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Clean Technologies and Environmental Policy

, Volume 12, Issue 6, pp 635–645 | Cite as

Synthesis of regional networks for the supply of energy and bioproducts

  • Lidija ČučekEmail author
  • Hon Loong Lam
  • Jiří J. Klemeš
  • Petar S. Varbanov
  • Zdravko Kravanja
Article

Abstract

This article presents a method for the synthesis of regional renewable energy supply chains, based on Mixed-Integer Linear Programming (MILP). This method addresses the challenges presented by biomass resources. The main challenges are the distributive and varied availabilities regarding both location and time. This work also aims to maximise the economically viable utilisation of resources, accounting for the competition between energy and food production. A four-layer supply chain superstructure has been developed, which includes the harvesting, preparation, core processing and distribution of products. This considered system’s boundaries involve a region, which is then divided into zones for optimising conversion operations and transportation flows. An MILP model has been formulated with profit maximisation as the optimisation criterion. The environmental impact is evaluated by the carbon footprint. The sensitivity of the optimal solutions is analysed for different regions’ sizes, transportation costs, pre-processing alternatives and the co-production of food and energy.

Keywords

Biomass supply chains Bioenergy generation Carbon footprint Regional energy and food networks 

List of symbols

Superscripts

UP

Upper bound

LO

Lower bound

L1

Harvesting and supply layer

L2

Collection and pre-processing layer

L3

Main processing layer

L4

Use layer

c

Competing

conv

Conversion

tr

Transport

road

Road conditions

yb

Yearly basis

op

Operating costs

inv

Investment costs

fix

Fixed part of the annualized investment

var

Cost coefficient of the annualized investment

price

Price of the products

Sets

I

Set of supply zones

M

Set of collection and intermediate process centres

N

Set of process plants

T

Set of technology options

P

Set of products

J

Set of demand locations

E

Set of environmental impacts

Subsets

Jo

Set of demand locations at local level (subset of J)

Je

Set of demand locations for export (subset of J)

PI

Set of intermediate products (subset of P)

PD

Set of directly used products (subset of P)

PP

Set of produced products from plants (subset of P)

PIP = PI × PP

Set of pairs of intermediate product and produced product (if a produced product is produced from a given intermediate product)

PT = PI × T

Set of pairs of intermediate product and applicable process technology for it

PIC

Set of intermediate products competing for food and energy production (subset of PI)

Indexes

i

Index for supply zones

m

Index for collection and intermediate process centres

n

Index for process plants

t

Index for technology options

p

Index for products

j

Index for demand locations

jo

Index for demand locations at local level

je

Index for demand locations for export

pi

Index for intermediate products

pd

Index for directly used products

pp

Index for produced products from plants

pic

Index for intermediate products competing for food and energy production

e

Index for environmental impacts

Scalars

fyb

Cost coefficient for yearly basis

qm,L2

Total mass-flow at collection centre m, t/y

Parameters

\( q_{pi}^{m,{\text{L}}1,\text{L}2} \)

Product’s mass-flow at collection centre m, t/y

\( {{Dem}}_{{j^{\text{o}} ,p}} \)

Regional demand at location j o for product p, t/y or MWh/y or MJ/y

HYpi

Yield for product pi, t/(km2 y)

Ai

Total available area, km2

\( f_{pi}^{{{\text{conv}},{\text {L2}}}} \)

Conversion factor of intermediate product pi by pre-processing

\( q_{t}^{m,\text{L}3} \)

Inlet mass-flow to the selected technology, t/y

\( f_{pi,pp,t}^{{{\text{conv}},{\text {L3}}}} \)

Conversion factor of intermediate product pi by processing

\( c_{p}^{{{\text{tr}},{\text{L}}a,{\text{L}}b}} \)

Transportation cost coefficient of product from layer a to the layer b, €/(t km)

cfix,inv,L2

Fixed investment costs by pre-processing, €/y

\( c_{t}^{{{\text{fix}},{\text{inv}},{\text {L3}}}}\)

Fixed part of investment costs by processing, €/y

\( c_{t}^{{{\text{var}},{\text{inv}},{\text {L3}}}} \)

Variable part of investment cost by processing, €/t

\( ei_{p,e}^{{{\text{tr}},{\text{L}}a,{\text{L}}b}} \)

Transport environmental impact factor from layer a to the layer b, \( t_{{{\text{CO}}_{2} }} \)/(t km)

\( ei_{pi,e}^{{\text {L2}}} \)

Environmental impact factor caused by pre-processing, \( t_{{{\text{CO}}_{2} }} \)/t

\( ei_{pi,t,e}^{\text{L}3} \)

Environmental impact factor by the processing, \( t_{{{\text{CO}}_{2} }} \)/t

\( D_{x,y}^{{{\text{L}}a,{\text{L}}b}} \)

Distance between object x in layer a and object y in layer b, km

\( f_{x,y}^{{{\text{road}},{\text{L}}a,{\text{L}}b}} \)

Road condition factor between object x in layer a and object y in layer b

\( c_{p}^{\text{price}} \)

Price of the product, €/t or €/MWh or €/MJ

Ni

The size of set I

Nm

The size of set M

Nn

The size of set N

Nj

The size of set J

Variables

\( q_{i,pi}^{m,\text{L}1} \)

