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COLLABORATIVE RESEARCH ON COLLABORATIVE RESEARCH ON SUSTAINABLE SYSTEMS PLANNING, SUSTAINABLE SYSTEMS PLANNING, DESIGN AND OPERATIONS DESIGN AND OPERATIONS RESEARCH AT UTM-PROCESS SYSTEMS ENGINEERING CENTRE RESEARCH AT UTM-PROCESS SYSTEMS ENGINEERING CENTRE (PROSPECT)(PROSPECT)by Prof Zainuddin Abdul MananPhD, CEng, FIChemE
www.fkkksa.utm.my/prospect
R & D ChallengesR & D Challenges
ContentContent
Innovation – Closed vs OpenInnovation – Closed vs Open
About PROSPECTAbout PROSPECT
Case Study: P2C PartnershipCase Study: P2C Partnership
Collaborative ProjectsCollaborative Projects
Challenges & Motivation Challenges & Motivation for Collaborative for Collaborative ResearchResearch
Collaborative R & D
Tough financial climate
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
Source: David Brown, IChemE President
Super-competitive world (e.g.
More difficult to publish)
Closed innovationClosed innovation
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Inside the company
Candidate projects
Development projects
‘Products’
Screen Screen
Source: David Brown, IChemE President
Open InnovationOpen Innovation
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Outside the company
Inside the company
Candidate projects Development projects ‘Products’
Diverse exploitation routes
Source: David Brown, IChemE President
Closed Innovation – Closed Innovation – current (traditional view)current (traditional view) Innovation comes from within, self-
reflective 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 Open Innovation –Now and the futurethe future Authorship joint, complex and
evolutionary Knowledge created through interactions Innovation as a mass activity
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-Case Study: PROSPECT-2-Company collaborationCompany collaboration
Process Systems Engineering Centre (PROSPECT)Universiti Teknologi Malaysia
Profile, Vision & MissionProfile, 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 Process Systems Engineering through innovation and creativity
• 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 Sustainabilitywww.fkkksa.utm.my/prospect
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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 TrainingPROSPECT’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
S1S1 ITIT S2S2
Plan/MonthPlan/Month DD JJ FF MM AA MM JJ JJ OO SS OO NN DD
Technology/Process Technology/Process ReviewReview & & Screening,Screening,
Industrial Industrial Attachment (on-site) Attachment (on-site) – Data Collection– Data Collection
Data Analysis, Data Analysis, Optimisation and Optimisation and Economic studiesEconomic studies
Project milestone 1: Project proposal presentation Task duration 1
S1=semester 1S2=semester 2 IT= Industrial training
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2
3
2Project milestone 2: Project progress presentation 3Project milestone 3: Project results presentation
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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.
List of PROSPECT P2C List of PROSPECT P2C ProjectsProjects
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Company R&D Projects Year Started Student Involvement
BERNAS Towards a Resource-Efficient, Integrated Rice Mill Complex –Optimisation of Rice Supply Chain
2009 1 PhD student
- Optimisation Rice-Husk Based CHP System 2008 1 undergrad studentCCM Development Of Math Models for Retrofit based on
Minimum Water Network Technique and considering multiple contaminants
2009 1 undergrad student1 MSc student
Combined Mass and Heat Exchange Networks 2009 1 MSc student
TITAN Petchem Computational Fluid Dynamics Modeling of Ethylene Cracker Furnace
2008 2 undergrad students
Development of Soft Sensor for Ethylene Cracker 2009 1 PhD studentSteam Trap Optimisation 2008 1 MSc Student
Mechmar Boiler Techno-Economic Feasibility of CDM Project from Palm Oil Waste
2008 1 MSc student (part time)
Malaysian Energy Centre & Malaysian Venture Capital
Optimal-Audit, Optimal-Heat, Optimal-Water Software Development
2006 5 undergrad students, 2 MSc students, 2 programmers
Pan Century Oleo Chemical (PCOC)
Maximum heat recovery network and hydraulic system analysis
2007 1 undergrad student
Maximum Heat Recovery System (Pinch Analysis) 2007 1 undergrad studentFELDA Oil Products Heat recovery network retrofit 2008 1 undergrad student
MIMOS Semiconductor (MySEM)
Cost Effective Minimum Water Network using graphical approach
2006 1 PhD student1 undergrad student
Malaysian Newsprint Industry (MNI)
Maximum water recovery with regeneration targeting using numerical method
2006 1 MSc student
Optimisation of CHP system 2008 1 MSc studentPolycore Electrical Energy Management 2008 2 undergrad studentsInfineon Overall Plant Utility Optimisation 2008 1 MSc Student (part time)
Ethylene Malaysia Power recovery network 2006 1 MSc student
List of PROSPECT P2C List of PROSPECT P2C ProjectsProjects
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Company Projects Title Year Students Involved
TITAN Polymer (M) Sdn Bhd
• Modelling The Product Quality and Production Rate of 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
•
2008 3 MSc student4 undergrad students
Kempas Edible Oil Sdn Bhd
• Develop a prediction model for : Phosphoric acid and bleaching earth dosage for degumming and bleaching process, respectively, in palm oil refinery.
