modelling stakeholder objectives

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Modelling Stakeholder Objectives

in the Design of IndustrialSymbiotic Networks: A systems

approach

Kathleen B. Aviso, Ph.D.

Paterno and Natividad Professorial Chair in EngineeringChemical Engineering Department, De La Salle University Manila


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International Conference on Eco-Industrial Development

October 29 - 30, 2014

Shanghai Jiao Tong University, Shanghai China

About DLSUDe La Salle University is a private, non-profit university

established by the Brothers of the Christian Schools (FSC)

in Manila, Philippines in 1911


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October 29 - 30, 2014


Key Statistics of DLSU

1000 academic staff (50% full time)

20,000 undergraduate students

4,000 graduate students (masters + Ph.D.)

3,000 degrees granted per year (10% postgraduate) 6 ha. (Manila campus) + 50 ha. (STC campus)

1200+ Scopus-indexed publications (h-index = 40)

2014 QS World Ranking 601-650

2014 QS Asian University Ranking 151-160


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October 29 - 30, 2014


DLSU Researchers in Related Fields

Name Tools Applications

Prof. R. Tan Mathematical Programming,

P-graph, Decision Analysis,

Fuzzy optimization

Energy supply chains, industrial

complexes, Industrial symbiosis, life

cycle systems

Prof. A. Chiu Systems analysis Industrial symbiosis, Circular

economy

Dr. KB Aviso* Fuzzy, multi-objective, bi-

level optimization

Industrial symbiosis, supply chains

Dr. MAB Promentilla* Multi-criterion decision

analysis

Low-carbon energy

Prof. LF Razon LCA Biomass production with focus on N-

footprint

Dr. C Sy Robust optimization Energy planning

Dr. AT Ubando LCA, fuzzy optimization Algae cultivation, polygeneration

systems

Dr. KDS Yu* Input-output models, risk

analysis

Industrial networks and supply chains


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October 29 - 30, 2014


Network in

Co-authorship in

Industrial

SymbiosisResearch(Yu et al., 2013)


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October 29 - 30, 2014


Network in

Co-authorship in

Industrial

SymbiosisResearch(Yu et al., 2013)


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October 29 - 30, 2014


if the neoclassical economic model of human behavior is

to be believed then every individual actoris also

essentially a self-interested maximizer of individual profit

- Jackson and Clift, 1998

7


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8

Introduction Population and the onset of

climate change will impactthe availability of

freshwater resources

Freshwater availability has

been identified as a key

indicator for humansurvival (Rockstrom et al., 2009)


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Industrial Ecology

Popularised in 1989 by Frosch and Gallopoulos

Utilizes an analogy between the industrial system and naturalecosystems (metabolism and symbiosis) to achievesustainability

Waste materials from one industry become inputs of anotherindustry (Industrial symbiosis)

IE is a systems approach towards sustainability

Reference: Frosch and Gallopoulos, 1989, Scientific American, 261, 94 - 102

Industrial

SystemComponent

Resources

Products

By-ProductsWaste

Industrial

System

ComponentResources

Products

By-ProductsWaste

IndustrialSystem

ComponentResources

Products

By-Products

Waste

Industrial Ecology

IndustrialSystem

Component

Industrial

System

Component

Material and

Energy

Exchange

IndustrialSystem

Component

Industrial System Industrial Eco-system


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October 29 - 30, 2014


Industrial Symbiosis

Kalundborg Eco-industrial Park, Denmark10

Reference: Ecodecision, Spring 1996 (20)


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October 29 - 30, 2014


Other Examples of

Eco-industrial ParksIndustrial Park Location

Kwinana Australia

Chamusca Portugal

Forth Valley and Grangemouth UK

Landskrona Sweden

Kawasaki Japan

Tianjin China

Ulsan Korea

11

Adapted from Zhu and Ruth (2014)


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October 29 - 30, 2014


Industrial Symbiosis (IS)

The symbiotic relationships in industrial systems areencouraged by geographical proximity as in eco-industrial

parks (EIP)(Ehrenfeld and Chertow, 2002)

The exchange of common utilities such as energy and waterare precursors to full-blown IS (Chertow, 2007)

