2025 will be 2.42 gigatons of coal equivalent (Gtce) and 4.43 gigatons of CO2 (GtCO2), respectively. In the second low-carbon scenario, these peak values in 2025 will be 2.28 Gtce and 4.13 GtCO2, respectively, that is, 5% and 7% lower than the respective values in the first scenario. After 2025, energy consumption and CO2 emissions will decrease gradually up to 2050. Measures required to achieve low-carbon peak targets include policies for adjusting and optimizing the industrial structure, promoting low-carbon energy, and capping energy and coal use, and CO2 emissions in this sector.

9-3.産業部門のエネルギー消費の比較((1)エネルギー消費,Session 9 エネルギー評価・経済)

9-3.産業部門のエネルギー消費の比較((1)エネルギー消費,Session 9 エネルギー評価・経済)

Efficient energy consumption in industrial sectors and its effect on environment: A comparative analysis between G8 and Southeast Asian emerging economies

This study investigates the long-term relationship between industrial production and energy consumption along with the environmental degradation in developed and developing countries. The purpose of this study is to identify countries that meet the competitive challenges of achieving efficient environmentally friendly energy consumption. The investigation process utilises the Toda Yamamoto technique looking at 40 years of data from 1971 to 2011. The investigation reveals that both G8 and Southeast Asian countries are energy dependent, as indicated by unidirectional Granger causality running from energy consumption to industrial production. The most energy efficient economies are among G8 countries and the least efficient are found in Southeast Asian countries. The results also reveal that industrial production of developed countries is more environmentally friendly than developing countries. Finally, the study provides significant policy implications toward improving energy security and efficiency.

Integrated IDA–ANN–DEA for assessment and optimization of energy consumption in industrial sectors

This paper puts forward an integrated approach, based on logarithmic mean divisia index (LMDI) – an index decomposition analysis (IDA) method, an artificial neural network (ANN) and a data envelopment analysis (DEA) for the analysis of total energy efficiency and optimization in an industrial sector. The energy efficiency assessment and the optimization of the proposed model use LMDI to decompose energy consumption into activity, structural and intensity indicators, which serve as inputs to the ANN. The ANN model is verified and validated by performing a linear regression comparison between the specifically measured energy consumption and the corresponding predicted energy consumption. The proposed approach utilizes the measure-specific, super-efficient DEA model for sensitivity analysis to determine the critical measured energy consumption and its optimization reductions. The proposed method is validated by its application to determine the efficiency computation and an analysis of historical data as well as the prediction and optimization capability of the Canadian industrial sector.

Energy demand forecasting in Iranian metal industry using linear and nonlinear models based on evolutionary algorithms

Developing energy-forecasting models is known as one of the most important steps in long-term planning. In order to achieve sustainable energy supply toward economic development and social welfare, it is required to apply precise forecasting model. Applying artificial intelligent models for estimation complex economic and social functions is growing up considerably in many researches recently. In this paper, energy consumption in industrial sector as one of the critical sectors in the consumption of energy has been investigated. Two linear and three nonlinear functions have been used in order to forecast and analyze energy in the Iranian metal industry, Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) are applied to attain parameters of the models. The Real-Coded Genetic Algorithm (RCGA) has been developed based on real numbers, which is introduced as a new approach in the field of energy forecasting. In the proposed model, electricity consumption has been considered as a function of different variables such as electricity tariff, manufacturing value added, prevailing fuel prices, the number of employees, the investment in equipment and consumption in the previous years. Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD) and Mean Absolute Percent Error (MAPE) are the four functions which have been used as the fitness function in the evolutionary algorithms. The results show that the logarithmic nonlinear model using PSO algorithm with 1.91 error percentage has the best answer. Furthermore, the prediction of electricity consumption in industrial sector of Turkey and also Turkish industrial sector is reinvestigated, the results indicate significant improvement.

Energy Consumption in the Turkish Industrial Sector

Turkey's primary energy resources seem limited as indigenous energy production meets nearly 31 % of the total primary energy demand. But the growth of Turkey's industry gives rise to a substantial increase in energy demand. Final energy consumption grew from 52.6 Mtoe (million tons of oil equivalent) in the year 1990 to 78.4 Mtoe in the year 2002. Industrial demand accounts for about 41.6 % of the final energy demand in Turkey. Basic metal industries, non-metallic material products and chemical and petroleum products have the highest energy consumption in industrial sector. In this work, industrial energy consumption and energy consumption of different indsutrial subsectors in Turkey are investigated.

Inventory of SO2, PM and toxic metals emissions from industrial sources in Greater Mumbai, India

Abstract A comprehensive and spatial emission inventory was carried out for sulphur dioxide (SO2), particulate matter (PM) and toxic metals from industrial sources in Greater Mumbai, India. Fuel consumption database was developed for industrial sources. Emission factors for various pollutants were compiled from the literature, scrutinized and used appropriately as applicable under Indian conditions. Emissions of SO2, PM and toxic metals were estimated for 2001–02 and extrapolated to 2010. SO2 emissions from fossil fuel combustion covering 215 point sources for 2001–02 were computed as 55.591 Gg y−1 whereas those for PM were calculated as 9.794 Gg y−1. The total metal emissions from industrial sources were computed as 0.375 Gg y−1. Total fossil fuel energy consumption in industrial sector during 2001–02 was 145 PJ, which included fuel consumption (29%) in power plants. Amongst the industries, thermal power plants (TPP) were the major source of emissions in the region contributing 27% share towards SO2, 19% PM and 62% metals. Projected scenario for 2010 indicates that there would be substantial reduction in PM and metal emission load while the gaseous pollutant concentration would show a decreasing trend.