| Title |
Data-driven sustainable intelligent manufacturing based on demand response for energy-intensive industries |
| ID_Doc |
16404 |
| Authors |
Ma, SY; Zhang, YF; Liu, Y; Yang, HD; Lv, JX; Ren, S |
| Title |
Data-driven sustainable intelligent manufacturing based on demand response for energy-intensive industries |
| Year |
2020 |
| Published |
|
| DOI |
10.1016/j.jclepro.2020.123155 |
| Abstract |
The circular economy plays an important role in energy-intensive industries, aiming to contribute to ethical sustainable societal development. Energy demand response is a key actor for cleaner production and circular economy strategy. In the Industry 4.0 context, the advanced technologies (e.g. cloud computing, Internet of things, cyber-physical system, digital twin and big data analytics) provide numerous opportunities for the implementation of a cleaner production strategy and the development of intelligent manufacturing. This paper presented a framework of data-driven sustainable intelligent/smart manufacturing based on demand response for energy-intensive industries. The technological architecture was designed to implement the proposed framework, and multi-level demand response models were developed based on machine, shop-floor and factory to save energy cost. Finally, an application of ball mills in a slurry shop-floor of a partner company was presented to demonstrate the proposed framework and models. Results showed that the energy efficiency of ball mills can be greatly improved. The energy cost of the slurry shop-floor saved approximately 19.33% by considering electricity demand response using particle swarm optimisation. This study provides a practical approach to make effective and energy-efficient decisions for energy-intensive manufacturing enterprises. (C) 2020 The Author(s). Published by Elsevier Ltd. |
| Author Keywords |
Data-driven; Sustainable intelligent manufacturing; Demand response; Particle swarm optimisation; Energy-intensive industries; Circular economy |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000579490900018 |
| WoS Category |
Green & Sustainable Science & Technology; Engineering, Environmental; Environmental Sciences |
| Research Area |
Science & Technology - Other Topics; Engineering; Environmental Sciences & Ecology |
| PDF |
https://doi.org/10.1016/j.jclepro.2020.123155
|