Performance Management Concept to Evaluate and Improve the Energy Efficiency of Machine Tools

2012 ◽  
pp. 53-110
Author(s):  
André Zein
Keyword(s):  
Energy Efficiency ◽  
Performance Management ◽  
Machine Tools ◽  
Management Concept

scholarly journals Implementation of Energy Management Concept and Energy Management System in High Rise Office Building

2019 ◽  
Vol 16 (3) ◽  
pp. 85
Author(s):  
Marsul Siregar ◽  
Firma Purbantoro ◽  
Tajuddin Nur
Keyword(s):  
Energy Efficiency ◽  
Energy Management ◽  
Water Consumption ◽  
Management System ◽  
Green Building ◽  
Efficiency Index ◽  
Office Buildings ◽  
Office Building ◽  
Consumption Index ◽  
Management Concept

Energy Management Concept as part of Green Building Concept is focused to Improve Energy Efficiency Index (EEI) and Water Consumption Index (WCI). The Implementation Energy Management Concept in an office buildings of this study based on the management system model of continual improvement ISO 50001:2011. The purpose of this study was to determine the extent to which the implementation of green building principles in Office Buildings. This study took the case study in an office building in Jakarta Indonesia that has two towers, each tower has 32 floors and 3 basement floors. The method used is descriptive with respect to GREENSHIP Rating Tools for existing building which consists of six categories; Appropriate Site Development (ASD), Energy Efficiency & Conservation (EEC), Water Conservation (WAC), Material Resources & Cycle (MRC), Indoor Air Health & Comfort (IHC) and Building & Environments Management (BEM). The results show that implementation the Energy Management Concept could also made energy performance more efficient, after Implementing through Retrofitting of the Chiller System, Recycle Waste Water, Replacement of Conventional lamp to Energy Saving LED and also Training and Education to all employees and tenants. From comparing data research before implementation of Energy Management Concept in 2014 and after Implementation and retrofitting in 2016, 2017 & 2018, it is found that Energy Efficiency Index (EEI) from 238.8 kwh/m2/Years to 134,04kwh/m2/Year and Water Consumption Index (WCI)From 50 liter/person/Day to 27.18 Liter/person/Day. And the saving cost from electricity bill payments is IDR. 466,803,325.67 / month (18%) and roughly will Break Event Point (BEP) for 3.86 Years

A novel approach for acquiring the real-time energy efficiency of machine tools

Energy ◽  
2017 ◽  
Vol 121 ◽  
pp. 524-532 ◽  
Author(s):  
Peiji Liu ◽  
Fei Liu ◽  
Hang Qiu
Keyword(s):  
Energy Efficiency ◽  
Real Time ◽  
Machine Tools ◽  
The Real ◽  
Novel Approach

The Total Energy Efficiency Index for machine tools

Energy ◽  
2016 ◽  
Vol 102 ◽  
pp. 682-693 ◽  
Author(s):  
Timo Schudeleit ◽  
Simon Züst ◽  
Lukas Weiss ◽  
Konrad Wegener
Keyword(s):  
Energy Efficiency ◽  
Total Energy ◽  
Machine Tools ◽  
Efficiency Index

scholarly journals A Composite Evaluation Model of Sustainable Manufacturing in Machining Process for Typical Machine Tools

Processes ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 110 ◽  
Author(s):  
Lishu Lv ◽  
Zhaohui Deng ◽  
Tao Liu ◽  
Linlin Wan ◽  
Wenliang Huang ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Machine Tools ◽  
Manufacturing Industry ◽  
Orthogonal Experiment ◽  
Evaluation Model ◽  
Sustainable Manufacturing ◽  
Machining Process ◽  
Machining Parameters ◽  
Machining Processes ◽  
Carbon Efficiency

Machine tool is the basic manufacturing equipment in today’s mechanical manufacturing industry. A considerable amount of energy and carbon emission are consumed in machining processes, the realization of sustainable manufacturing of machine tools have become an urgent problem to be solved in the field of industry and academia. Therefore, five types of machine tools were selected for the typical machining processes (turning, milling, planning, grinding and drilling). Then the model of the energy efficiency, carbon efficiency and green degree model were established in this paper which considers the theory and experiment with the resource, energy and emission modeling method. The head frame spindle and head frame box were selected to verify the feasibility and practicability of the proposed model, based on the orthogonal experiment case of the key machining process. In addition, the influence rules of machining parameters were explored and the energy efficiency and green degree of the machine tools were compared. Finally, the corresponding strategies for energy conservation and emission reduction were proposed.

scholarly journals Regular Neural Network Based Machining Parameters Optimization Model Of NC Machine Tool

2018 ◽  
Vol 232 ◽  
pp. 01006
Author(s):  
Sanping Wang ◽  
Junwen Chen ◽  
Wei Yan
Keyword(s):  
Neural Network ◽  
Energy Efficiency ◽  
Energy Consumption ◽  
Machine Tool ◽  
Process Parameters ◽  
Machine Tools ◽  
Machining Process ◽  
Machining Parameters ◽  
Nc Machine Tool ◽  
Nc Machine

Energy consumption process is the basis for energy efficiency improvement of machine tools. Most of the existing researches focus on the static modelling of energy consumption of a machine tool; however, there are a few studies that paid attention to that how process parameters influence the energy consumption of machine tools during processing. It is noted that the process parameters can be selected to reduce energy consumption during machining processes without additional investment. In this paper, a characteristic energy consumption model for NC machine tool was proposed. Then, the mapping rule between process parameters and energy consumption of machine tool was studied, and the model was solved with the regular neural network (RNN). Finally, the result was verified with an experiment of milling the surface of aluminium block, which can effectively improve the energy efficiency of machine tool. The experiment results are shown that regular neural network is used to optimize the process parameters and process the same machining characteristics; we analyze the in machining process of machine tool based on the three cutting parameters, and then, a model of energy consumption. We employ to learn, and use this trained model to select optimal parameters.

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