• Product quality of the refined oil from degumming and bleaching process.
2009 2 undergrad students
Mensilin Holdings Sdn Bhd
Optimisation of decentralized electricity generation from biogas and biomass.
2010 1 PhD student
Kerry Ingredients Modelling and optimization of Industrial Spray Dryer
2010 1 undergrad student
Kerteh Petronas Gas Bhd
Modelling of Benfield CO2 removal system Integrated reformer Methanol with natural gas plant Life cycle analysis (LCA)
2010 3 undergrad students
Overall Theme:Overall Theme:Sustainable Systems Sustainable Systems Planning, Design and Planning, Design and
OperationsOperations
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Process Design & Improvement
Process Design & Improvement
4 Key Focuses at 4 Key Focuses at PROSPECTPROSPECT
Product Design
Plant Optimization
Resource Planning
HOLISTIC RESOURCE HOLISTIC RESOURCE CONSERVATION NETWORKCONSERVATION NETWORK
Some End Users of the Some End Users of the Resource Conservation Resource Conservation ProjectsProjects
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Chem. Eng. Mag. Chem. Eng. Mag. (CEM), Dec 2006(CEM), Dec 2006
Increase priority
Source Elimination
Reuse/Outsourcing
Fresh Resouce
Regeneration Reuse
Source Reduction
Composting toilet
Normal electrical fan
Dual flush toilet
Vacuum toiletAerated Flow
Tap
RW Harvesting
MicrofiltrationSand filter with activated carbon
The Resource Management Hierarchy
Holistic Resource Conservation Network
Looking at the bigger picture!Looking at the bigger picture!
EfficiencyThemeTheme
Environment
Security
Systems Design
“Systems Design for Resource Sustainability”
Engineering Sustainability
Target (T)
Design (D)
T & D Holistic Retrofit Batch Math Model
Software
Heat
Water
Power
Gas
Mass/Materials
MultipleResources
* 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 Optimal Water SoftwareSoftware
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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 resources
EM Successful Case EM Successful Case Studies Studies
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>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 3Annual savings = USD 187,000 /yrPayback period = 1 year
Other available softwareOther available software
The composite curves
Results for maximum energy recovery
Software Features:- Maximum energy recovery targeting and design- Heat exchanger network design- Area calculations- Multiple utilities selections- Cost calculations
Software Features:- 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 WM Successful Case StudiesStudies
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FW reduction: 85.1%WW reduction: 97.7%
Net annual savings = RM 190, 000 /yearPayback period = 4 months
FW reduction: 35.8%WW reduction: 100%
Net annual savings = USD 105, 000 /yearPayback period = 1.87 years
FW reduction: 95.3 %WW reduction: 64.7 %
Net annual savings = USD 5, 400 /yearPayback period = 5 years
PALM OIL REFINERY PALM OIL REFINERY INTENSIFICATIONINTENSIFICATION
SFE-based Crude Palm Oil SFE-based Crude Palm Oil RefiningRefining
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• 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
It is envisioned that the use of supercritical extraction technology can lead to an intensified process that:
Palm Oil Refinery Palm Oil Refinery IntensificationIntensification
Genetic algorithm optimization of supercritical fluid extraction of nimbin from neem seeds, J. Food Eng., 97, 127–134, 2010.
