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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

1

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

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Thank Youfor inquiries: http://www.fkkksa.utm.my/prospect

mfaiz@fkkksa.utm.my zain@fkkksa.utm.my