| Title |
A big data driven analytical framework for energy-intensive manufacturing industries |
| ID_Doc |
10973 |
| Authors |
Zhang, YF; Ma, SY; Yang, HD; Lv, JX; Liu, Y |
| Title |
A big data driven analytical framework for energy-intensive manufacturing industries |
| Year |
2018 |
| Published |
|
| DOI |
10.1016/j.jclepro.2018.06.170 |
| Abstract |
Energy-intensive industries account for almost 51% of energy consumption in China. A continuous improvement in energy efficiency is important for energy-intensive industries. Cleaner production has proven itself as an effective way to improve energy efficiency and reduce energy consumption. However, there is a lack of manufacturing data due to the difficult implementation of sensors in harsh production environment, such as high temperature, high pressure, high acid, high alkali, and smoky environment which hinders the implementation of the cleaner production strategy. Thanks to the rapid development of the Internet of Things, many data can be sensed and collected in the manufacturing processes. In this paper, a big data driven analytical framework is proposed to reduce the energy consumption and emission for energy-intensive manufacturing industries. Then, two key technologies of the proposed framework, namely energy big data acquisition and energy big data mining, are utilized to implement energy big data analytics. Finally, an application scenario of ball mills in a pulp workshop of a partner company is presented to demonstrate the proposed framework. The results show that the energy consumption and energy costs are reduced by 3% and 4% respectively. These improvements can promote the implementation of cleaner production strategy and contribute to the sustainable development of energy intensive manufacturing industries. (C) 2018 Elsevier Ltd. All rights reserved. |
| Author Keywords |
Energy-intensive manufacturing industries; Big data analytics; Cleaner production; Data mining |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000441998400007 |
| WoS Category |
Green & Sustainable Science & Technology; Engineering, Environmental; Environmental Sciences |
| Research Area |
Science & Technology - Other Topics; Engineering; Environmental Sciences & Ecology |
| PDF |
https://liu.diva-portal.org/smash/get/diva2:1245930/FULLTEXT01
|