Mathematical modeling and genetic algorithm optimization of clove oil extraction with supercritical carbon dioxide, The Journal of Supercritical Fluids, 51, 331–338, 2010.
Effects of Parameters on Yield for Sub-Critical R134a Extraction of Palm Oil, J. Food Eng., 95 (2009) 606–616
Selected/Related PROSPECT’s publications
Development of a New Process for Palm Oil Refining Based on Supercritical Fluid Extraction Technology, Ind. & Eng. Chem. Res., 2009, 48, 5420-5426:
Simulation Modeling of the Phase Behavior of Palm Oil –Supercritical Carbon Dioxide, JAOCS, Vol. 80, no. 11 (2003)
Integrate CPO refining & extraction of valuable comp in a few steps & avoid destruction of the comps
Experimental design, modeling, Experimental design, modeling, optimization of sub and optimization of sub and supercritical phenomenasupercritical 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
ProcessProcessOptimisationOptimisation
ExperimentalExperimentalTestingTesting
Process Process modeling modeling
& Dev& Dev
Objective:Cheaper, Cleaner,& Safer Proceses
Resource Planning
Resource Planning
Process Design & Improvement
Product Design
Plant Optimization
4 Key Focuses at 4 Key Focuses at PROSPECTPROSPECT
INTEGRATED, RESOURCE-INTEGRATED, RESOURCE-EFFICIENT RICE (IRE) MILL EFFICIENT RICE (IRE) MILL
COMPLEXCOMPLEX
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.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 Current Scenario of rice industryindustry
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ParametersParameters
heating and electricity requirements during
peak and off-peak seasons
heating and electricity requirements during
peak and off-peak seasons
cogen operating conditions
cogen operating conditions
capital cost for various sizes of cogen system
capital cost for various sizes of cogen system
distance between rice husk supply locations
and facility
distance between rice husk supply locations
and facility
transportation costtransportation costEconomic parameter
of each productEconomic parameter
of each productEconomic
parameter of each technology
Economic parameter of each
technology
Utility systemUtility system
logisticlogistic
Resource allocation
Resource allocation
A systematic framework is required to optimise these parameters to achieve optimal profit
A systematic framework is required to optimise these parameters to achieve optimal profit
an optimum logistic
network for RE supply to
the rice mills
an optimal integrated network of rice mill & downstream processes
optimal rice mill utility system with RE-mix
Superstructure:Superstructure:Optimal Design of A Rice Mill Utility Optimal Design of A Rice Mill Utility System System with Rice Husk Logistic Networkwith Rice Husk Logistic Network
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Rice husk from own rice mill
i
Rice husk from own rice mill
i
Rice husk from private rice mill
j
Rice husk from private rice mill
j
Boiler with different capacity
b
Boiler with different capacity
bturbine
bturbine
b
CHFc
CHFc
Electricity demand
Electricity demand
Cooling towerCooling tower
IBDi
IBDi
FBDi
FBDi
Electricity grid
Electricity grid
LP
MP
logistic Utility network facilities
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.
