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7/25/2019 Keynote lecture ESCAPE 2016
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P-Graph Approach to Carbon-Constrained
Energy Planning Problems
Raymond R. Tan1, Kathleen B. Aviso1, Dominic C.Y. Foo2
1Chemical Engineering Department
De La Salle University, Manila, Philippines2Chemical Engineering Department
University of Nottingham Malaysia, Selangor, Malaysia
26th European Symposium on Computer-Aided Process
Engineering, June 12 - 15, 2016 Portoro, Slovenia
7/25/2019 Keynote lecture ESCAPE 2016
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My co-authors
26th European Symposium on Computer-Aided Process
Engineering, June 12 - 15, 2016 Portoroz, Slovenia
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Prof. Dominic C.Y. Foo
University of Nottingham Malaysia Campus
Prof. Raymond Tan
De La Salle University, Manila, Philippines
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Outline of Presentation
Global Energy/Climate Scenario
Generalized Source/Sink Model
P-graph methodology
Problem structure in planning CO2 abatement technologies
Case study
Results, Conclusions and Future work
26th European Symposium on Computer-Aided Process
Engineering, June 12 - 15, 2016 Portoroz, Slovenia
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Exceeding the Limits(Rockstrom et al., 2009)
Atmospheric CO2 levels
now exceed 400 ppm
Global GHG emissions
continue to grow, fuelled
by economic and
demographic trends
Climate change has
complex links with other
issues e.g.,
biodiversity loss, water
stress, land use
26th European Symposium on Computer-Aided Process
Engineering, June 12 - 15, 2016 Portoroz, Slovenia
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Selected Energy Statistics
Indicator Value Source
Projected primary energy demand
2040
750 - 860 x 1018 J IEA (2015)
Projected CO2 emissions in 2040 45 x 109 t/y EIA (2016)
World petroleum reserves-to-
production (R/P) ratio in 2001
39 y BP (2002)
Projected global consumption of oil,
NG and coal in 2020
255, 178 and 129 x
1018 J
EIA (2002)
Estimated land requirement to
supply bioenergy
0.74-1.94 ha/capita Nonhebel (2005)
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Engineering, June 12 - 15, 2016 Portoroz, Slovenia
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Some Recent Energy Trends
Strong demand growth for reliable, low-cost energy, especially inthe developing world, will continue for decades
Nuclear energy has fallen into disfavor as a result of the
Fukushima crisis in 2011
Non-conventional gas is now accessible; CO2 emissions, ratherthan supply, will be the constraint to its use.
Transport trends (EVs and HEVs) may cause shifts in patterns of
energy use
There still remain inherent limitations to various forms ofrenewable energy
26th European Symposium on Computer-Aided Process
Engineering, June 12 - 15, 2016 Portoroz, Slovenia
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CO2 Abatement Technology Wedges(Source: International Energy Agency)
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Engineering, June 12 - 15, 2016 Portoroz, Slovenia
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Basic Problem Pattern
Minimize use of scarce, high-quality stream
Each stream source has fixed quality and quantity characteristics
Each stream demand has fixed quality and quantity requirements
Quality index is inverse and follows a linear mixing rule
26th European Symposium on Computer-Aided Process
Engineering, June 12 - 15, 2016 Portoroz, Slovenia
7/25/2019 Keynote lecture ESCAPE 2016
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Source: A streamwhich containsthe targeted
species. Eachsource has:Flowrate FiQuality QiQuality load:
mi = Fi Qi
Source/sink representation
Sink: An existingprocess unit/equipment that canaccept a source.
