COLLABORATIVE RESEARCH ON
SUSTAINABLE SYSTEMS PLANNING, DESIGN
AND OPERATIONS
RESEARCH AT UTM-PROCESS SYSTEMS ENGINEERING CENTRE (PROSPECT)
by Prof Zainuddin Abdul Manan
PhD, CEng, FIChemE
www.fkkksa.utm.my/prospect
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Content
R & D Challenges
Innovation – Closed vs Open
About PROSPECT
Case Study: P2C Partnership
Collaborative Projects
Challenges & Motivation for
Collaborative Research
Tough
financial
climate
Collaborative
R&D
Supercompetitive
world (e.g.
Source: David Brown,
IChemE President
More difficult to
publish)
Pressure
for open
innovation
*Open Innovation: Open
Innovation: Researching
a New Paradigm (Oxford,
2006) by Henry
Chesbrough,
professor and executive
director at the Center for
Open Innovation at UC
Berkeley
Closed innovation
Candidate
projects
Screen
Screen
Inside the
company
Development
projects
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‘Products’
Source: David Brown,
IChemE President
Open Innovation
Outside the company
Diverse
exploitation
routes
Inside the
company
Candidate projects
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Development projects
‘Products’
Source: David Brown,
IChemE President
Closed Innovation – current
(traditional view)
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Innovation comes from within, selfreflective process
Knowledge is a monopoly of an organisation
Promote elite university education
Hire bright people (Abrahamovich vs Wenger)
Put them in special conditions
Free from market pressures
Pipeline of ideas to products
Delivered to passive waiting consumers
Open Innovation –Now and the
future

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Authorship joint, complex and evolutionary
Knowledge created through interactions
Innovation as a mass activity
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Increase diversity of parallel experiments: faster learning
Public platforms, shared development, lower cost
Consumers are innovators
Networked companies/platform innovators
Clusters and networks in regions
Cities and countries as open innovation
systems
Innovation essential social and dynamic
Case Study: PROSPECT-2-Company
collaboration
Process Systems Engineering Centre (PROSPECT)
Universiti Teknologi Malaysia
Profile, Vision & Mission
• Process Systems Engineering Centre (PROSPECT) is a centre of excellence
within the Faculty of Chemical Engineering, UTM. PROSPECT specialises in
aspects of planning, design and creation of sustainable and innovative process
and product supply chain as well as optimal and efficient operation of process
systems with emphasis on conservation of natural resources; in particular,
materials, energy and water. More than 15 years experience in Process
Systems Engineering (PSE) R & D, software product development, consultancy
services and training has positioned PROSPECT as one of the leading PSE
centres of excellence in the region
• Vision – To be recognized as a world class centre of excellence in
technology and continuing education in
Engineering through innovation and creativity
Process
Systems
• Mission - To become a world class Process Systems Engineering
centre for the development of human capital and innovative
technologies to contribute towards wealth creation for the nation
and mankind with emphasis on sustainable development through
conservation of natural resources
• Tagline – Engineering Sustainability
www.fkkksa.utm.my/prospect
Students’ Industrial Training
Current Practice and Challenges:
• Companies typically accept students to do
practical training in-house.
Usually, the
students expect companies to assign them
tasks and provide them with learning
experience.
• This approach can be rather one-sided and
not something companies look forward to
except for to fulfill its social responsibility.
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Students’ Industrial Training
PROSPECT’s Approach:
• Assign students with specific industrial
projects (after discussion with company)
that he/she will conduct not only during
the practical training period, but also
before and after the training aimed
towards
benefiting
the
company,
especially financially.
• In UTM chemical eng department, each
student is required to take two semesters
of research projects, with practical
training sandwiched between the two
semesters.
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Project Milestone
S1
Plan/Month
D
J
IT
F M A M
Technology/Process
Review & Screening,
S2
J
J
O
S
O
D
N
1
Industrial Attachment
(on-site) – Data
Collection
2
Data Analysis,
Optimisation and
Economic studies
3
Task duration
1 Project milestone 1: Project proposal presentation
2 Project milestone 2: Project progress presentation
3 Project milestone 3: Project results presentation
S1=semester 1
S2=semester 2
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IT= Industrial training
PROSPECT’s Approach:
• Students are attached in company to do detailed studies
for their project using this mechanism (our approach).
