Advertisement

Raw Materials Supply

  • José Miguel Laínez
  • Mar Pérez-Fortes
  • Aarón D. Bojarski
  • Luis Puigjaner
Chapter
Part of the Green Energy and Technology book series (GREEN)

Abstract

The pressure on reducing environmental footprint is facilitating the emergence of energy supply chains that have biomass as main feedstock. For the development of efficient energy supply chains from biomass it is required to properly integrate the various elements that comprise such systems (e.g., biomass supply, pre-treatment facilities and technologies for biomass to energy and/or fuels conversion). Additionally, it is recognised that a concerted effort is required, embracing the different supply chain entities, in order to correctly estimate environmental burdens and to propose effective environmental strategies. This chapter proposes the use of a mathematical modelling approach as an analytical tool that can support decision-making towards accomplishing the design and planning of efficient multiple source—multiple product bio-energy supply chains. The mathematical formulation of this problem becomes a multi-objective MILP (moMILP). Criteria selected for the objective function are the net present value (NPV) and the overall environmental impact, which is computed using the Impact 2002+ indicator. The main advantages of this approach are highlighted through a case study of a biomass-based supply chain that comprises different components geographically distributed over Spain. For comparison purposes, such a supply chain is contrasted to one embracing coal usage.

Keywords

Supply Chain Life Cycle Assessment Life Cycle Inventory Life Cycle Impact Assessment Supply Chain Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notation

Indices

e

Suppliers

f, f′

Facility locations

i

Tasks

j

Equipment technology

s

Materials (states)

t, t

Planning periods

a

Mid-point environmental impact categories

g

End-point environmental impact categories

Sets

Ag

Set of mid-point environmental interventions that are combined into end-point damage factors g

Erm

Set of suppliers e that provide raw materials

\( \hat{E}_{\text{prod}} \)

Set of suppliers e that provide production services

\( \bar{E}_{\text{tr}} \)

Set of suppliers e that provide transportation services

Fe

Set of locations f where supplier e is placed

FP

Set of materials s that are final products

Ij

Set of tasks i that can be performed in technology j

\( \bar{J}_{e} \)

Technology j that is available at supplier e

\( \tilde{J}_{f} \)

Technology j that can be installed at location f

Ji

Technologies that can perform task i

Mkt

Set of market locations

NTr

Set of non-distribution tasks

RM

Set of materials s that are raw materials

Sup

Set of supplier locations

Ts

Set of tasks producing material s

\( \bar{T}_{s} \)

Set of tasks consuming material s

Tr

Set of distribution tasks

Parameters

Asft

Maximum availability of raw material s in period t in location f

Demsft

Demand of product s at market f in period t

distanceff′

Distance from location f to location f

FCFJjft

Fixed cost per unit of technology j capacity at location f in period t

\( I_{ft}^{J} \)

Investment required to establish a processing facility in location f in period t

NormFg

Normalising factor of damage category g

Pricesft

Price of product s at market f in period t

\( {\text{Price}}_{jft}^{J} \)

Investment required per unit of technology j capacity increased at facility f in period t

rate

Discount rate

Waters

Moisture for material s

\( {\text{Water}}_{ij}^{\max } \)

Maximum moisture for task i performed in equipment j

\( \alpha_{sij} \)

Mass fraction of task i for production of material s in equipment j

\( \bar{\alpha }_{sij} \)

Mass fraction of task i for consumption of material s in equipment j

\( \beta_{jf} \)

Minimum utilisation rate of technology j capacity that is allowed at location f

\( \zeta_{ag} \)

g end-point damage characterisation factor for environmental intervention a

\( \theta_{{ijff^{\prime}}} \)

Capacity utilisation rate of technology j by task i whose origin is location f and destination location f

\( \rho_{{eff^{\prime}t}}^{\text{tr}} \)

Unitary transportation costs from location f to location f′ during period t

\( \tau_{ijfet}^{{{\text{ut}}1}} \)

Unitary cost associated with task i performed in equipment j from location f and payable to external supplier e during period t

\( \tau_{sfet}^{{{\text{ut}}2}} \)

Unitary cost associated with handling the inventory of material s in location f and payable to external supplier e during period t

\( \chi_{est} \)

Unitary cost of raw material s offered by external supplier e in period t

\( \psi_{{ijff^{\prime}a}} \)

a environmental category impact CF for task i performed using technology j receiving materials from node f and delivering it at node f

\( \psi_{ija}^{T} \)

a environmental category impact CF for the transportation of a mass unit of material over a length unit

Binary Variables

Vjft

1 if technology j is installed at location f in period t, 0 otherwise

Continuous Variables

DamCgft

Normalised end-point damage g for location f in period t

\( {\text{DamC}}_{g}^{\text{SC}} \)

