Energy consumption model and energy efficiency of machine tools: a comprehensive literature review

Zhou, Lirong; Li, Jianfeng; Li, Fangyi; Meng, Qiang; Li, Jing; Xu, Xiaorong

doi:10.1016/j.jclepro.2015.05.093

Cited by 415 publications

(203 citation statements)

References 74 publications

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“…The PPF method can be used to study machine tool energy-saving technologies, including energy saving in the use of machine tools and the design of high-efficiency machine tools. 12 As the main energy consumption component of the machine tool spindle unit, the spindle unit is an important consideration when investigating the energy-saving technologies of machine tools. 29,30 According to equation (26) h m = P t P t + P ml = n m T m n m T m + 9550P lm ð28Þ n m = (60f =p)(1 À s), p is the pole number of the motor, and s is motor slip.…”

Section: Application and Discussionmentioning

confidence: 99%

A new approach for calculating the input power of machine tool main transmission systems

Liu

2017

Advances in Mechanical Engineering

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The main transmission system, the key component for transferring energy in machine tools, is essential for studying the energy efficiency of machine tools, highlighting the need for manufacturers to measure the input power of the main transmission system to develop energy-saving technology. In response to such a need, this article proposes a new approach that can be applied to calculate the input power of the main transmission system fed by a converter. To develop this approach, the influences of the converter and frequency variations on motor loss are analyzed to build the idle and loaded loss motor models for manufacturers. Based on these two models, the relationships between the input power of the main transmission system, the motor, and its frequency are investigated to establish a calculation approach that is convenient because only the motor input power measurement is required. Finally, 20 group experiments are conducted to validate the proposed approach, and its applications are discussed. The applications demonstrate that this method provides a high level of accuracy and validity for calculating the input power of the main transmission system driven by a converter and could be widely used to study high-efficiency machine tools and monitor the energy efficiency of machine tools.

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Section: Application and Discussionmentioning

confidence: 99%

A new approach for calculating the input power of machine tool main transmission systems

Liu

2017

Advances in Mechanical Engineering

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“…Одним з першочергових за-вдань оптимального управління підприємством є визначення чинників, що впливають на рівень спо-живання електроенергії (Babets', Mel'nykova, Hrebeniuk, & Lobov, 2015;Zhou et al, 2016).…”

Section: вступunclassified

Formation of integrated energy efficiency indicator for mining industry companies

Sinchuk¹,

Sinchuk²,

Peresunko³

et al. 2017

Min. miner. depos,

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Purpose. Justification of the principles for assessing the competitive status of Ukrainian mining companies' energy efficiency as a derivative of electricity consumption and qualitative estimation of the companies' competitive positions in terms of their energy efficiency.Methods. The research is based on the model for assessing the competitive status of energy efficiency as a derivative of electricity consumption. The composite method of estimating the company's electricity consumption comprises determination of the competition intensity indicator along with the assessment of the energy market capacity.Findings. The indicators of internal potential of enterprises' electricity consumption, the competitive position in certain segments of the electricity consumption market, the company's ability to withstand the influence of external factors are determined. Analysis of the conducted calculations allowed to identify leaders and outsiders among the companies competing for energy efficiency status.Originality. Estimation of the energy efficiency status level of Ukrainian mining companies contains new approaches to using mathematical and statistical analysis methods modified in accordance with the industry specifics. Practical implications.The obtained results can be used to carry out a comprehensive analysis of the electricity consumption management. Considering the company life cycle will allow to assess its financial resources, its ability to achieve a certain level of energy resources efficient use and to determine the external and internal factors that influence the management of the company power supply, and to predict changes in its energy consumption indicators.

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“…Thus, by comparing the energy consumption of each plan, the plan combined with least energy consumption will be found and chosen as the best plan to implement. Normally, the energy consumption in one data center includes quite a number of ingredients, like PMs, administration system's, and airconditioning system's power costs [2]. Regarding E for energy consumption, the energy consumption in one data center will be identified as:…”

Section: Model and Designmentioning

confidence: 99%

The Energy Optimization Based on Virtual Machine Placement

Wang¹,

Cheng²,

Shi³

2017

Proceedings of the 7th International Conference on Education, Management, Information and Mechanical Engineering (EMIM 2017)

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Abstract. In order to solve the problem of huge energy consumption in data centers, this paper delivers a new virtual machine placement framework, including profiles and task classification. The first part of this framework is profiling phase. This phase intent to build virtual machine profiles, to identify each virtual machines run-time, resource requests, and previous utilization. The profiles are based on past virtual machine logs and coming virtual machine plans. According to these profiles, the system related to this framework will make better decisions for virtual machine placements. The second part is virtual machine classification phase. Based on some typical characteristics of virtual machines in the profiles built in last phase, the system will apart virtual machines into some given sorts. Each sort of virtual machines will have differential placement methodologies. The third part is virtual machine placement phase. All information of virtual machines, including profiles and sorts, will come to this phase. Then, the system will, accordingly, conduct typical first-fit-decrease algorithm to build a virtual machine placement plan, to enhance the power efficiency.

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Energy consumption model and energy efficiency of machine tools: a comprehensive literature review

Cited by 415 publications

References 74 publications

A new approach for calculating the input power of machine tool main transmission systems

A new approach for calculating the input power of machine tool main transmission systems

Formation of integrated energy efficiency indicator for mining industry companies

The Energy Optimization Based on Virtual Machine Placement

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