• Under this mechanism, the student will undertake to do
detailed study on one of the listed projects (see
examples). In the 1st semester they can start doing the
technology screening and literature survey on the
project, and present their proposal to plant before the
start of their practical training in April.
• Then, they can start to collect operation data in plant
between April and June (during on-site practical
training). They will present another progress report to
plant in June at the end of the on-site attachment
period.
• Once they finish data collection, they will do technical &
feasibility analysis and improvement as well economic
analysis in the second semester of their project and
present the final results to the plant in the second
semester (July till November).
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PROSPECT’s Approach:
• Master and PhD students (can start
anytime, but min. cost is the
scholarship/allowance for student)
– Duration for MEng is 2 years, PhD is 3 to
4 years
• Masters and PhD students will typically
work on much larger scale and more
innovative projects.
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List of PROSPECT P2C Projects
Company
BERNAS
R&D Projects
Towards a Resource-Efficient, Integrated Rice Mill Complex –
Optimisation of Rice Supply Chain
- Optimisation Rice-Husk Based CHP System
CCM
Development Of Math Models for Retrofit based on Minimum
Water Network Technique and considering multiple
contaminants
Combined Mass and Heat Exchange Networks
TITAN Petchem
Computational Fluid Dynamics Modeling of Ethylene Cracker
Furnace
Development of Soft Sensor for Ethylene Cracker
Steam Trap Optimisation
Mechmar Boiler
Techno-Economic Feasibility of CDM Project from Palm Oil
Waste
Malaysian Energy Centre & Optimal-Audit, Optimal-Heat, Optimal-Water Software
Malaysian Venture Capital Development
Pan Century Oleo Chemical Maximum heat recovery network and hydraulic system
(PCOC)
analysis
Maximum Heat Recovery System (Pinch Analysis)
FELDA Oil Products
Heat recovery network retrofit
MIMOS Semiconductor Cost Effective Minimum Water Network using graphical
(MySEM)
approach
Malaysian Newsprint
Maximum water recovery with regeneration targeting using
Industry (MNI)
numerical method
Optimisation of CHP system
Polycore
Electrical Energy Management
Infineon
Overall Plant Utility Optimisation
Ethylene Malaysia
Power recovery network
Year Started
2009
Student Involvement
1 PhD student
2008
2009
1 undergrad student
1 undergrad student
1 MSc student
2009
2008
1 MSc student
2 undergrad students
2009
2008
2008
1 PhD student
1 MSc Student
1 MSc student (part time)
2006
5 undergrad students, 2 MSc
students, 2 programmers
1 undergrad student
2007
2007
2008
2006
2006
1 undergrad student
1 undergrad student
1 PhD student
1 undergrad student
1 MSc student
2008
2008
2008
2006
1 MSc student
2 undergrad students
1 MSc Student (part time)
1 MSc student
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List of PROSPECT P2C Projects
Company
Projects Title
Year
Students Involved
TITAN Polymer • Modelling The Product Quality and Production Rate of
(M) Sdn Bhd
Propylene Polymerization in Industry Reactors
• Formulation of Modelling and Simulation Algorithm for
Propylene Homopolymerization Loop Reactor
• Artificial Neural Network Modelling of
Propylene Polymerization in Industrial Loop Reactors
• Development and Simulation of Hybrid Model for
Propylene Polymerization in Industrial Reactors
•
Kempas Edible Oil • Develop a prediction model for : Phosphoric acid and
Sdn Bhd
bleaching earth dosage for degumming and bleaching
process, respectively, in palm oil refinery.
• Product quality of the refined oil from degumming and
bleaching process.
2008
3 MSc student
4 undergrad students
2009
2 undergrad students
Mensilin Holdings Optimisation of decentralized electricity generation from
Sdn Bhd
biogas and biomass.