Superstructure:Superstructure:Optimal resource Optimal resource allocation for IRE allocation for IRE rice mills complexrice mills complex
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k=2Broken rice
k=2Broken rice
k =1Head rice
k =1Head rice
k=3Rice bran
k=3Rice bran
k=4Rice husk
k=4Rice husk
n=1Graded rice
5%
n=1Graded rice
5%
n=3Rice noodle
n=3Rice noodle
n=5Rice bran oil
n=5Rice bran oil
marketmarket
aa
cc dd een=6
furfuraln=6
furfural
ff
n=2Graded rice
10%
n=2Graded rice
10%n=4
Defatted rice bran
n=4Defatted rice
bran
Wet paddyWet paddy
bb
n =7Rice husk ash
n =7Rice husk ash
RH from out source
RH from out source
gg
Dried paddyDried paddy
INTEGRATED ENERGY & INTEGRATED ENERGY & EMISSIONS PLANNINGEMISSIONS PLANNING
Ministry of Energy,Green Tech and Water
J. Renew. Energy, Available online May 2010
Resource Planning for Resource Planning for Green and Secure Energy Green and Secure Energy Supply – National Policy Supply – National Policy formulationformulation
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
Demand Demand satisfactionsatisfaction
EmissionEmissionTargetTarget
RE RE targettarget
Objective:Min cost of
electricity generation
*Biomass, solar, wind, hydro, etc
Product Design
Product Design
Process Design & Improvement
Resource Planning
Plant Optimization
4 Key Focuses at 4 Key Focuses at PROSPECTPROSPECT
Tailor-Made Green Tailor-Made Green Diesel Diesel
and Gasolineand Gasoline
Tailor-Made Green Diesel and Gasoline (D&G)
• Aim– Aim– A A GREENERGREENER bio-bio-diesel or bio-gasoline diesel or bio-gasoline mixmix
• 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
Experimental Experimental validationvalidation
CADCADOptimalOptimal
formulationformulation
Target Target propertiesproperties
Objective:Green Diesel and Gasoline
Meeting TargetProperties
A Cleaner, Cost-effective A Cleaner, Cost-effective Solvent Alternative for Solvent Alternative for
Carotenoid and Vitamin E Carotenoid and Vitamin E Extraction from Palm OilExtraction from Palm Oil
Palm Oil Fine Chemical Solvent Design
• Aim– Aim– An alternative An alternative ECONOMICAL, SAFE ECONOMICAL, SAFE and and CLEANERCLEANER solvent solvent for Palm Oil Fine for Palm Oil Fine Chemical extractionChemical 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
Experimental Experimental validationvalidation
Solvent Solvent screeningscreening
(ICAS)(ICAS)
Target Target propertiesproperties
Objective:Solvent
Meeting TargetProperties
Process Design & Improvement
Resource Planning
Product Design
Plant Optimisation
Plant Optimisation
4 Key Focuses at 4 Key Focuses at PROSPECTPROSPECT
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Overall Refinery & Petrochemical Process Improvement using AI
Techniques
* 15 related published journal papers by PROSPECT in this area.
Types of ModelsTypes of ModelsType 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 Box-Empirical Model, Neural Network
viewed as models with a highly parameterized structure such 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)
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
Modeling a polymer reactor kinetics (black) and balances (white)
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1. Fast response2. Accuracy3. Noise tolerance4. Forecasting5. Overall process understanding6. Knowledge of highly nonlinearity system
ANN, 1ANN, 1stst Principle, Hybrid Principle, Hybrid Models Models
Outputs & Benefits• A dynamic simulator• Defining process most sensitive
parameters• Improved productivity ; e.g. for a refinery,
gasolinelight naphta heavy naphta gas oil and other products yield
• Other benefits– Pollution and waste minimization– Energy savings– Forecasting market demand for future
planning
Completed worksCompleted works
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No. Case study Plant Objective Benefits
1 Tabriz refinery/ Iran
Plat former unit Maximizing gasoline production
4.48 % increase in gasoline production
2 Typical Hydrotreater plant Plant simulator 99.9999%Accuracy
3 Typical Delayed coking unit
Plant simulator 99.9999%Accuracy
4 Tabriz refinery/ Iran
Hydrocracker unit
Maximizing light naphtha
5 Domestic Oil- asphaltene precipitation
Precipitate amount
99.6 % accuracy
6 Kuwait refinery Desalting unit Maximizing desalting and dehydration efficiency
7 Kuwait refinery Refinery Estimating Ozone concentration
99.00% accuracy
Polypropylene Polypropylene Dynamic Modeling Dynamic Modeling & Optimisation& Optimisation