(773c) Multiscale Strategic Planning Model for the Design of Integrated Ethanol and Gasoline Supply Chain

Multiscale
Strategic Planning Model for the Design of Integrated Ethanol and Gasoline
Supply Chain

Federico Andersen¹,
Soledad Diaz¹ and Ignacio Grossmann²,

(1)  Chemical Engineering, Planta Piloto de Ingenieria
Quimica (PLAPIQUI), Universidad Nacional del Sur - CONICET, Bahia Blanca,
Argentina

(2)  Dept. of Chemical Engineering, Carnegie Mellon
University, Pittsburgh, PA 15213

Ethanol
continues to receive attention as a major energy source from biomass that can
help to address the increasing needs of liquid fuels that are predicted over
the next few decades. Research and industrial efforts have made of ethanol as
one of the safest (both economically and environmentally) alternatives to
conventional fuels, at least in the short and medium term. However several
challenges remain, which have not yet been fully solved and developed. One of
the main concerns is related to the implementation of higher blends of ethanol
with gasoline and the utilization of alternative production processes for
lignocellulosic ethanol. These concerns are based on the RFS Schedule under the
Energy Independence and Security Act of 2007, which points to the utilization
of 21 of 36 billion gallons of advanced biofuel by 2022. The widely used raw
material to produce E10 within the US is corn, but due to increasing demands,
alternative raw materials such as lignocellulosic material must be considered
for the production of fuel grade ethanol. Facing the need to start the
commercialization of higher ethanol blends, new challenges with respect to
bioethanol production can be identified. Therefore in this transition from E10
to E30 it would be necessary not only to consider the addition of new raw
materials, but also to allow for the distribution of different ethanol blends
at the same time in the same gas station.

Because of the
need to provide the market with multiple fuel blends, the business of gas
stations has become a multi-blend market, where the demand of each blend
depends on the driver's preference and legal requirements. This preference is
mainly related to the price of the blend and the miles per gallon of fuel that
can be driven, which is also different among vehicles. This feature requires an
accurate forecast of demand for every blend and poses a new challenge to
estimate and satisfy this demand with minimal cost, opening up a new market for
multi-product gas stations, where blender pumps are used to obtain the desired
blend mixing E85 with E10 at the gas station itself. Since most of the current
gasoline distribution takes place through single-product gas stations and the
investments to retrofit all of them into multi-product would be huge, we have
modeled the coexistence of both of these types of gas stations and the prospect
to move towards the multi-product ones in a gradual way, considering the
possibility to enhance or reduce the total quantity of gas stations.

In this work we
formulate the integrated ethanol and gasoline supply chain, taking into account
the stages from the harvesting sites, through production sites for ethanol,
petroleum refineries, blending stages and up to the retail of different blends
in gas stations. The general problem can be stated as follows. Given a
superstructure that combines all these stages and different means of
transportation which connect the nodes, the integrated ethanol and gasoline
supply chain can be represented with a multiperiod model and a time horizon of
20 years. Given also an initial capacity for ethanol plants and gasoline
distribution center and data of type and quantity of existing gas stations; and
given a forecast of demand for different blends over the entire time horizon,
the main goal is to determine several major decisions in order to minimize cost.
Some of these decisions involve: whether to install or not small; medium or
large ethanol plants and gasoline distribution centers (taking into account
economy of scale for the investments), and timing both for retrofits of different
types of gas stations that involve blending pumps. The costs components
included in the analysis are: investment capital cost (where economy of scale
has been considered); raw materials; production; transportation; storage and distribution
cost.

Since the
industries and players of the bioethanol supply chain are widely spread over
the US, the coordination over different geographical locations is a key feature
for the optimization of the supply chain. Another key feature is based on
different time scales required in the model. This fact arises from the inherent
difference between the lifetime of investments in ethanol plants, which is on
the order of years, and the replenishment of the gas stations that occurs every
week or every two weeks. Because of both features the need to cope with two
different formulations of the model can be identified. Each formulation
contains a different level of detail of some stages, especially in the
downstream supply chain (at gas stations level).

Within the more
general formulation we have considered an aggregated strategic planning model
in order to identify the regions of the US where more investments are needed
and the optimal configuration of the network. Within the second formulation,
even though the main modeling effort has been devoted to the operation of gas
stations and the selection of blending pumps, the global characteristic of a
supply chain model is also being reflected. Hence, this multiscale strategic
planning model contains a high level of details about gas stations in
comparison to other stages of the supply chain.

To illustrate
the application of the proposed MILP models, we have considered over a
geographical region a set of feasible retrofits between the single-product and
multi-product gas stations, and decrease of capacity in the period that they
are being retrofitted. Also we have included the possibility to satisfy high
ethanol fuel demand with lower ethanol fuels.

Integer
variables are associated to active gas stations; required retrofits; new gas
stations and those that should be dropped off. Due to the size of the
multiscale strategic planning model, we have applied a decomposition scheme to
efficiently solve the problem using the Lagrangean decomposition strategy.

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