Normalised end-point damage g along the whole SC

EPurchet

Economic value of purchases executed in period t to supplier e

ESalest

Economic value of sales executed in period t

FAssett

Investment on fixed assets in period t

FCostt

Fixed cost in period t

Fjft

Total capacity of technology j during period t at location f

FEjft

Capacity increment of technology j at location f during period t

ICaft

Mid-point a environmental impact associated to site f which rises from activities in period t

\( {\text{Impact}}_{f}^{2002} \)

Total environmental impact for site f

\( {\text{Impact}}_{\text{overall}}^{2002} \)

Total environmental impact for the whole SC

LHVsi

Lower heating value for material s in task i

NPV

Net present value

Pijff′t

Activity magnitude of task i in equipment j in period t whose origin is location f and destination location f

Pvsijfft

Amount of material s for flexible task i in equipment j in period t whose origin is location f and destination location f

Profitt

Profit achieved in period t

\( {\text{Purch}}_{et}^{\text{pr}} \)

Amount of money payable to supplier e in period t associated with production activities

\( {\text{Purch}}_{et}^{\text{rm}} \)

Amount of money payable to supplier e in period t associated with consumption of raw materials

\( {\text{Purch}}_{et}^{\text{tr}} \)

Amount of money payable to supplier e in period t associated with consumption of transport services

