385597 Life Cycle Optimization on Sustainable Design of Cellulosic Biopower Supply Chains

Wednesday, November 19, 2014: 2:35 PM
International A (Marriott Marquis Atlanta)
Dajun Yue and Fengqi You, Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL

With a large fleet of aging power plants and increasingly stringent environmental regulations and policies, the country is exploiting its energy portfolio and seeking potential sustainable alternatives for its electricity supply. As one of the most promising renewable power generation options, biopower from cellulosic biomass offers the advantage of being dispatchable and having minimum implications on food market, considerable potentials to waste reduction, and abundant domestic availability across the country [1, 2]. The major obstacle at the current stage for large-scale implementation of biopower supply systems lies in the development of an efficient supply chain network that links the biomass supply with biopower conversion facilities [3-5]. Another perspective to note is that, although cellulosic biomass resources are considered carbon-neutral, activities such as biomass collection, transportation, storage and material processing would incur positive greenhouse gas (GHG) emissions, which can have implications on global warming and climate change [6, 7]. Hence, it is essential to simultaneously consider the economic performance as well as the environmental impacts when addressing the design and operation of bio-electricity supply chains.

It is the goal of this work to develop a generic optimization model for sustainable design and operation of biopower supply chains, which can simultaneously evaluate and optimize the economic and environmental performances by achieving the optimal decisions in network design, technology selection, facility location and sizing, transportation and storage, etc. [8, 9]. To address this challenge, we propose a multi-objective, multi-period mixed-integer linear fractional programming (MILFP) model that captures the critical features of the biopower supply chain, including geographically disperse distribution of biomass, seasonality in biomass supply, water content and degradation issues, etc. [10]. Levelized cost of electricity is chosen as the economic metrics, which is extensively used as the major indicator for the projected economic performance of a renewable energy system. The environmental impact associated with the production of a unit amount of bioelectricity is chosen as the environmental metrics, which is evaluated based on a cradle-to-gate life cycle assessment and would lead to a more environmentally sustainable way of manufacturing [11-14]. We note that MILFP is a special class of mixed-integer nonlinear program (MINLP). Due to the non-convexity and combinatorial nature of the resulting MILFP problems, it can be challenging for the global optimization of large-scale supply chain problems. To tackle this issue, we further present two tailored MILFP solution approaches, namely the parametric algorithm [15, 16] and the reformulation-linearization method [17]. To illustrate the application, we present a county-level case study on a potential biopower supply chain in the state of Illinois. The resulting Pareto curve reveals how the inherent tradeoffs between the economic and environmental objectives influence the design and operational decisions. On the other hand, it is shown that the tailored solution methods excel general-purpose MINLP methods both in terms of solution time and quality.


[1]        D. Yue, M. A. Kim, and F. You, "Design of Sustainable Product Systems and Supply Chains with Life Cycle Optimization Based on Functional Unit: General Modeling Framework, Mixed-Integer Nonlinear Programming Algorithms and Case Study on Hydrocarbon Biofuels," ACS Sustainable Chemistry & Engineering, vol. 1, pp. 1003-1014, 2013/08/05 2013.

[2]        D. Yue, F. You, and S. W. Snyder, "Biomass-to-bioenergy and biofuel supply chain optimization: Overview, key issues and challenges," Computers & Chemical Engineering.

[3]        D. Yue, M. Slivinsky, J. Sumpter, and F. You, "Sustainable Design and Operation of Cellulosic Bioelectricity Supply Chain Networks with Life Cycle Economic, Environmental, and Social Optimization," Industrial & Engineering Chemistry Research, vol. 53, pp. 4008-4029, 2014/03/12 2014.

[4]        A. A. Rentizelas, I. P. Tatsiopoulos, and A. Tolis, "An optimization model for multi-biomass tri-generation energy supply," Biomass and Bioenergy, vol. 33, pp. 223-233, 2// 2009.

[5]        J. Keirstead, N. Samsatli, A. M. Pantaleo, and N. Shah, "Evaluating biomass energy strategies for a UK eco-town with an MILP optimization model," Biomass and Bioenergy, vol. 39, pp. 306-316, 4// 2012.

[6]        F. You, L. Tao, D. J. Graziano, and S. W. Snyder, "Optimal design of sustainable cellulosic biofuel supply chains: Multiobjective optimization coupled with life cycle assessment and input–output analysis," AIChE Journal, vol. 58, pp. 1157-1180, 2012.

[7]        F. You and B. Wang, "Life Cycle Optimization of Biomass-to-Liquid Supply Chains with Distributed–Centralized Processing Networks," Industrial & Engineering Chemistry Research, vol. 50, pp. 10102-10127, 2011/09/07 2011.

[8]        K. Tong, J. Gong, D. Yue, and F. You, "Stochastic Programming Approach to Optimal Design and Operations of Integrated Hydrocarbon Biofuel and Petroleum Supply Chains," ACS Sustainable Chemistry & Engineering, vol. 2, pp. 49-61, 2014/01/06 2013.

[9]        K. Tong, M. J. Gleeson, G. Rong, and F. You, "Optimal design of advanced drop-in hydrocarbon biofuel supply chain integrating with existing petroleum refineries under uncertainty," Biomass and Bioenergy, vol. 60, pp. 108-120, 1// 2014.

[10]     B. H. Gebreslassie, Y. Yao, and F. You, "Design under uncertainty of hydrocarbon biorefinery supply chains: Multiobjective stochastic programming models, decomposition algorithm, and a Comparison between CVaR and downside risk," AIChE Journal, vol. 58, pp. 2155-2179, 2012.

[11]      A. Azapagic, "Life cycle assessment and its application to process selection, design and optimisation," Chemical Engineering Journal, vol. 73, pp. 1-21, 4// 1999.

[12]      A. Azapagic and R. Clift, "The application of life cycle assessment to process optimisation," Computers & Chemical Engineering, vol. 23, pp. 1509-1526, Dec 1999.

[13]      B. H. Gebreslassie, M. Slivinsky, B. Wang, and F. You, "Life cycle optimization for sustainable design and operations of hydrocarbon biorefinery via fast pyrolysis, hydrotreating and hydrocracking," Computers & Chemical Engineering, vol. 50, pp. 71-91, 3/5/ 2013.

[14]      B. H. Gebreslassie, R. Waymire, and F. You, "Sustainable design and synthesis of algae-based biorefinery for simultaneous hydrocarbon biofuel production and carbon sequestration," AIChE Journal, vol. 59, pp. 1599-1621, 2013.

[15]      F. Q. You, P. M. Castro, and I. E. Grossmann, "Dinkelbach's Algorithm as An Efficient Method to Solve A Class of MINLP Models for Large-Scale Cyclic Scheduling Problems," Computers & Chemical Engineering, vol. 33, pp. 1879-1889, Nov 2009.

[16]      Z. Zhong and F. You, "Globally Convergent Exact and Inexact Parametric Algorithms for Solving Large-Scale Mixed-Integer Fractional Programs and Applications in Process Systems Engineering," Computers & Chemical Engineering, vol. DOI: 10.1016/j.compchemeng.2013.10.017

[17]      D. Yue, G. Guillén-Gosálbez, and F. You, "Global optimization of large-scale mixed-integer linear fractional programming problems: A reformulation-linearization method and process scheduling applications," AIChE Journal, vol. 59, pp. 4255-4272, 2013.

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