Multiscale strategic planning model for the design of integrated ethanol and gasoline supply chain

Abstract

In this paper, we address the design and planning of an integrated ethanol and gasoline supply chain. We assume that the supply chain is composed of harvesting sites, production sites for ethanol, petroleum refineries, distribution centers where blending takes place, and the retail gas stations where different blends of gasoline and ethanol are sold. We postulate a superstructure that combines all the components of the supply chain. We consider different means of transportation that connect the nodes in the superstructure. We model this multiscale design of integrated ethanol and gasoline supply chain as a multiperiod MILP model for given forecasts of demand for different blends over the entire time horizon. In order to identify the regions of the US where investments are needed and the optimal configuration of the network, we first consider a strategic planning model in which gasoline stations are aggregated in the different regions.We then consider a second detailed formulation where regions are disaggregated into gas stations in order to also determine the retrofit projects for the selection of blending pumps over their expected life. A bilevel decomposition algorithm is proposed to solve the detailed model. We illustrate the application of these MILP models with two large-scale problems..

Multi‐scale systems engineering for energy and the environment: Challenges and opportunities

“…82 Strategic planning of an integrated ethanol and gasoline supply chain has also been investigated considering thermochemical or biochemical conversion of biomass at two different scales so that the fuel demand per region and the number of regional gas stations required are taken into account. 83 The benefits of biological, catalytic, and even thermochemical biomass supply chains are often seen in the lifecyle analysis of the systems, due to the absorption of CO 2 during photosynthesis. 84 Beyond this, some studies have even analyzed the social benefits of these supply chains by incorporating analysis on the number of jobs added based on refinery placements.…”

A new process for fuel ethanol dehydration based on modeling the phase equilibria of the anhydrous MgCl2 + ethanol + water system

“…4 The potential feedstock for fuel ethanol is cellulosic materials such as corn stalks, wood chips, and switchgrass so that the pressure on food sources can be reduced. 5,6 By fermentation of the sugar mixture, a broth containing 5-15 wt % ethanol is achieved. 7 The ethanol is then separated from the solid residue and concentrated to near the azeotropic point using conventional distillation.…”

Fair profit allocation in supply chain optimization with transfer price and revenue sharing: MINLP model and algorithm for cellulosic biofuel supply chains

“…Comprehensive reviews on the optimization of biofuel supply chains are presented by Yue et al and Sharma et al 12 Deterministic mixed-integer linear programming (MILP) models have been widely employed to investigate cost-minimizing or profit-maximizing biofuel supply chain configurations, which often incorporate spatially explicit, multiechelon, and multiperiod features. [13][14][15][16][17][18][19][20][21] Multiobjective optimization models have been proposed to account for other dimensions of biofuel supply chains in addition to economic performance, including environmental sustainability, social benefit, safety criteria, and so forth. [22][23][24][25][26][27] Various pricing and quantity uncertainties in the biofuel supply chain have been addressed by stochastic programming, robust approaches, fuzzy programming, and so forth.…”