jurong island , eco-industrial park and the internet of things · jurong island , eco-industrial...
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Markus Kraft
Jurong Island , eco-industrial park and the
internet of things
Markus Kraft
04/Nov/2014
Markus Kraft
Co-Authors
• Cambridge:
Dr Sebastian MOSBACH, Dr Jethro AKROYD, Dr Pooya AZADI,
Dr Amit BHAVE
• NTU:
Dr Cathrine KASTNER, Prof Raymond LAU, (Brandon) Yung Sin YONG,
Jiming PANG, Wee Minn TAN, (Edmund) Sin Yong CHONG, PHANG Zhi
Hua, Maruf Bin AZIZ, Listiantono NUGROHO, Gabriel ALEXANDER,
TRAN Viet Phuong (Shen), M Ghanesh KUMAR
• NUS
Prof Iftekhar KARIMI
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Projected 2020 Business-As-Usual
Greenhouse Gas Emissions
Projecting from 2005, business-as-usual (BAU) emissions are expected to reach 77.2 million tonnes (MT) in 2020.
77.2 (60.3) MTrefers to total greenhouse gas emissions. Greenhouse gases other than CO2 have been converted into CO2 equivalent.
Source: http://app.nccs.gov.sg
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Singapore and UK are committed to tackling climate change challenges
Climate change – a global challenge
Sustainable Development Blueprint
targets 35% improvement from 2005
levels by 2030
Legally binding target for 80% reduction
from 1990 levels by 2050
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PowergenerationSwitch fuel mix from oil to natural gas LNG terminalEncourage more solar test-bedding and research
WaterIncinerates sludge rather than landfill disposals Reduce plastics incineration
HouseholdsTighten Minimum Energy Performance Standards (MEPS) for household air conditioners and refrigerators (2013)Extend MEPS to lightning and other Apppliences (2014)
Measures to Reduce Emissions
Source: http://app.nccs.gov.sg
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Buildings Green Mark Certification for new and retro-fitted buildings.Audit cooling systemsSubmit energy building data
Transport70-30% split between public and privatetransportCarbon Emission based Vehicle (CEV) scheme
IndustryExtend GREET schemeEnergy efficiency financing pilot schemesCo-generation plants in the energy intensive sector
Measures to Reduce Emissions
Source: http://app.nccs.gov.sg
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• Carbon emission in Singapore
• The Cambridge CREATE programme CARES and C4T
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Part 1: Key concepts
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4 interdisciplinary research programmes (IRPs)
• Carbon capture and utilisationIRP1
MUSCAT
• Multi-scale electrochemical engineeringIRP2
EMSET
• Optimising industrial parks and plantsIRP3
CAPRICORN
• Integrated chemicals and electrical systems operation IRP4
ICESO
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• Carbon emission in Singapore
• The Cambridge CREATE programme CARES and C4T
• Jurong island and the concept of eco-industral parks
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Part 1: Key concepts
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Sources :
Singapore Land Authority (sla.gov.sg)
Onemap.sg
Integrated Land Information Services (INLIS)
Building & Construction Authority (bca.gov.sg)
Jurong Island – (Eco)
Industrial Park
EIPs
http://en.wikipedia.org/wiki/Jurong_Island#mediaviewer/File:Jurong_Industrial_Estate_and_Jurong_Island,_panorama,_Nov_06.jpg
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Benefits of Eco Industrial Parks
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Industrial point of view (POV):• Economy of scale• Incentives• Availability of good and
services to be exchanged
Public POV:• Economic development• Industrial growth• Availability of good and
services
Disadvantages of Eco Industrial Parks
Industrial POV:• Economy of scale not
always applicable
Public POV:• Concentration of industries
may cause environmental damage and heath and safety risks
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Establishing an IP
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• Wealth of literature on topic• Economic impact• Environmental impact• Land prices
• Case studies• Example – Yemen Arab Republic (World
Bank 1984)• 80% over budget (after scale back)• inadequate rents• high administrative costs• unable to obtain suitable personnel• Several large enterprises (instead of
many small-mid size as desired)• in the end:
‘liquidate part or whole of the Sanaa estate’
General consensus: No easy way to successfully establish an IP or EIP
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Kalundborg Industrial Park
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Most cited/studied IP
22 member exchanges9 materials14 water6 energy
Most discussed IP
Gradually grew from 1960s, unplanned
First QUANTATITAVE study. Jacobsen, 2006 †
† N. B. Jacobsen. Industrial symbiosis in Kalundborg , Denmark. Journal of IndustrialEcology, 10(1-2):239–255, 2006.
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Current Eco-Industrial Park (EIP)
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† D. Sakr, L. Baas, S. El-Haggar, and D. Huisingh. Critical success and limiting factors for eco-industrial parks: global trends and Egyptian context. Journal of Cleaner Production, 19(11):1158–1169, July 2011.
