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Optimal design of sustainable cellulosic biofuel supply chains: Multiobjective optimization coupled with life cycle assessment and input–output analysis
Abstract
This article addresses the optimal design and planning of cellulosic ethanol supply chains under economic, environmental, and social objectives. The economic objective is measured by the total annualized cost, the environmental objective is measured by the life cycle greenhouse gas emissions, and the social objective is measured by the number of accrued local jobs. A multiobjective mixed‐integer linear programming (mo‐MILP) model is developed that accounts for major characteristics of cellulosic ethanol supply chains, including supply seasonality and geographical diversity, biomass degradation, feedstock density, diverse conversion pathways and byproducts, infrastructure compatibility, demand distribution, regional economy, and government incentives. Aspen Plus models for biorefineries with different feedstocks and conversion pathways are built to provide detailed techno‐economic and emission analysis results for the mo‐MILP model, which simultaneously predicts the optimal network design, facility location, technology selection, capital investment, production planning, inventory control, and logistics management decisions. The mo‐MILP problem is solved with an ε‐constraint method; and the resulting Pareto‐optimal curves reveal the tradeoff between the economic, environmental, and social dimensions of the sustainable biofuel supply chains. The proposed approach is illustrated through two case studies for the state of Illinois. © 2011 American Institute of Chemical Engineers AIChE J, 2012
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Cites
Logistics issues of biomass: the storage problem and the multi-biomass supply chain
Rentizelas, A. A., Tolis, A. J., & Tatsiopoulos, I. P. (2009) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2008.01.003 · Cited by 633
Cited by
Towards supply chain sustainability: economic, environmental and social design and planning
Mota, B., Gomes, M. I., Carvalho, A., & Barbosa-Povoa, A. P. (2015) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2014.07.052 · Cited by 438
Biomass supply chain design and analysis: Basis, overview, modeling, challenges, and future
Sharma, B., Ingalls, R. G., Jones, C. L., & Khanchi, A. (2013) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2013.03.049 · Cited by 422
Cambero, C., & Sowlati, T. (2014) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2014.04.041 · Cited by 352
Methods to optimise the design and management of biomass-for-bioenergy supply chains: A review
De Meyer, A., Cattrysse, D., Rasinmäki, J., & Van Orshoven, J. (2014) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2013.12.036 · Cited by 329
Quantum computing for energy systems optimization: Challenges and opportunities
Ajagekar, A., & You, F. (2019) · Energy · doi:10.1016/j.energy.2019.04.186 · Cited by 289
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Metadata
- Authors
- You, F., Tao, L., Graziano, D. J., & Snyder, S. W.
- Venue
- AIChE Journal
- Publisher
- Wiley
- Published
- 2011-04-28
- Volume
- 58
- Issue
- 4
- Pages
- 1157-1180
- ISSN
- 0001-1541 (ISSN-L)
- 1547-5905
- OpenAlex
- W2109671759
- Cited by
- 669
- References
- 1
- FWCI
- 66.64
- Type
- journal article
- Language
- en
- Open access
- closed
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