Each sink has:Flowrate FjQuality Qj where:Qj
min Qj Qjmax
Load capacity:mi = Fi Qi
Source i
j = 1
j = 2
Sinkj
j = 3
i = 1
i = 2
i= 3
(El-Halwagi, 2006)
26th European Symposium on Computer-Aided Process
Engineering, June 12 - 15, 2016 Portoroz, Slovenia
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Streams and Qualities in Pinch Analysis
Flows Qualities Examples/Problems
Heat Temperature
Heat integration (1971, 1979)
Total site integration (1984)
Integration of thermal equipments (1982)
Mass Concentration
Mass integration (1989)
Water/Hydrogen management(1994,1996)
Pollution prevention/Treatment networks
Mass Properties Recycle/reuse networks (2004)
Steam Pressure Cogeneration (1993, 2008)
Energy CO2 Carbon-constrained energy planning (2007)
Mass Time Supply chain management (2002)
Energy TimeStand-alone energy system (2007)
Isolated power system (2007)
26th European Symposium on Computer-Aided Process
Engineering, June 12 - 15, 2016 Portoroz, Slovenia
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The Solution Strategy
In the simplest case, the objective is to
identify the minimum external resource
needed by maximising use of internal
sources.
Problem components:
Targeting identification of optimal
resource budget
Network design matching sources
and sinks to achieve target
i = 1
i = 2
i = 3
i =NSR
SOURCE
j = 1
j = 2
j = 3
j =NSK
SINK
26th European Symposium on Computer-Aided Process
Engineering, June 12 - 15, 2016 Portoroz, Slovenia
7/25/2019 Keynote lecture ESCAPE 2016
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Optimization Model
Objective Function
Balance at Sink
Balance at Source
Quality Constraint
26th European Symposium on Computer-Aided Process
Engineering, June 12 - 15, 2016 Portoroz, Slovenia
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P-graph Methodology
Process graph or p-graph is a graph theoretic method
developed for process network synthesis (Friedler et al., 1992,
1993)
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 Provides near optimal solutions
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RM1
P1
RM2OPERATING UNIT
26th European Symposium on Computer-Aided Process
Engineering, June 12 - 15, 2016 Portoroz, Slovenia
7/25/2019 Keynote lecture ESCAPE 2016
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Recent applications of P-graph
Application Authors
Vehicle maintenance scheduling Adonyi et al. (2013)
Industrial symbiosis networks Aviso et al., (2015a)
Allocation of in economic systems during crises Aviso et al., (2015b)
Planning of CO2 capture and storage systems Chong et al. (2014)
Planning of building evacuation during
emergenciesGarcia-Ojeda et al. (2012)
Planning of supply chains in the Energy-Water-
Food Nexus Heckl et al. (2015b)
Open-structure biomass networks Lam et al. (2013)
Renewable energy systems for cities Maier and Narodoslawsky (2014)
Biomass supply chains with consideration of
occupational safety Ng et al. (2015)
Operation of polygeneration plants underabnormal conditions Tan et al. (2014)
Inoperability risk allocation in urban infrastrcuture Tan et al. (2015)
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Problem Statement
There are Mnumber of energy sources with supply limit and
specified CO2 intensity
There are Nenergy demands with given energy requirement and
maximum tolerable CO2 intensity
A high quality energy source (e.g. zero CO2 intensity) is externally
available
Determine the optimal allocation of energy sources which minimizes
the zero-carbon energy source and meets energy demand of sinks
26th European Symposium on Computer-Aided Process
Engineering, June 12 - 15, 2016 Portoroz, Slovenia
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P-Graph Source-Sink Model
SOURCE 1
S1
SOURCE 2
S2
DEMAND 1
D1
DEMAND 2
D2
Superstructure for 2 sources and
2 sinks
P-graph representation for 2
sources and 2 sinks
26th European Symposium on Computer-Aided Process
Engineering, June 12 - 15, 2016 Portoroz, Slovenia
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P-Graph Source-Sink Model
26th European Symposium on Computer-Aided Process
Engineering, June 12 - 15, 2016 Portoroz, Slovenia