Optimization models prescribe designs to maximize benefits inIS (e.g. Lovelady and El-Halwagi, Chew and Foo, 2009)


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October 29 - 30, 2014


11 134 417

879

938

530

1817

934

7

1287

Process Systems Engineering (PSE)

in the Design of water exchange

networks

13

1

3

2

4

FW

WW

Optimized Network

Plant A

Plant B

Plant E

SR1

SK1

Plant C

SK3

SR2

SK2

Plant D

SR3

SR4

SK4

SR5

200 t/h 1,221.38 t/h

422.53 t/h

78.62 t/h

1,000 t/h

3,500 t/h

2,501.15 t/h

512.07 t/h

1,987.93 t/h

Centralized

Regeneration

UnitCR= 500 ppm

FW

1,000 t/h

498.85 t/h

WW12.07 t/h

78.62 t/h

Initial PSE models

identified designs for a

single decision-maker


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October 29 - 30, 2014


Issues in Industrial Symbiosis

The main stumbling block for forging networks is thatparticipating plants have conflicting objectives (Tan, 2008)

IS lends itself to uncertainties in the reliability of the exchange

networks (Liao et al., 2007) Initial models for industrial symbiosis have failed to integrate

stakeholder interests

Initial models have assumed that the optimal design for the

whole system is the same for the individual participants

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October 29 - 30, 2014


Modeling participant

goals A fuzzy optimization model is

developed to integrate

participant goals in optimizing

the design

It results in a design which

satisfices the individual

objectives of participating

plants

15


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October 29 - 30, 2014


Developing the Optimization

Model

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The objective is to

maximize the satisfaction of

the least satisfied

participant


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Modeling Participant

Goals

Plant Original Cost % Savings

System

Optimum

Fuzzy

Optimum

M 90.75 5.64 7.72

O 7,812.75 0.60 5.43

P 3,575.00 35.22 23.83

Over-all 11,478.50 11.42 11.18

17

11 134 417

879

938

530

1817

934

7

1287

Benefits of IS can be more equitably distributed using fuzzy optimization

(Keckler and Allen, 1f999)

ikik

N

'k

N

j

'ikjk SWrK 'JK

k,i

'jk'jk

N

k

N

i

'ikjk DFrK IK

'k,j

'jkm'jk,inU

'jkm,F

N

k

N

i

'ikjkikm,out DQFQrQK IK

m,'k,j


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October 29 - 30, 2014


Modeling Participant

Goals

Plant Original Cost % Savings

System

Optimum

Fuzzy

Optimum

M 90.75 5.64 7.72

O 7,812.75 0.60 5.43

P 3,575.00 35.22 23.83

Over-all 11,478.50 11.42 11.18

18

11 134 417

879

938

530

1817

934

7

1287

Benefits of IS can be more equitably distributed using fuzzy optimization

(Keckler and Allen, 1f999)

ikik

N

'k

N

j

'ikjk SWrK 'JK

k,i

'jk'jk

N

k

N

i

'ikjk DFrK IK

'k,j

'jkm'jk,inU

'jkm,F

N

k

N

i

'ikjkikm,out DQFQrQK IK

m,'k,j


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October 29 - 30, 2014


Modelling the supply chains

How do you meet the demandfor products when you need to

consider environmental

constraints?

Environmental constraints differ

between economic or national

regions

19


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Modelling the Supply Chain

More efficient productiontechnologies may exist in other

regions

Find a balance between

consumption based and

production based targets

An optimal exchange network

can reduce regional

environmental stress

20


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October 29 - 30, 2014


Modeling the role of

government Implementation of IS

networks may be of interest

topolicy makers

The government can

influence participating

companies through

incentives or disincentives

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*This article received an Editorial

commendation for being a highly

cited article in Trans. IChemE

Part B (2012)


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October 29 - 30, 2014


A multi-level Stackelberg

approach

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FOLLOWERS

LEADER: EIP Authority

Minimize Freshwater Consumption

Subject to:Objectives of individual plantsFreshwater unit costWastewater treatment unit costSubsidy rate fraction

Plant 1Minimize Cost1Subject to:Water balancesWater qualityconstraintsTopologicalconstraints

Plant nMinimize CostnSubject to:Water balancesWater qualityconstraintsTopologicalconstraints

Plant 2Minimize Cost2Subject to:Water balancesWater qualityconstraintsTopologicalconstraints

Solve objectivefunction of leader

Solve objectivefunction offollowers

Do thesolutionscoincide?

