Optimal Process Systems Design Water Energy Nexus · Feed Bypass to Feed Bypass to ......

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Optimal Process Systems Design and the Water‐Energy Nexus

Dr. Patrick Linke

Professor of Chemical EngineeringTexas A&M University at Qatar, Education City, Doha

Today

Broad overview

• Water and Energy Research at TAMUQ: The Qatar Sustainable Water & Energy Utilization Initiative (QWE)

• The Water‐Energy Nexus, Qatar and Decision‐Making• Process Systems Engineering Research• An excursion to reality: Qatar National Food Security

Programme (QNFSP) – A national sustainable g (Q )development model programme by the Office of the Heir Apparent

Breadth with the occasional depth

QWE

The Qatar Sustainable Water and Energy Utilization Initiative

• Center of Excellence at TAMUQ

• ActivitiesResearch and development, technical service, capacity building, outreach

• Stakeholder engagementThrough Strategic Advisory Board, QWE consortium (Chevron, RasGas, QP, GDF S Sti li W t ) t i ti l ( M EGDF Suez, Stirling Water), engagement in national programmes (e.g. MoE, QNFSP, GSDP: QNV‐QNDS) and targeted projects (e.g. QNRF, QSTP, QAFCO, … )

• Partnerships with world‐class groupsIn Qatar, North America, Europe and Asia to broaden and deepen scientific and technical expertise deployed at QWE.

QWE Capabilities• 3 Faculty with complementary expertise

– Dr. Patrick Linke– Dr. Ahmed Abdel‐Wahab– Dr. Marcelo Castier

• 15 highly qualified engineers and scientists

• Access to additional internationally recognized experts

• World class laboratories and i l f ili icomputational facilities

• More than $8M of funded projects

• To combine the power of experiments and computations to analyze, design and innovate micro to macro levels

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QWE Research Areas

• Technologies for important water / energy applications

– DesalinationProcess Innovation, hybrid desalination systems and systems analysis for solar desalination, including Zero liquid discharge systems and Value Extraction

– Advanced water and wastewater treatment processes

– Molecular design and selection for energy applications

• Systems analysis, integration and optimization

– Environmental impact assessment and management

I d t i l t l i i t ti d ti i ti– Industrial energy systems analysis, integration and optimization

– Water integration

– Energy / Desalination infrastructure planning

– Process optimization (reaction‐separation‐energy systems)

Today

Broad overview





Two statements

• It takes energy to provide waterIt t k t t d• It takes water to produce energy

… and an impressive terminology:

The Water-Energy Nexusgy

It takes energy to provide water

Technologies to improve water qualityimprove water quality require energy input.

The energy demand increases ithincreases with required level of quality improvement.

Source: www.unece.org

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It takes energy to provide water

Produce Distribute

Source Process Primary use

ENERGY

Secondary useOr recycle OR Discharge

Reclaim

CollectDistribute

It takes water to produce energy

• Power generation– Cooling– Cleaning (e g solar)Cleaning (e.g. solar)

• Exploration, production, processing– Oil & gas production & processing– CoolingCooling– Other process water

• Other (e.g. energy crop production)

Context | The State of Qatar

• Arid climate, extreme heat in summerLi it d t l f h t• Limited natural fresh water resources

• Abundant energy resources• Capital availability• Strong technology dependency for water (seawater desalination)

• Strong technology dependency for climate adaptation (air con)

Energy is key – National income, water, cool, …

National Vision 2030

• Sustainable development dimensionsE i– Economic

– Environmental– Social

• How could it relate to energy and water ?Minimize footprints (air water land economic)– Minimize footprints (air, water, land, economic)

– Seek social development and economic diversification opportunities

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Minimize footprints – how ?

• With efficient policies and regulationsT t ffi i t t h i l t• To create more efficient technical systems

• That integrate the most suitable technologies in a synergistic way

A system is a set of interacting or interdependent y f g p(technology) components forming an integrated whole.

NB: The “integrated whole” needs to be efficient.