Salessfft

Amount of product s sold from location f in market f′ in period t

Ssft

Amount of stock of material s at location f in period t

Superscripts

L

Lower bound

U

Upper bound

Acronyms

B-NET

Biomass utilisation networks

BM

Biomass

CBA

Cost benefit analysis

CCS

Carbon capture and storage

CF

Characterisation factors

DFCF

Discounted-free-cash-flow

Eco-indicator 99

Damage environmental metric

EISA

Energy Independence and Security Act

FWR

Forest wood residues

GHG

Greenhouse gas

GrSCM

Green supply chain management

IMPACT 2002+

Mid-point and end-point (damage) environmental metric

LCA

Life cycle assessment

LCIA

Life cycle impact assessment

LCI

Life cycle inventory

STN

State task network

moMILP

Multi-objective mixed integer linear programming

NPV

Net present Value

SCM

Supply chain management

STN

State task network

References

  1. 1.
    Capros P, Mantzos L, Tasios N, De Vita A, Kouvaritakis N (2010) EU trends to 2030: update 2009. Technical report, Directorate-General for Energy and the Directorate-General for Climate Action: European CommissionGoogle Scholar
  2. 2.
    US Department of Energy (2010) Energy efficiency and renewable energy: biomass program Technical report, US Department of Energy. http://www1.eere.energy.gov/library/pdfs/biomass_green_jobs_factsheet_2010_01.pdf. Accessed 10 Sept 2010
  3. 3.
    Tang CS (2006) Perspectives in supply chain risk management. Int J Prod Econ 103:451–488CrossRefGoogle Scholar
  4. 4.
    Panichelli L, Gnansounou E (2008) GIS-based approach for defining bioenergy facilities location: a case study in Northern Spain based on marginal delivery costs and resources competition between facilities. Biomass Bioenerg 32:289–300CrossRefGoogle Scholar
  5. 5.
    Hamelinck CN, Suurs RAA, Faaij APC (2005) International bioenergy transport costs and energy balance. Biomass Bioenerg 29:114–134CrossRefGoogle Scholar
  6. 6.
    Leduc S, Schwab D, Dotzauer E, Schmid E, Obersteiner M (2008) Optimal location of wood gasification plants for methanol production with heat recovery. Int J Energy Res 32:1080–1091CrossRefGoogle Scholar
  7. 7.
    Ayoub N, Seki H, Naka Y (2009) Superstructure-based design and operation for bimoass utilization networks. Comput Chem Eng 33:1770–1780CrossRefGoogle Scholar
  8. 8.
    Rentizelas AA, Tolis AJ, Tatsiopoulos IP (2009) Logistics issues of biomass: the storage problem and the multi-biomass supply chain. Renew Sustain Energy Rev 13:887–894CrossRefGoogle Scholar
  9. 9.
    Rentizelas AA, Tatsiopoulos IP, Tolis A (2009) An optimization model for multi-biomass tri-generation energy supply. Biomass Bioenerg 33:223–233CrossRefGoogle Scholar
  10. 10.
    Van Dyken S, Bakken BH, Skjelbred HI (2010) Linear mixed-integer models for biomass supply chains with transport, storage and processing. Energy 35:1338–1350CrossRefGoogle Scholar
  11. 11.
    Srivastava SK (2007) Green supply chain management: a state of the art literature review. Int J Manag Rev 9:53–80CrossRefGoogle Scholar
  12. 12.
    Bojarski AD, Laínez JM, Espuña A, Puigjaner L (2009) Incorporating environmental impacts and regulations in a holistic supply chains modeling: an LCA approach. Comput Chem Eng 33:1747–1759CrossRefGoogle Scholar
  13. 13.
    Puigjaner L, Guillén G (2008) Towards an integrated framework for supply chain management in the batch chemical process industry. Comput Chem Eng 32:650–670CrossRefGoogle Scholar
  14. 14.
    ISO14040 (1997) Environmental management: life cycle assessment: principles and framework. Technical report. ISO. GenevaGoogle Scholar
  15. 15.
    Guinee J, Gorree M, Heijungs R, Huppes G, Kleijn R, de Konig A, van Oers L, Sleeswijk A, Suh S, de Haes HU, de Brujin H, van Duin R, Huijbregts M, Lindeijer E, Roorda A, van-der Ven B, Weidema B (2001). Life cycle assessment. An operational guide to the ISO standards Part 3: scientific background. Ministry of Housing, Spatial Planning and the Environment (VROM) and Centre of Environmental Science, Leiden University (CML)Google Scholar
  16. 16.
    Graves SC, Willems SP (2003) Handbooks in operations research and management science. Supply chain management: design, coordination and operation,vol 11. Elsevier, AmsterdamGoogle Scholar
  17. 17.
    Humbert S, Margni M, Jolliet O (2005) Impact 2002+: user guide draft for version 2.1. Technical report. Industrial Ecology & Life Cycle Systems Group, GECOS. Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, SwitzerlandGoogle Scholar
  18. 18.
    Pennington D, Margni M, Amman C, Jolliet O (2005) Multimedia fate and human intake modeling: spatial versus non-spatial insights for chemical emissions in Western Europe. Environ Sci Technol 39:1119–1128CrossRefGoogle Scholar
  19. 19.
    Goedkoop M, Spriensma R (2001) The eco-indicator 99: a damage oriented methods for life cycle impact assessment methodology report. Technical report. Pré Consultants B.V., Amersfoort, The NetherlandsGoogle Scholar
  20. 20.
    Matthews HS, Hendrickson C, Weber C (2008) The importance of carbon footprint estimation boundaries. Environ Sci Technol 42:5839–5842CrossRefGoogle Scholar
  21. 21.
    Kondili E, Pantelides CC, Sargent RW (1993) A general algorithm for short term scheduling of batch operations. Comp Chem Eng 17:211–227CrossRefGoogle Scholar
  22. 22.
    Laínez J, Kopanos G, Espuña A, Puigjaner L (2009) Flexible design-planning of supply chain networks. AIChE J 55:1736–1753CrossRefGoogle Scholar
  23. 23.
    Heijungs R, Suh S (2002) The computational structure of life cycle assessment. Kluwer, Dordrecht, The NetherlandsGoogle Scholar
  24. 24.
    Laínez JM, Guillén-Gozálbez G, Badell M, Espuña A, Puigjaner L (2007) Enhancing corporate value in the optimal design of chemical supply chains. Ind Eng Chem Res 46:7739–7757CrossRefGoogle Scholar
  25. 25.
    Gomez A, Zubizarreta J, Rodrigues M, Dopazo C, Fueyo N (2010) An estimation of the energy potential of agro-industrial residues in Spain. Resour Conserv Recycling 54:972–984CrossRefGoogle Scholar
  26. 26.
    ECN-Biomass (2010) Phyllis database for biomass and waste. Energy Research Centre of the Netherlands (ECN). http://www.ecn.nl/phyllis. Accessed 12 Aug 2010
  27. 27.
    Pérez-Fortes M, Bojarski AD, Velo E, Puigjaner L (2010) IGCC power plants: conceptual design and techno-economic optimization. In: Clean energy: resources production and developments. Energy science engineering and technology. NOVA, New YorkGoogle Scholar
  28. 28.
    IEA-GHG (2008) Co-production of hydrogen and electricity by coal gasification with CO2 capture: updated economic analysis. Technical report, UKGoogle Scholar
  29. 29.
    EcoinventV1.3 (2008) The ecoinvent database v1.3. Technical report, Swiss Centre for Life Cycle InventoriesGoogle Scholar
  30. 30.
    PRe-Consultants-bv (2008) Simapro 7.1.6. Technical report, PRe-Consultants-bv. The NetherlandsGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • José Miguel Laínez
    • 1
  • Mar Pérez-Fortes
    • 1
  • Aarón D. Bojarski
    • 1
  • Luis Puigjaner
    • 1
  1. 1.ETSEIBUniversitat Politècnica de CatalunyaBarcelonaSpain

Personalised recommendations