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P-Graph Source-Sink Model
AvailableSource
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Engineering, June 12 - 15, 2016 Portoroz, Slovenia
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P-Graph Source-Sink Model
Energy
Demands 19
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P-Graph Source-Sink Model
QualityConstraints
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Engineering, June 12 - 15, 2016 Portoroz, Slovenia
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P-Graph Source-Sink Model
Flow ofresources
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Engineering, June 12 - 15, 2016 Portoroz, Slovenia
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Case Study(Tan and Foo, 2007)
Energy Source Emission Factor
(t CO2/TJ)
Available Sources
(TJ)
Coal 105 600,00
Oil 75 800,00
Natural Gas 55 200,00
Zero-carbon 0 > 400,00
Total > 2,000,000
Energy Demand Emission Limit
(x 106 t CO2)
Expected Consumption
(TJ)
Region 1 20 1,000,000
Region 2 20 400,000
Region 3 60 600,000
Total 100 2,000,000
26th European Symposium on Computer-Aided Process
Engineering, June 12 - 15, 2016 Portoroz, Slovenia
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P-graph Representation
ZERO
CARBON
NATURAL
GASOILCOAL
REGION 3REGION 2REGION 1
ENERGY
SOURCE
ENERGY
DEMAND26th European Symposium on Computer-Aided Process
Engineering, June 12 - 15, 2016 Portoroz, Slovenia
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Optimal Solution
Source Region 1
(TJ)
Region 2
(TJ)
Region 3
(TJ)
Total
(TJ)
Coal 0.00 0.00 186,667 186,667
Oil 266,667 120,000 413,333 800,000
Natural Gas 0.00 200,000 0.00 200,000
Zero Carbon 733,333 80,000 0.00 813,333
Total 1,000,000 400,000 600,000
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Near Optimal Network
Source Region 1 Region 2 Region 3 Total
Coal 0.00 0.00 540,000 540,000
Oil 266,667 164,000 0.00 430,667
Natural Gas 0.00 140,000 60,000 200,000
Zero Carbon 733,333 96,000 0.00 829,333
Total 1,000,000 400,000 600,000
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Near Optimal Network
Source Region 1 Region 2 Region 3 Total
Coal 0.00 0.00 540,000 540,000
Oil 266,667 164,000 0.00 430,667
Natural Gas 0.00 140,000 60,000 200,000
Zero Carbon 733,333 96,000 0.00 829,333
Total 1,000,000 400,000 600,000
2%
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Conclusions and Future Work
The P-graph model is equivalent to alternative techniques (e.g.
CEPA and linear programming)
P-graph provides n-best solutions
Alternative structures may be important in real life decision-making
Future work will look into:
integration of multiple quality indicators
evaluation of robustness of alternative solutions
extensions to other problem variants
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References
Friedler, F., Tarjan, K., Huang, Y. W., and Fan, L. T.,1992a, Graph-theoretic approach to process
synthesis: axioms and theorems. Chemical Engineering Science, 47: 1973-1988.
Friedler, F., Tarjan, K., Huang, Y. W. and Fan, L. T., 1992b, Combinatorial algorithms for process
synthesis. Computers & Chemical Engineering, 16: 313 320.
Friedler, F., Tarjan, K., Huang, Y. W., & Fan, L. T., 1993, Graph-theoretic approach to process
synthesis: polynomial algorithm for maximal structure generation. Computers & Chemical
Engineering, 1993; 17: 929-942.
Foo, D. C., Tan, R. R., and Ng, D. K. ,2008, Carbon and footprint-constrained energy planning using
cascade analysis technique. Energy, 33(10), 1480-1488.Foo, D. C., Tan, R. R. (2015). A review on process integration techniques for carbon emissions and
environmental footprint problems. Process Safety and Environmental Protection.
Lam, H. L., 2013, Extended P-graph applications in supply chain and process network synthesis.
Current Opinion in Chemical Engineering, 2(4), 475-486.
Tan, R. R., and Foo, D. C., 2007, Pinch analysis approach to carbon-constrained energy sector
planning. Energy, 32(8), 1422-1429.
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Thanks for your attention
Comments and questions are welcome
Or contact me via e-mail:
Kathleen B. Aviso
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