Set-up membershipfunctions for leader and

followers

No

Maximize levels of satisfactionof leader and followers

simultaneously

Is thesolution

feasible?

Leader adjuststolerances and

controlvariables

No

Optimal SolutionYes

SatisficingYes


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October 29 - 30, 2014


Modeling the role of

government

0

0.

4

0.

8

1.

2

1.

6

2

0

0.3

0.6

0.

9

1.

2

1.5

1.

8

160

180

200

220

240

260

flowrate of freshwater

(t/day)

unit cost for wastewater

$/tunit cost for freshwater

$/t

240-260

220-240

200-220

180-200

160-180

Identifyingpolicies

which will influence

participant objectives

is key


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October 29 - 30, 2014


When information is not

complete The participation of several inde-

pendently operating plants results

in incomplete information

exchange due to confidentiality

issues

Designing the exchange network

should be able to work in the

absence of information

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Modeling incomplete information

Process characteristics may

be modeled depending on

the amount of informationthat is divulged

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Fixed Flow

Fixed Concentration

Fixed Flow

Fixed Concentration

(b) BLACK BOX

Variable Flow

ariable Concentration

Variable Flow

Variable Concentration

(c) GRAY BOX

(a) WHITE BOX

Variable Flow


Variable Flow


Fixed Flow

Fixed Concentration

Fixed Flow

Fixed Concentration

(b) BLACK BOX

Variable Flow

ariable Concentration

Variable Flow


(c) GRAY BOX

(a) WHITE BOX

Variable Flow


Variable Flow



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When the future is

uncertain IS lends itself to uncertain

futures as the fate and plans

of independent plants are

not revealed completely

Networks should be robust

in consideration of

uncertainties

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Robust Optimization Model

Objective function

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Scenario 1

Scenario 2

Scenario 3

A feasible structure in onescenario may be infeasible in

another

Freshwater reduction:

85% in Scenario 1

76% in Scenario 2

Infeasible in Scenario 3

Optimal Networks


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Modeling uncertain futures in

Eco-industrial networks The robust network is that which remains feasible

regardless of which future takes place

Robust design may beused in planning the design

of eco-industrial networks

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Conclusions

In the design of eco-industrial networks it is important to

consider issues resulting from the multi-stakeholdernature of

the problem

The independent goals of the participants can be integrated

through fuzzy optimization

Modeling the role of government is done through a multi-levelStackelberg game approach

It is possible to identify the design of a network even in the

presence of incomplete information

A robust design is that which remains feasible regardless of

which scenario takes place

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Future and on-going work

Future work will explore the development ofp-graph models for industrial network

synthesis and optimization

Game theoretic concepts such as the Shapley

value can be utilized to identify the

appropriate distribution of ISbenefits

Integrating social indicators into the model

Integration of the concept of riskand

resilience in identifying optimal networks

Use of decision analysis methods such as

AHP

Developing dynamic models to model theevolution of EIPs

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Further Reading

Aviso, K.B. (2013) Design of robust water exchange networks for eco-

industrial symbiosis.Process Safety and Environmental Protection (in

press, dx.doi.org/10.1016/j.psep.2012.12.001).

Aviso, K. B., Tan, R. R., Culaba, A. B, Foo, D. C. Y. and Hallale, N. (2011)

Fuzzy optimization of topologically constrained eco-industrial resource

conservation networks with incomplete information.Engineering

Optimization, 43: 257 279. Aviso, K. B., Tan, R. R. and Culaba, A. B. (2010) Designing Eco-Industrial

Water Exchange Networks Using Fuzzy Mathematical Programming. Clean

Technologies and Environmental Policy, 12, 353 362.

Aviso, K. B., Tan, R. R., Culaba, A. B. and Cruz, J. B. (2010) Bi-Level

Fuzzy Optimization Approach for Water Exchange in Eco-Industrial Parks.

Process Safety and Environmental Protection 88: 31 40.

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Thank you

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modelling stakeholder objectives

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