2010
1 PhD student
Kerry Ingredients Modelling and optimization of Industrial Spray Dryer
2010
1 undergrad student
Kerteh Petronas Modelling of Benfield CO2 removal system
Gas Bhd
Integrated reformer Methanol with natural gas plant
Life cycle analysis (LCA)
2010
3 undergrad students
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Overall Theme:
Sustainable Systems Planning,
Design and Operations
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4 Key Focuses at PROSPECT
Process Design
& Improvement
Product
Design
Resource
Planning
Plant
Optimization
HOLISTIC RESOURCE
CONSERVATION NETWORK
Some End Users of the Resource
Conservation Projects
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Holistic Resource Conservation Network
Looking at the bigger picture!
Composting
toilet
Dual flush
toilet
Chem. Eng. Mag.
(CEM), Dec 2006
Increase
priority
Vacuum
toilet
Aerated Flow
Tap
RW
Harvesting
Normal
electrical fan
Source
Elimination
Source
Reduction
Reuse/Outsourcing
Regeneration Reuse
Fresh Resouce
Sand filter with
activated carbon
Microfiltration
The Resource Management Hierarchy
Theme
“Systems Design for Resource Sustainability”
Efficiency
Engineering
Sustainability
Environment
Systems Design
Security
Target
(T)
Design
(D)
T&D
Holistic
Retrofit
Batch
Math
Model
Software
Heat
Water
Power
Gas
Mass/
Materials
Multiple
Resources
* More than 50 related and published journal papers by PROSPECT in this area.
Latest Work: A Holistic Approach for Design of Minimum Water Networks Using Mixed Integer Linear
Programming (MILP) Technique; Manuscript ID: ie-2010-000357.R1 paper accepted in Industrial &
Engineering Chemistry Research, 2010. Available online, May2010.
Optimal Water Software
Software features:
- Can TARGET and DESIGN the most
COST EFFECTIVE MINIMUM WATER
& WASTEWATER network
- Consider multiple contaminants
- Consider all resource management
hierarchy which include elimination,
reduction, reuse, outsourcing and
regeneration
- User can define payback period
desired
*Work is underway to extend the
method for energy and other
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resources
EM Successful Case Studies
>30% diesel savings (USD 275,000/yr)
20% saving on electricity (USD 16,000/yr)
Payback period < 2 months
Using available limited rice husk:
- Generate 0.6 MW power
- Satisfy the total drying heat
- Total annual power saving >1Mill USD/yr
- Payback period of 3.34 years.
Reduces cooling water to 2 from 3
Annual savings = USD 187,000 /yr
Payback period = 1 year
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Other available software
The
compo
site
curves
Software Features:
Software Features:
for
- MaximumResults
energy
recovery
maximum
energy
targeting and
design
recovery
- Heat exchanger network
design
- Area calculations
- Multiple utilities selections
- Cost calculations
- Energy auditing for various
equipments e.g. boilers,
chillers, pump, motors, steam
systems etc
- Suggesting energy
improvement measures e.g.
fuel switching, optimisation etc
WM Successful Case Studies
FW reduction: 85.1%
WW reduction: 97.7%
Net annual savings = RM 190, 000 /year
Payback period = 4 months
FW reduction: 35.8%
WW reduction: 100%
Net annual savings = USD 105, 000 /year
Payback period = 1.87 years
FW reduction: 95.3 %
WW reduction: 64.7 %
Net annual savings = USD 5, 400 /year
Payback period = 5 years
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PALM OIL REFINERY INTENSIFICATION
SFE-based Crude Palm Oil Refining
It is envisioned that the use of supercritical
extraction technology can lead to an
intensified process that:
• Can avoid the complex processing steps of
separating unwanted materials
• Integration of CPO refining and extraction
of valuable components (Vitamin E,
tocopherols, tocotrienols, etc) in a few steps
can avoid destruction of the components
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Palm Oil Refinery Intensification
Selected/Related PROSPECT’s publications
Simulation Modeling of the Phase Behavior of Palm Oil –
Supercritical Carbon Dioxide, JAOCS, Vol. 80, no. 11 (2003)
Development of a New Process for Palm Oil Refining Based
on Supercritical Fluid Extraction Technology, Ind. & Eng.
