(573a) Sustainable Design of Supply Chains Using Life Cycle Optimization Framework Based On Functional Unit With Application On Hydrocarbon Biofuel Product Systems

Concerns about climate change, waste pollution, energy security and resource depletion are driving the society to explore a more sustainable way for development and manufacturing, which calls for sustainable thinking in design of product systems and supply chains under both economic and environmental concerns [1-3]. In this work, we propose the Life Cycle Optimization (LCO) framework for design and planning of general product systems and supply chains, which integrates classical Life Cycle Assessment (LCA) methodology and multi-objective optimization techniques [4-6]. Distinct from most works in this field, both objectives are modeled based on the functional unit, where all the costs and environmental footprints are embedded and reflected. In this way, we observe that further space for improvement in economic and environmental performances of the supply chain can be achieved, leading to more cost-competitive and environmentally green product systems.

The multi-objective optimization framework is dealt with ε-constraint method, where the Pareto-optimal solutions are obtained by solving a sequence of subproblems with different ε-parameter values [7]. Due to the presence of fractional economic and environmental objective functions regarding functional unit, the subproblems are formulated as mixed-integer linear fractional programming (MILFP) problems. To globally optimize the MILFPs effectively, we employ two tailored MILFP methods (specifically, parametric algorithm and reformulation-linearization method), which are shown to be much more efficient than general-purpose MINLP methods and global optimizers [8, 9].

To illustrate the application of the proposed modeling framework and solution methods, we investigate a county-level case study on the sustainable design of a potential hydrocarbon biofuel supply chain in the state of Illinois, consisting of 102 counties. Advanced hydrocarbon liquid transportation fuels derived from cellulosic biomass are considered promising renewable energy alternatives, of which the technologies are relatively mature. Therefore, the industry is eager for the development of corresponding supply chain and infrastructure [10, 11]. A spatially explicit model is formulated to integrate decision making across biomass acquisition, centralized-distributed conversion, and product distribution, meanwhile simultaneously predict the optimal network design, technology selection, capital investment, production operations and logistics management decisions. Gallon of gasoline-equivalent (GGE) is chosen as the functional unit for the hydrocarbon biofuel products, namely biomass-derived gasoline and diesel [12]. The environmental performance is measured by the greenhouse gas (GHG) emission per GGE of biofuels, derived through a “field-to-wheel” LCA procedure based on the concept of 100-year global warming potentials (GWP100). The resulting Pareto solutions reveal the tradeoffs among the decision variables under both the economic and environmental objectives. The proposed solution methods are also proved to be much more efficient than general MINLP approaches.

References

[1]        A. Nikolopoulou and M. G. Ierapetritou, "Optimal design of sustainable chemical processes and supply chains: A review," Computers & Chemical Engineering, vol. 44, pp. 94-103, Sep 2012.

[2]        I. E. Grossmann and G. Guillén-Gosálbez, "Scope for the application of mathematical programming techniques in the synthesis and planning of sustainable processes," Computers & Chemical Engineering, vol. 34, pp. 1365-1376, 9/7/ 2010.

[3]        J. A. Elia, R. C. Baliban, X. Xiao, and C. A. Floudas, "Optimal energy supply network determination and life cycle analysis for hybrid coal, biomass, and natural gas to liquid (CBGTL) plants using carbon-based hydrogen production," Computers & Chemical Engineering, vol. 35, pp. 1399-1430, Aug 2011.

[4]        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 (Submitted), 2012.

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

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

[7]       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.

[8]        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.

[9]        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, submitted, 2013.

[10]      F. Q. 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, Apr 2012.

[11]      F. Q. 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, Sep 2011.

[12]      B. Wang, B. H. Gebreslassie, and F. You, "Sustainable design and synthesis of hydrocarbon biorefinery via gasification pathway: Integrated life cycle assessment and technoeconomic analysis with multiobjective superstructure optimization," Computers & Chemical Engineering, vol. 52, pp. 55-76, 5/10/ 2013.