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Event-Based Supervisory Control for Energy Efficient Manufacturing Systems.
- Source :
-
IEEE Transactions on Automation Science & Engineering . Jan2018, Vol. 15 Issue 1, p92-103. 12p. - Publication Year :
- 2018
-
Abstract
- It becomes more and more critical for manufacturing enterprises to improve energy efficiency because of the escalating energy prices, increasing global competitions, and more rigorous government regulations. In this paper, a systematic method is developed to improve the energy efficiency of a multistage manufacturing system through production control. The method aims at reducing energy consumption with minimal negative impact on production. We start from the analysis of system dynamics and develop quantitative methods to estimate energy saving opportunities. A supervisory control algorithm is developed to improve system energy efficiency by periodically taking the saving opportunities. Simulation case studies are performed to validate the effectiveness of the control algorithm.</p><p>Note to Practitioners—Manufacturing systems are facing increasing pressure to reduce energy consumption, as global competition, sustainability, and green processes are becoming more prevalent. Although most of the research efforts on manufacturing energy saving have focused on developing individual energy efficient machines, it can be more cost-effective to improve energy efficiency through better control of the energy usage of the whole production system. Therefore, this paper presents a systematic method to improve system energy efficiency with a minimal negative impact on production. This paper continues the work by Chang et al. by extending the scope of energy saving opportunity theory from serial production systems to general serial–parallel production systems. It also develops analytical methods based on Markov chain models to estimate the energy saving opportunity accurately. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 15455955
- Volume :
- 15
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Automation Science & Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 127154242
- Full Text :
- https://doi.org/10.1109/TASE.2016.2585679