North America 60 (17 operational) †
Asia 60 (predominately China) †
Europe 26 †
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Metrics
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‡ J. Dewulf and H. Van Langenhove. Integrating industrial ecology principles into a set of environmental sustainability indicators for technology assessment. Resources, Conservation and Recycling, 43(4):419–432, Mar. 2005.† R. Singh, H. Murty, S. Gupta and A. Dikshit. An overview of sustainability assessment methodologies. Ecological Indicators, (15):281-299, 2012.
Economic †• GNP• Growth• Cooperation
Environmental‡• Renewability of resources• Toxicity of emission• Process efficiency
Social †• Health care• Housing• Unemployment
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• Carbon emission in Singapore
• The Cambridge CREATE programme CARES and C4T
• Jurong island and the concept of eco-industral parks
• The hierachy of models – top down
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Part 1: Key concepts
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Park level models Plant level model
From C&I http://www.cieng.com/a-11-156-Industries-Refining.aspx
Water networkElectrical gridWaste treatmentTransportation
Water usagePower usageMaterial input/outputEmissions
Process level model
Heat requirementsMaterial inflowEfficiency
Hydro Cracking
Piping and instrumentationMechanical equipmentValves, flow directionsInput/output control
P&ID level model
Part level model
Equipment sensorsInflowOutflowPower consumption
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• Carbon emission in Singapore
• The Cambridge CREATE programme CARES and C4T
• Jurong island and the concept of eco-industral parks
• The hierachy of models – top down
• Example – Jurong Island simplistic plant level model
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Part 1: Key concepts
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Sources :
Singapore Land Authority (sla.gov.sg)
Onemap.sg
Integrated Land Information Services (INLIS)
Building & Construction Authority (bca.gov.sg)
SRC
Linde Gas
ExxonMobil Refinery
ExxonMobil Chemicals
Carotino
Akzo-Nobel
YTL
Ellba Eastern
Croda
Chevron Oronite
Tate & Lyle
Oil Tanking Odfjell
Denka
Eastman Chemicals
Far East
Oasis @ Sakra
Etc.
Zoning based on land lot information (Onemap)
e.g MK34-00854A
Locate companies through addresses (INLIS) and
building projects (bca.gov.sg) corresponding to lot
numberse.g Sembcorp Gas @ 80 Sakra Road
Jurong Island Map
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Economic Input-Output Life Cycle Assessment (EIO-LCA)
method estimates the materials and energy resources
And the environmental emissions
based on industrial sector and company revenue
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US 2002 Benchmark
Contributed by: Green Design Institute Last update: 4 July 2010
Input:
• Revenue - Monetary unit: 2002 US dollars (US$)
• Industrial sectors (1 out of 428)
Output:
• greenhouse gases• energy• toxic releases• hazardous waste
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Petroleum & Basic Chemicals Sector
Total Revenue (2002): US$ 12.9 B
Total CO2 Emission: 7.71 Mt/yr
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Petroleum & Basic Chemicals Sector
DENKA
Products: Acetylene Black
Capacity: 12 kt/yr
Revenue (2002): US$9.7 M
CO2 Emission: 24.5 kt/yr
The Polyolefin Company
Products: COSMOPLENE
Capacity: 245 kt/yr
Revenue (2002): US$397 M
CO2 Emission: 229 kt/yr
Lucite International
Products: MMA
Capacity: 120 kt/yr
Revenue (2002): US$186 M
CO2 Emission: 107 kt/yr
Total Revenue (2002): US$ 12.9 B
Total CO2 Emission: 7.71 Mt/yr
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Resin, Rubber, Artifical Fibers, Agri Chem & Pharm Sector
Asahi Kasei Plastics Singapore
Products: Polyphenylene Ether | Modified Polyphenylene Ether
Capacity: 39 kt/yr | 48 kt/yr
Revenue (2002): US$ 143 M | US$ 203 M
CO2 Emission: 5.7 kt/yr | 8.0 kt/yr
DENKA
Products: HIPS
Capacity: 65 kt/yr
Revenue (2002): US$126 M
CO2 Emission: 67 kt/yr
Total Revenue (2002): US$ 472 M
Total CO2 Emission: 80.7 kt/yr
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Storage Sector
Universal Terminal
Capacity: 2 360 000 cbm
Products: Black and white petroleum products and base oil products
Oiltanking
Capacity: 1 642 000 cbm
Products: Gasoline, gasoil, heavy fuel oil, naphtha, feed stocks, jet fuel and petrochemicals
Singapore LNG Terminal
Capacity: 540 000 cbm
Products: LNG and LPG
Stolthaven
Capacity: 71 000 cbm
Products: Low pressure gases, chemicals and petroleum products
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• Carbon emission in Singapore
• The Cambridge CREATE programme CARES and C4T
• Jurong island and the concept of eco-industral parks
• The hierarchy of models – top down
• Example - Jurong Island: simplistic plant level model
• Example - Jurong Island: Process level model
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Part 1: Key concepts
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Chevron’s HDPE plant on Jurong Island, Singapore
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Merger between Chevron Phillips Chemical (50%), EDB Investment (30%), Sumitomo Chemicals (20%)
Product- High Density Polyethylene (HDPE)
Brand- Marlex
Capacity- 210,000 MTA
Process- Single Loop-Slurry
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Single Loop-Slurry Process
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Inlet: Ethylene Co-monomer-Hexene-1 Diluent- Isobutane Catalyst- typically chromium
trioxide on silica support)
Possible CO2 eqv. emission source:1. Vent from ethylene recovery2. Condenser (refrigerants)3. On site utilities (boilers, furnace)
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• How to represent data?