Chem. Res., 2009, 48, 5420-5426:
Effects of Parameters on Yield for Sub-Critical R134a
Extraction of Palm Oil, J. Food Eng., 95 (2009) 606–616
Mathematical modeling and genetic algorithm optimization
of clove oil extraction with supercritical carbon dioxide, The
Journal of Supercritical Fluids, 51, 331–338, 2010.
Genetic algorithm optimization of supercritical fluid
extraction of nimbin from neem seeds, J. Food Eng., 97,
127–134, 2010.
Integrate CPO refining
& extraction of valuable
comp in a few steps &
avoid destruction of the
comps
Experimental design, modeling,
optimization of sub and supercritical
phenomena
• new process designs for palm
oil (and other veg oil) refining
based on SFE
• modeling and optimisation of
the properties & processes
using computer-aided tools
(GA, ANN, math model, ASPEN
simulator)
• experimental testing on the
processes
Intensified and Optimised
Palm Oil SFE Processes
Objective:
Cheaper, Cleaner,
& Safer Proceses
Process
modeling
& Dev
Process Experimental
Optimisation
Testing
4 Key Focuses at PROSPECT
Process Design
& Improvement
Product
Design
Resource
Planning
Plant
Optimization
INTEGRATED, RESOURCE-EFFICIENT
RICE (IRE) MILL COMPLEX
Malaysia Rice
Board
Latest work (in review): “Optimal Design Of A Rice Mill Utility System With Rice Husk
Logistic Network”, Biomass & Bio-energy, in review, 2010.
Current Scenario of rice industry
optimal rice mill utility
system with RE-mix
heating and electricity
requirements during
peak and off-peak
seasons
capital cost for
various sizes of cogen
system
Economic parameter
of each product
cogen operating
conditions
Parameters
Economic
parameter of each
technology
an optimal integrated
network of rice mill &
downstream processes
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an optimum
logistic
network for
RE supply to
the rice mills
distance between rice
husk supply locations
and facility
transportation cost
A systematic framework is required
to optimise these parameters to
achieve optimal profit
Superstructure:
Optimal Design of A Rice Mill Utility System
with Rice Husk Logistic Network
Electricity
grid
Rice husk from
own rice mill
i
Electricity
demand
Cooling tower
Boiler with
different capacity
b
turbine
b
LP
IBD
i
MP
Rice husk from
private rice mill
j
logistic
Using available limited rice husk:
- Generate 0.6 MW power
CHF
FBD
Satisfy
the
total
drying
heat
c
i
- Total annual power saving >1Mill USD/yr
- Payback period of 3.34 years.
facilities
Utility network
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Superstructure:
Optimal resource
allocation for IRE rice
mills complex
RH from out
source
Wet paddy
a
n =7
Rice husk ash
g
Dried paddy
b
k =1
Head rice
k=2
Broken rice
k=3
Rice bran
k=4
Rice husk
f
c
n=1
Graded rice
5%
d
n=2
Graded rice
10%
e
n=6
furfural
n=3
Rice noodle
n=4
Defatted rice
bran
n=5
Rice bran oil
market
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INTEGRATED ENERGY & EMISSIONS
PLANNING
Ministry of Energy,
Green Tech and Water
J. Renew. Energy, Available online May 2010
Resource Planning for Green and
Secure Energy Supply – National
Policy formulation
Math models for
• Optimal grid electricity
generation mix & optimal
location, types and
economic scale of RE plant
to put on stream to satisfy
the demand as well as to
meet government policy
target
• The‘best’ feed in tariff to
make RE grid connected is
economically attractive
An Optimal RE-Integrated
Power Generation Planning
Objective:
Min cost of
electricity generation
RE
target
Demand
satisfaction
*Biomass, solar, wind, hydro, etc
Emission
Target
4 Key Focuses at PROSPECT
Process Design
& Improvement
Product
Design
Resource
Planning
Plant
Optimization
Tailor-Made Green Diesel
and Gasoline
Tailor-Made Green Diesel
and Gasoline (D&G)
• Aim– A GREENER biodiesel or bio-gasoline
mix
• Among the options:
– Butanol
– Ethanol
– BL
– Etc
• Which fuel and how
much should we mix to
get the GREENEST but
AFFORDABLE biofuel?
