Clean Technologies and Environmental Policy

, Volume 19, Issue 3, pp 721–734 | Cite as

Mathematical optimization of a supply chain for the production of fuel pellets from residual biomass

  • Manuel Alejandro Méndez-Vázquez
  • Fernando Israel Gómez-Castro
  • José María Ponce-Ortega
  • Alma Hortensia Serafín-Muñoz
  • José Ezequiel Santibañez-Aguilar
  • Mahmoud M. El-Halwagi
Original Paper

Abstract

One of the main concerns of humankind in the last years is the availability of energy sources. Research has been focused on finding clean and renewable ways to satisfy the energy demand worldwide. In the particular case of the state of Guanajuato, Mexico, clay industry burns each year about 15,000 m3 of fuel oil and residual oils, and 96,000 t of wood derivatives. As a way to reduce the environmental impact of clay industry, the use of solid fuel pellets, obtained from vegetable residual material, is proposed. The raw material for the pellets is obtained from agribusiness and from the cities of the state. The solid biofuel has high density, low content of humidity, a homogeneous shape and high energy density. Nevertheless, special care must be taken about the location of the production facility and hubs, in order to make the production of the biofuel economically feasible. Furthermore, to have an environmentally friendly fuel, the supply chain and the production process must minimize the global environmental impact. In this work, a mathematical programming model is proposed to determinate the optimal location of the production facilities, the hubs, and the best distribution logistics. The problem is modelled using a general disjunctive programming approach, and then relaxed into a mixed-integer non-linear programming (MINLP) problem. It has been determined that the main plant should be located in the city of Irapuato, while secondary plants must be established in the cities of León, Irapuato, Abasolo and Salamanca. Moreover, it has been estimated that, when the residual biomass is converted into pellets, about 72,548 t/year of equivalent CO2 are avoided in the main plant, together with 24,182 of equivalent CO2 avoided per secondary facility.

Keywords

Solid biofuel Pellet Supply chain optimization Mathematical programming 

List of symbols

Variables

Absorbedw [t year−1]

Carbon dioxide absorbed by trees

CostRM [USD]

Cost of raw material

CostTRANS [USD]

Cost of transport of raw material

CostPROC [USD]

Cost of processing raw material

CC [USD year−1]

Capital cost

COP [USD year−1]

Operational cost

GP [kg h−1]

Production rate

Ceq [USD]

Cost of equipment

CO2, Base [t year−1]

Emissions of carbon dioxide for the base case

CO2, scenario [t year−1]

Emissions of carbon dioxide when using pellets

Disposal [t year−1]

Emissions of carbon dioxide due to disposal of non-used biomass

e [year−1]

Capital recovery factor

LUCw [t year−1]

Emissions of carbon dioxide due to deforestation

Processrm [t year−1]

Emissions of carbon dioxide due to biomass processing

Transportw [t year−1]

Emissions of carbon dioxide due to wood transportation

Transportr&p [t year−1]

Emissions of carbon dioxide due to biomass and pellet transportation

Usew [t year−1]

Emissions of carbon dioxide due to wood burning

Usep [t year−1]

Emissions of carbon dioxide due to pellet burning

Parameters

i [1]

Interest rate

N [y]

Lifetime of the equipment

neq [dimensionless]

Scaling factor of equipment

tOP [h year−1]

Operating hours per year

αeq [USD t−1]

Unitary cost of equipment

Notes

Acknowledgments

The authors acknowledge the financial support of Universidad de Guanajuato; the scholarship granted to Manuel Alejandro Méndez-Vázquez by Consejo Nacional de Ciencia y Tecnología, Mexico; and the contributions of the start-up companies GEMAR and Todo Pellet.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Manuel Alejandro Méndez-Vázquez
    • 1
  • Fernando Israel Gómez-Castro
    • 1
  • José María Ponce-Ortega
    • 2
  • Alma Hortensia Serafín-Muñoz
    • 3
  • José Ezequiel Santibañez-Aguilar
    • 2
  • Mahmoud M. El-Halwagi
    • 4
  1. 1.Campus Guanajuato, División de Ciencias Naturales y Exactas, Departamento de Ingeniería QuímicaUniversidad de GuanajuatoGuanajuatoMexico
  2. 2.División de Estudios de Posgrado, Facultad de Ingeniería QuímicaUniversidad Michoacana de San Nicolás de HidalgoMoreliaMexico
  3. 3.Campus Guanajuato, División de Ingenierías, Departamento de Ingeniería CivilUniversidad de GuanajuatoGuanajuatoMexico
  4. 4.The Artie McFerrin Department of Chemical EngineeringTexas A&M UniversityCollege StationUSA

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