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This contains a power-point presentation of the article found at http://www.aidic.it/cet/15/45/225.pdfwhich discusses the implementation of the P-graph methodology for the implementation of Industrial Symbiosis
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K. B. Avisoa,b, A.S.F. Chiuc, K. D. S. Yud, ,
M. A. B. Promentillaa,b L.F. Razona,b, A.T. Ubandob,e,
C. L. Syc and R. R. Tana,b
a Chemical Engineering DepartmentbCenter for Engineering and Sustainable Development Research
cIndustrial Engineering Departmentd School of Economics
e Mechanical Engineering DepartmentDe La Salle University Manila, Philippines
PRES 15
Kuching, Malaysia
August 23 – 27, 2015
Introduction
� Population growth coupled with climate
change are expected to aggravate issues on
resource scarcity
� Freshwater is a key resource for human
sustainability
� Industrial Ecology provides a systematic
framework to achieve sustainability
2
Industrial Ecology
• Popularised in 1989 by Frosch and Gallopoulos
• It utilizes an analogy between the industrial system and natural ecosystems (metabolism and symbiosis) to achieve sustainability
• Waste materials from one industry become inputs of another industry (Industrial symbiosis)
• IE is a systems approach towards sustainability
Reference: Frosch and Gallopoulos, 1989, Scientific American, 261, 94 - 102
Industrial
System
ComponentResources
Products
By-Products
Waste
Industrial
System
ComponentResources
Products
By-Products
Waste
Industrial
System
ComponentResources
Products
By-Products
Waste
Industrial Ecology
Industrial
System
Component
Industrial
System
Component
Material and
Energy
Exchange
Industrial
System
Component
Industrial System Industrial Eco-system
PRES 15
Kuching, Malaysia
August 23 – 27, 2015
Industrial Symbiosis
Kalundborg Eco-industrial Park, Denmark4
Reference: Ecodecision, Spring 1996 (20)
PRES 15
Kuching, Malaysia
August 23 – 27, 2015
Industrial Symbiosis (IS)
• The symbiotic relationships in industrial systems are encouraged by geographical proximity as in eco-industrial parks (EIP)(Ehrenfeld and Chertow, 2002)
• The exchange of common utilities such as energy and water are precursors to full-blown IS (Chertow, 2007)
• Optimization models prescribe designs to maximize benefits in IS (e.g. Lovelady and El-Halwagi, Chew and Foo, 2009)
5
PRES 15
Kuching, Malaysia
August 23 – 27, 2015
11 134 417
879
938
530
1817
934
7
1287
Process Systems Engineering (PSE)
in the Design of Water Exchange
Networks
6
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
PRES 15
Kuching, Malaysia
August 23 – 27, 2015
Issues on Industrial Symbiosis
• IS lends itself to uncertainties in the reliability of the
exchange networks (Liao et al., 2007)
• Formerly independent units are now highly
interconnected
• Variability in process streams exist due to seasonal
variations
• Risk assessment and management strategies should be
developed to handle system variability and reliability
7
PRES 15
Kuching, Malaysia
August 23 – 27, 2015
Input-Output Modelling
S-1
S-2
S-3
Wastes and Pollutants
Fin
al O
utp
uts
Reso
urc
e I
np
uts
System Boundary
8
PRES 15
Kuching, Malaysia
August 23 – 27, 2015
Input-Output Modelling
S-
1
S-
2
S-3
Wastes and Pollutants
Fin
al
Ou
tpu
ts
Reso
urc
e
Inp
uts
System Boundary
9
Interdependencies in IS
networks can be modelled using
Input-Output Analysis
PRES 15
Kuching, Malaysia
August 23 – 27, 2015
Problem Statement
� Given n resource sources, m resource sinks
� What is the optimal resource exchange network to reduce
fresh resource consumption? Minimize annual costs?
� Given a crisis event that results in the reduction in
capacity of one plant in the network, how should the
exchanges be modified to reduce system disruption or
failure?
10
PRES 15
Kuching, Malaysia
August 23 – 27, 2015
Optimization Model
�R���
���+ F� = D�∀j
�R���
���≤ S�∀i
�R��C� + F�C��
���≤ D�Q�∀j
11
min = �F�P��
���+ AC AC – annual costs
Ci – quality of source i
Dj – resource reqt of
demand j
Fj – amount of resource
delivered to sink j
PF – freshwater cost
Qj – required quality of
demand j
Rij – flowrate or recycle
stream
Si – available source
PRES 15
Kuching, Malaysia
August 23 – 27, 2015
P-graph Model
� Process graph or p-graph is a graph theoretic method
developed for process network synthesis
� P-graph utilizes 3 algorithms to identify the optimal
network structure
� MSG – maximal structure generation
� SSG – solution structure generation
� ABB – advanced branch and bound
� P-graph is a graphical representation of
matrix calculations such as MILP
12
RM1
P1
RM2OPERATING UNIT
PRES 15
Kuching, Malaysia
August 23 – 27, 2015
P-graph model of the IS
network
� Each plant is considered as a process unit
� Material/Energy flows are modeled as raw materials,
product or by-products
� Streams are pre-qualified based on process unit
requirements
Assumptions:
� Complete substitutability of available resources
13
PRES 15
Kuching, Malaysia
August 23 – 27, 2015
Case Study
� The case study is taken from Keckler and Allen (1998)
� The reuse and treatment of water is considered between 3
industrial plants in an EIP considering the establishment
of a water treatment facility
� A scenario on capacity reduction is investigated
14
11 134 417
879
938
530
1817
934
7
1287
PRES 15
Kuching, Malaysia
August 23 – 27, 2015
Water Limiting Data
Plant Water need
(cu m/d)
Input Quality
(ppm)
Output Quality
(ppm)
(TOC, TSS, TDS) (TOC, TSS, TDS)
M 42 25, 500, 2500 1928, 2639, 7824
O 3,600 25, 25, 200 484, 105, 904
P 4,940 5, 100, 500 8, 22, 276
Fresh water n/a n/a 0,1,140
15
PRES 15
Kuching, Malaysia
August 23 – 27, 2015
Water Quality of Treatment
Processes
16
Treatment
Step
Symbol Output Quality
(TOC, TSS, TDS)
Treatment
cost ($/cu m.)
Primary and
Secondary
A 20,30, 1000 1.45
Filtration and
Precipitation
B 5, 10, 500 0.11
Reverse
Osmosis
C 5, 1, 10 1.58
Freshwater S 0, 1, 140 n/a
Hub H n/a 0.53
PRES 15
Kuching, Malaysia
August 23 – 27, 2015
Conclusion
� 9 feasible solutions have been generated
� The solution vary in degree of recycling and water
treatment
� The solutions provide insight on potential risk
management strategies to deal with failures in IS
networks
24
PRES 15
Kuching, Malaysia
August 23 – 27, 2015
Future Work
� Integration of additional criteria for evaluating sub-
optimal solutions
� Implementation of P-graph framework in consideration of
multiple product/by-product exchanges in IS networks
� Implement without pre-qualifying streams
25
PRES 15
Kuching, Malaysia
August 23 – 27, 2015
Acknowledgment
� The authors would like to thank the Department of
Science Technology for funding this research
26
PRES 15
Kuching, Malaysia
August 23 – 27, 2015
THANK YOUFor comments and suggestions you may also contact me at:
Tel. No.: + 632 – 5244611 loc 127
Email: [email protected]
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