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Part 2: Internet of Things , Industry 4.0,…. Concepts
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Example: Engine
Series of observations at different process conditions:
Process conditions: Responses:Experiment
Examples: Engine
speed, load, inlet
temperature, etc.
Examples: Peak in-
cylinder pressure,
BMEP, emissions, etc.
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Example: Jurong Island
• Expect data from:
• Industries
• Government
• Models have variable input/output requirements
• All of these sources have different units, descriptors,
requirements and attributes and are obtained at different levels.
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Fundamental issue: How shall we represent and store our data?
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Example: EngineML -Internal Combustion Engine Data formattest
dataconsistent format
point data (e.g. rpm, CO, ul) time resolved data (p-CA) Apparatus (production engine, research engine) errors data type (consistent units) raw or processed
xml code selected
machine and human readable tree structure validated against schema
easily accessible database
read by model code data stored consistently old data never forgotten
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Comments:
• Notable names associated with Semantic Descriptors:
• BASF, Bayer Technology, Evonik, Fluor, Siemens, Bentley
Systems, Fiatech, Dow, Exxonmobile, W3C, to mention a few.
• Goals of development in line with our purposes
• Norwegian Oil Industry Association using semantic descriptors
• Some tools already or being developed in research and industry
• ISO 15926 - Fiatech and Dexpi, OntoCAPE
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Part 2: Internet of Things , Industry 4.0,…. Concepts
• How to represent data?
• What is a model?
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Example: Engine
Series of observations at different process conditions:
Process conditions: Responses:Experiment
Examples: Engine
speed, load, inlet
temperature, etc.
Examples: Peak in-
cylinder pressure,
BMEP, emissions, etc.
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Engine avatar – model in the cloud
Model parameters:
Examples: Heat transfer coefficients, combustion parameters, etc.
Series of model evaluations at different process conditions:
Process conditions: Responses:
Examples: Engine
speed, load, inlet
temperature, etc.
Examples: Peak in-
cylinder pressure,
BMEP, emissions, etc.
Model
Engine - Avatar
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Model
Functional relationship
Data
Description of how toestimate parameters
What is a model?
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Type (executable, etc.)
Input
variables
Output variables
Example: ModelML
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MODS is a unique software tool which can be “wrapped around” any process, system or software, enabling:(a) Data-driven modelling
(b) Rapid multi-objective optimisation of processes, systems, technologies
(c) The generation of surrogates (fast response) models derived from more complex systems/processes. e.g. Polynomial fits, High dimensional model representation (HDMR)
(d) Data standardisation and visualisation
(e) Global parameter estimation for any model
(f) Uncertainty propagation throughout systems
(g) Global and local sensitivity analysis
(h) Intelligent design of experiments
Model development Suite (MoDS)
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Example: Bayesian parameter estimation - MoDS
• GSK Jet milling application – Particle size distribution
• Generation of a “cheap” surrogate model
• Bayesian inference on unknown parameters based on measurements
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Part 2: Internet of Things , Industry 4.0,…. Concepts
• Internet of Things , Industry 4.0,…. Concepts
• How to represent data?
• What is a model?
• “Lego” model or model networks
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Example: A material network model
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[18] C. Gu, S. Leveneur, L. Estel, and A. Yassine. Modeling and optimization of material/energy flow exchanges in an eco-industrial park. Energy Procedia, 36:243–252,2013
Tensor matrix S where each elementsij
k = 1 if an exchange possibility exists of material k from industry I to industry j; 0 otherwise
Define, production and internal and external prices for material k
Model calculates costs (including delivery) to establish if an interchange is economically profitable.
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Concatenation of models is complex in general:
Model 1
Model 2
Concatenated model
Inputs Outputs
NB This covers:
Instrumental models, pre- and post-processors
Cartesian products of models
Arbitrary concatenations of (networks of) any number of models
Hierarchies of models/networks
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Model Hierachy
Industrial Park
Chemical Plant / Refinery / Power station
Process flow sheet model
Pipe and Instrumentation Diagramme
Joint Modeldescription (Semantic
represenation)
Draws from DEXPI
COMOS
MoDS
Data
Available at each
level
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Summary
• Part 1
• CO2 emission on Jurong Island using plant scale and process
scale models
• Part 2
• Elements of a cyber infrastructure to describe , analyse, and
optimise the activities in a complex network of models
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