ie9b00492_si_002.xlsx (8.02 MB)
Sequential Use of Geographic Information System and Mathematical Programming for Optimal Planning for Energy Production Systems from Residual Biomass
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posted on 2019-05-16, 00:00 authored by José E. Santibañez-Aguilar, Diego F. Lozano-García, Francisco J. Lozano, Antonio Flores-TlacuahuacResidual biomass
is a renewable resource with attractive characteristics
to produce energy and biofuels. Diverse studies have stated that residual
biomass used for biofuels and energy production can contribute partially
to solve the energy demand problem, decreasing fossil fuels carbon
emissions. Most works have focused on developing new technologies,
processes, and processing systems based on biomass. Other works have
addressed the supply chain-planning problem to determine optimal locations
considering diverse objectives. A third group of works have proposed
schemes based on Geographic Information Systems (GIS) to determine
suitable locations in different types of systems. Nevertheless, works
capable to combine the advantage of GIS, mathematical programming,
and process design have not been properly conducted. Therefore, this
paper presents a sequential approach for the optimal planning of a
residual biomass processing system. The methodology considers selecting
potential locations through a multicriteria methodology based on GIS.
Also, this paper proposes a mathematical programming approach for
the optimal planning of a residual biomass processing system, which
considers as input the locations predefined by GIS methodology, as
well as six potential products, six processing routes, and eight raw
materials. The mathematical programming approach consists of mass
balances to obtain the interconnections between the different supply
chain nodes, as well as constraints to model the considered technologies
involving capital investment and production costs. The GIS approach
was applied to a case study in Mexico, which produced 764 harvesting
sites and 334 processing plants for all considered residual biomass
types. The optimization approach conducted used 33 processing plants,
467 harvesting sites, and 2 products from 3 biomass types in order
to determine the final supply chain topology. Results show that the
proposed methodology is a useful tool to determine the optimal supply
chain topology during the decision process.
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Geographic Information Systemsupply chain nodeslocationGIS3 biomass typesbiomass processing system334 processing plantsenergy demand problem467 harvesting sitessupply chain-planning problemfuels carbon emissionsResidual Biomass Residual biomass33 processing plantsEnergy Production Systemssupply chain topologymethodology764 harvesting sitesprogramming approachGeographic Information Systems
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