Production rate of intermediate product pi at supply zone i, t/y

\( A_{{i,{pic}}}^{\text{c}} \)

Competing area for food and energy at zone i for product pi, km2

\( q_{x,y,p}^{{m,{\text{L}}a,{\text{L}}b}} \)

Mass-flow of product p from object x in layer a to object y in layer b, t/y

\( q_{n,pi,t}^{m,T,\text{L}2,\text{L}3} \)

Mass-flow of intermediate product pi to the selected technology t at the process plant n, t/y

\( q_{n,pi,pp,t}^{m,T,\text{L}2,\text{L}3} \)

Mass-flow of produced products pp from intermediate product pi with the selected technology t at the process plant n, t/y

ENVBe

Total environmental burden type e, t/y

ctr

Transportation costs, €/y

cop

Operating costs, €/y

\( c_{pi}^{{{\text{op}},\text{L}2}} \)

Operating costs by the pre-processing for product pi, €/t

\( c_{pi,t}^{{{\text{op}},\text{L}3}} \)

Operating costs by the processing for product pi and technology t, €/t

cinv

Annual investment costs, €/y

cpi

Raw material costs for product pi, €/t

PB

Profit before taxes, €/y

Binary variables

\( y_{m}^{\text{L}2} \)

Binary variable for existence of collection and intermediate process centre m

\( y_{n,t}^{\text{L}3} \)

Binary variable for existence of technology t at process plant n

Abbreviations

MILP

Mixed-integer linear programming

RES

Renewable energy sources

MIP

Mixed-integer programming

MSW

Municipal solid waste

DDGS

Distillers dried grains with solubles

CFP

Carbon footprint

GAMS

General algebraic modelling system

MOO

Multi-objective optimisation

Notes

Acknowledgement

This work has been carried out as part of a Collaborative PhD study at the University of Maribor and the University of Pannonia, supported by the Bilateral SI-HU Project TET SI-11/2008 ‘Process systems engineering and sustainable development’. Also the financial supports from the EC project Marie Curie Chair (EXC) MEXC-CT-2003-042618 ‘Integrated Waste to Energy Management to Prevent Global Warming—INEMAGLOW’ and from the Slovenian Research Agency (Program No. P2-0032, Project No. L2-0358 and PhD research fellowship contract No. 1000-08-310074) are gratefully acknowledged.

References

  1. Brooke A, Kendrick D, Meeraus A, Raman R (2005) GAMS: a user guide. The Scientific Press, WashingtonGoogle Scholar
  2. Čuček L, Kravanja Z (2010) LCA-based MINLP synthesis of a bioethanol production network. In: 2010 AIChE spring meeting & 6th global congress on process safety, San Antonio, Texas, Paper 41c, 64Google Scholar
  3. Dunnett A, Adjiman C, Shah N (2007) Biomass to heat supply chains: applications of process optimization. Process Saf Environ Prot 85(5):419–429CrossRefGoogle Scholar
  4. Ecoheatcool, Euroheat & Power Initiative (2006) Ecoheatcool work package 1. The European Heat Market, Final Report. www.euroheat.org. Accessed 18 January 2010
  5. European Fusion Development Agreement (EFDA) (2005) Energy, powering your world. www.efda.org. Accessed 18 January 2010
  6. Friedler F, Tarjan K, Huang YW, Fan LT (1992) Graph-theoretical approach to process synthesis: axioms and theorems. Chem Eng Sci 47(8):1973–1988CrossRefGoogle Scholar
  7. Gwehenberger G, Narodoslawsky M (2008) Sustainable processes—the challenge of the 21st century for chemical engineering. Process Saf Environ Prot 86(5):321–327CrossRefGoogle Scholar
  8. Lam HL, Varbanov P, Klemeš J (2010a) Minimising carbon footprint of regional biomass supply chains. Resour Conserv Recycl 54(5):303–309Google Scholar
  9. Lam HL, Varbanov P, Klemeš J (2010b) Optimisation of regional energy supply chains utilizing renewables: P-graph approach. Comput Chem Eng 34(5):782–792CrossRefGoogle Scholar
  10. Lambert DK, Middleton J (2010) Logistical design of a regional herbaceous crop residue-based ethanol production complex. Biomass Bioenergy 34:91–100CrossRefGoogle Scholar
  11. 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(4):887–894CrossRefGoogle Scholar
  12. Uslu A, Faaij APC, Bergmann PCA (2008) Pre-treatment technologies, and their effect on international bioenergy supply chain logistics. Techno-economic evaluation of torrefaction, fast pyrolysis and pelletisation. Energy 33(8):1206–1223Google Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Lidija Čuček
    • 1
    • 2
    Email author
  • Hon Loong Lam
    • 2
    • 1
  • Jiří J. Klemeš
    • 2
  • Petar S. Varbanov
    • 2
  • Zdravko Kravanja
    • 1
  1. 1.Faculty of Chemistry and Chemical Engineering, University of MariborMariborSlovenia
  2. 2.Centre for Process Integration and Intensification—CPI², Research Institute of Chemical and Process Engineering, Faculty of Information TechnologyUniversity of PannoniaVeszprémHungary

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