Skip to main content

Advertisement

SpringerLink
Log in

Synthesis of regional networks for the supply of energy and bioproducts

  • Published:
Clean Technologies and Environmental Policy Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Abbreviations

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

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

J o :

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

J e :

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)

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

j o :

Index for demand locations at local level

j e :

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

f yb :

Cost coefficient for yearly basis

q m,L2 :

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

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

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

HY pi :

Yield for product pi, t/(km2 y)

A i :

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)

c fix,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

N i :

The size of set I

N m :

The size of set M

N n :

The size of set N

N j :

The size of set J

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

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

ENVB e :

Total environmental burden type e, t/y

c tr :

Transportation costs, €/y

c op :

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

c inv :

Annual investment costs, €/y

c pi :

Raw material costs for product pi, €/t

P B :

Profit before taxes, €/y

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

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

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

References

  • Brooke A, Kendrick D, Meeraus A, Raman R (2005) GAMS: a user guide. The Scientific Press, Washington

  • 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, 64

  • Dunnett A, Adjiman C, Shah N (2007) Biomass to heat supply chains: applications of process optimization. Process Saf Environ Prot 85(5):419–429

    Article  CAS  Google Scholar 

  • Ecoheatcool, Euroheat & Power Initiative (2006) Ecoheatcool work package 1. The European Heat Market, Final Report. www.euroheat.org. Accessed 18 January 2010

  • European Fusion Development Agreement (EFDA) (2005) Energy, powering your world. www.efda.org. Accessed 18 January 2010

  • Friedler F, Tarjan K, Huang YW, Fan LT (1992) Graph-theoretical approach to process synthesis: axioms and theorems. Chem Eng Sci 47(8):1973–1988

    Article  CAS  Google Scholar 

  • Gwehenberger G, Narodoslawsky M (2008) Sustainable processes—the challenge of the 21st century for chemical engineering. Process Saf Environ Prot 86(5):321–327

    Article  CAS  Google Scholar 

  • Lam HL, Varbanov P, Klemeš J (2010a) Minimising carbon footprint of regional biomass supply chains. Resour Conserv Recycl 54(5):303–309

    Google Scholar 

  • Lam HL, Varbanov P, Klemeš J (2010b) Optimisation of regional energy supply chains utilizing renewables: P-graph approach. Comput Chem Eng 34(5):782–792

    Article  CAS  Google Scholar 

  • Lambert DK, Middleton J (2010) Logistical design of a regional herbaceous crop residue-based ethanol production complex. Biomass Bioenergy 34:91–100

    Article  Google Scholar 

  • 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):887–894

    Article  Google Scholar 

  • 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):1206–1223

    Google Scholar 

Download references

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.

Author information

Authors and Affiliations

  1. Faculty of Chemistry and Chemical Engineering, University of Maribor, Smetanova ul. 17, 2000, Maribor, Slovenia

    Lidija Čuček, Hon Loong Lam & Zdravko Kravanja

  2. Centre for Process Integration and Intensification—CPI², Research Institute of Chemical and Process Engineering, Faculty of Information Technology, University of Pannonia, Egyetem u. 10, 8200, Veszprém, Hungary

    Lidija Čuček, Hon Loong Lam, Jiří J. Klemeš & Petar S. Varbanov

Authors
  1. Lidija Čuček
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hon Loong Lam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiří J. Klemeš
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Petar S. Varbanov
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zdravko Kravanja
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lidija Čuček.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Čuček, L., Lam, H.L., Klemeš, J.J. et al. Synthesis of regional networks for the supply of energy and bioproducts. Clean Techn Environ Policy 12, 635–645 (2010). https://doi.org/10.1007/s10098-010-0312-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10098-010-0312-6

Keywords

Search

Navigation