Tailor-Made, Sustainable
Green D & G
Objective:
Green Diesel and Gasoline
Meeting Target
Properties
Target
properties
CAD
Experimental
Optimal
validation
formulation
A Cleaner, Cost-effective Solvent
Alternative for Carotenoid and
Vitamin E Extraction from Palm Oil
Palm Oil Fine
Chemical Solvent
Design
• Aim– An alternative
ECONOMICAL, SAFE and
CLEANER solvent for
Palm Oil Fine Chemical
extraction
• Typical solvent used is
hexane. But hexane is
hazardous
• What possible
alternative solvent can
be used for valuable
minor component
extraction?
Sustainable
Green Solvent
Objective:
Solvent
Meeting Target
Properties
Target
properties
Solvent
screening
(ICAS)
Experimental
validation
4 Key Focuses at PROSPECT
Process Design
& Improvement
Product
Design
Resource
Planning
Plant
Optimisation
Overall Refinery & Petrochemical Process
Improvement using AI Techniques
* 15 related published journal papers by
PROSPECT in this area.
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Types of Models
Type
Description
Application
1st Principle
Mechanistic models are usually
built from physical laws,
conservation relations, and
established physical and
chemical relations
For chemical/biochemical
processes, 1st principle
yields mass and energy
balances that are often
used as a general dynamic
model structure
Black Boxviewed as models with a highly
Empirical Model, parameterized structure such
Neural Network that in principle any input–
output map can be realized
For example: systems
nonlinearity and the
uncertainty in the reaction
kinetics and/or the
thermodynamics that are in
general static functions of
state variables.
Gray Box
(Hybrid)
Modeling a polymer reactor
kinetics (black) and
balances (white)
Available knowledge of process
phenomena is used to form a
white-box part, while missing
information is approximated by
black-boxes fitted on process
data
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ANN, 1st Principle, Hybrid Models
1.
2.
3.
4.
5.
6.
Fast response
Accuracy
Noise tolerance
Forecasting
Overall process understanding
Knowledge of highly
nonlinearity system
Outputs & Benefits
• A dynamic simulator
• Defining process most sensitive parameters
• Improved productivity ; e.g. for a refinery,
gasoline
light naphta
heavy naphta
gas oil and other products yield
• Other benefits
– Pollution and waste minimization
– Energy savings
– Forecasting market demand for future planning
Completed works
No.
Case study
Plant
Objective
Benefits
1
Tabriz refinery/
Iran
Plat former unit
Maximizing
gasoline
production
2
Typical
Hydrotreater plant
Plant simulator
3
Typical
Plant simulator
4
Tabriz refinery/
Iran
5
Domestic
6
Kuwait refinery
Delayed coking
unit
Hydrocracker
unit
Oil- asphaltene
precipitation
Desalting unit
4.48 % increase
in gasoline
production
99.9999%
Accuracy
99.9999%
Accuracy
7
Kuwait refinery
Refinery
Maximizing
light naphtha
Precipitate
amount
Maximizing
desalting and
dehydration
efficiency
Estimating
Ozone
concentration
99.6 % accuracy
99.00% accuracy
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Polypropylene Dynamic
Modeling & Optimisation
Dynamic modelling and simulation of PP
homopolymer production in loop
reactors & copolymer in FBR
Product quality (MFI
and XS)
-Measured experimentally -Substantial time delays
between sampling &
analysis
-Tackling input variables
/industrial data (F,T,P etc)
Production rate
-Measured at the end of
process instead of just
after the reactors
Thank You
for inquiries: http://www.fkkksa.utm.my/prospect
mfaiz@fkkksa.utm.my zain@fkkksa.utm.my
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