Optimal Production Scheduling for Energy Efficiency Improvement in Biofuel Feedstock Preprocessing Considering Work-In-Process Particle Separation
Abstract
Biofuel is considered a promising alternative to traditional liquid transportation fuels. The large-scale substitution of biofuel can greatly enhance global energy security and mitigate greenhouse gas emissions. One major concern of the broad adoption of biofuel is the intensive energy consumption in biofuel manufacturing. This paper focuses on the energy efficiency improvement of biofuel feedstock preprocessing, a major process of cellulosic biofuel manufacturing. An improved scheme of the feedstock preprocessing considering work-in-process particle separation is introduced to reduce energy waste and improve energy efficiency. A scheduling model based on the improved scheme is also developed to identify an optimal production schedule that can minimize the energy consumption of the feedstock preprocessing under production target constraint. A numerical case study is used to illustrate the effectiveness of the proposed method. The research outcome is expected to improve the energy efficiency and enhance the environmental sustainability of biomass feedstock preprocessing.
Recommended Citation
L. Li et al., "Optimal Production Scheduling for Energy Efficiency Improvement in Biofuel Feedstock Preprocessing Considering Work-In-Process Particle Separation," Energy, vol. 96, pp. 474 - 481, Elsevier, Feb 2016.
The definitive version is available at https://doi.org/10.1016/j.energy.2015.12.063
Department(s)
Engineering Management and Systems Engineering
Research Center/Lab(s)
Intelligent Systems Center
Sponsor(s)
National Science Foundation (U.S.)
Keywords and Phrases
Biofuel Manufacturing; Feedstock Preprocessing; Optimal Production Scheduling; Particle Separation
International Standard Serial Number (ISSN)
0360-5442
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2016 Elsevier, All rights reserved.
Publication Date
01 Feb 2016
Comments
This work is supported by the U.S. National Science Foundation under Grant number 1434392 .