Significance and roles

Policies & RegulationsPolicies & Regulations

• Policy‐making and regulation: Requires clear understanding of impacts onRegulationsRegulations

Integrated System

Integrated System

understanding of impacts on efficiency of the system

• Systems engineering:Technology selection, systems design, systems analysis/assessment

TechnologiesTechnologies • Technology development / applied science:Focus on technologies crucial to efficiency of the system

An aside – “decision‐making”

• Many objectives involved to assess “efficiency”– Economic (e.g. low cost, economic knock‐on effects, …)– Environmental (e.g. low emissions, low waste, small footprint, …)

– Social (e.g. acceptable, positive employment contributions, …)

• There is a limit: Non inferior solutions• There is a limit: Non‐inferior solutions“Can’t gain in one objective without loosing in another”

Good decisions through understanding of trade‐offs.

Context | Research Focus


• Policies & Regulations:Support decisions through understanding of systems

• Systems engineering:RegulationsRegulations

Integrated System

Integrated System

Systems engineering:Develop tools for assessment, analysis, optimal design, decision‐support for policy makers

• Technology development / applied science:I l i l

TechnologiesTechnologies

Innovate select crucial water and energy technologies that will be crucial to efficient systems; gain better understanding of fundamentals (chemistry, …)

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Today

Broad overview





Illustration of Water‐Energy Nexus issues –A Holistic Approach for Sustainable Use of Industrial Seawater for Process Cooling(QNRF NPRP, 2008 – 2011; Application case: MoE, QAFCO, QAPCO, QP)

Policies & RegulationsPolicies & Regulations • Develop a scientific framework for environmental Integrated System

Integrated System


impact assessment of cooling water discharge into seawater

• Develop quantitative techniques for predicting the reaction mechanisms and kinetics of biocides and their reaction products in seawater

• Develop computational tools to predict the fate and transport of biocides and their reaction productstransport of biocides and their reaction products

• Deploy process integration to reduce process heating (and therefore cooling) requirements

• Aid in developing sound regulatory policies

Current Research Activities ‐ Detail

• Sustainable Water utilization


– Desalination Process Innovation, hybrid desalination systems and systems analysis for solar desalination, including Zero liquid discharge systems and Value Extraction

– Integrated water management

• Sustainable Energy Utilization

– Energy utilization in industrial zones



– Water and energy systems analysis, integration and optimization


Design of optimal desalination processes and systems

Process


Integrated System

Integrated System

TechnologiesTechnologies Synthesis of optimal membrane desalination processes (RO, RO‐NF, …)

Systems integration and design

Better membrane element models for process analysis

Process Design: Optimal process configurations

Process systems optimization:Value extraction & energy integration (renewables)

Waterloss jth

Infrastructure

integration (renewables)

Macro-system:Integration with water/energy

infrastructures, infrastructure design

Sink D1 Sink DjthSink D2

INTERCEPTOR lth

Storage 1

Storage 2

Storage sth

Desal. Plant 1

Desal. Plant 2

Ground Water

Brine discharge

Seawater

Seawater

AquiferSink A1 Sink AkthSink A2

Main forDomestic

Main forAgriculture

Waterloss lth

Waterloss kth

Waterloss ith

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In detail …

Towards a Systematic Approach to the Synthesis of Optimal Membrane‐Based Seawater Desalination NetworksSeawater Desalination Networks

SWRO Network Design

Involves ‘Synthesis’ and ‘Optimization’• Generation of alternatives• Generation of alternatives• Analysis of each alternative to establish

performance• Identify optimal solutions

Thus, two different approach elements:– Representation: to capture all possible alternatives

– Optimization: to determine best solution(s)

Conventional 3‐Unit SuperstructureIllustration Example

RO Stage

Mixer

SplitterStageModule

Element

Conventional Approach

Global Optimization time : 643 CPU seconds

E.g. Saif, Y., Elkamel, A., and Pritzker, M., 2008, “Global Optimization of Reverse Osmosis Network for Wastewater Treatment and Minimization”, Ind. Eng. Chem. Res. 47(1) 3060‐3070

Optimal SolutionGlobal Optimization time : 643 CPU seconds(for a 2‐unit superstructure)

Optimal Solution

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Conventional SWRO Representation

– Initially, a complete and ‘comprehensive’ network superstructure representation is developed:superstructure representation is developed:

• Full connectivity• Continuous and discrete decision variables• Cost Function ‘Objective’• Typically 2 components: H2O and Salt • Class of optimization problem: MINLP

– The number of possible connectivity options increase exponentially as the total number of RO units increase

What would we like to achieve ?Complex Representation

Conventional Approach ‐Observations

Goal 1• (a) Compact

Representation• (b) Explore different

designs

Goal 2

Representation• Too many options

Simplistic Stage Model• Does not predict the Goal 2

• (a) Capture Multi-component effects

• (b) More realistic economic assessment

oes o p ed c einteresting phenomena (scaling, boron…)

Obtains One Solution

Towards a Compact Representation

Reduce Stream Connectivity Purpose

• Conceptualize network connections

• Eliminate or keep, depending on impact on performance

• Classification• Meaningless

• Use insight to significantly reduce number of combinatorial alternatives evaluated

• Directly eliminate inefficient cases

• Sometimes Meaningful• Always Meaningful

Classification of connections

Meaningless Sometimes Meaningful Always Meaningful

Feed Bypass to

Feed Bypass toPermeate

Concentrate Recycle

Concentrate &Permeate Mixing

Permeate &ConcentrateMixing

Concentrate &Concentrate Mixing

Feed Bypass to Concentrate

PermeateRecycle

Permeate &Permeate Mixing

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Lean Superstructure

3‐Unit Superstructure Arrangement

ConventionalNew

Lean Superstructure vs. Conventional 3‐Unit Lean Superstructure Optimal Solution

CPU time : 135 seconds

2‐Unit Conventional Superstructure (Saif et al,2008)Optimal Solution

CPU time : 643 seconds (reported)

New Approach Considerations



designs

Goal 2

Goal 1(a): done.



Systematic Design Exploration

TargetFrom Superstructure Optimization of N units

Performance

Different ways of Combining units

Distance from Target

No. units1 2 3 N

Increasing Design Complexity

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Design Exploration

Stage Added, on the concentrate side

Concentrate &

MeaninglessSometimes MeaningfulAlways Meaningful

Feed Bypass to

Stage Added

Single Stage Pass Added, on the permeate side

Permeate

Concentrate &Permeate Mixing

Permeate Mixing

Feed Bypass to

Concentrate Recycle

ypPermeate

Concentrate &Concentrate Mixing

Pass Added

Stage Added

Stage & Pass Added, on both sides

Recycleyp

ConcentratePermeate &Permeate Mixing

Pass Added

Design Exploration

• Generation of : Design Classes

– Design Classes correspond to multiple lean superstructures generated based on increasing network size and feasible connectivity

– Corresponding lean superstructures capture all structural and operational variants intended to be explored within distinct designs

Design Exploration

Class 2aClass 3a

Class 1a Class 2b

Class 3c

Class 3b

Class 3e

Class 3d

Problem Formulation

• The network design mathematical formulation includes:

P M d l & C i l i h d i i bl h– Process Models & Constraints relating the design variables to the physical stream structure &performance

– Performance Assessment which relate various operational and capital cost elements to the design variables• Minimize J

– J is a function of economical parameters• Subject. to.

– Equality & Inequality Constraints

– An optimization procedure taking into account all operational, technical and environmental constraints• Class of Optimization Problem: MINLP• Global Solver

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Problem Formulation

Mass Balance, Network Splitters Membrane Models Eqns*

Mass Balance, Network Mixers

Mass Balance, RO Units

Objective Function**

*F. Evangelista, A Short‐cut Method for the Design of Reverse‐OsmosisDesalination Plants, Ind. Eng. Chem. Process Des. 24 (1) (1985) 211

Process Constraints

**Saif, Y., Elkamel, A., and Pritzker, M., 2008, “Global Optimization ofReverse Osmosis Network for Wastewater Treatment and Minimization”,Ind. Eng. Chem. Res. 47(1) 3060‐3070

Synthesis Strategy & Implementation

Synthesis Strategy:– Target: Optimization of a ‘Lean Superstructure’Target: Optimization of a Lean Superstructure– Design: Performance trade‐off assessment of by means of

individual design classes

Implementation:– MS Excel– Lindo Solver:What'sBest! 9.0

• Excel add‐In for Linear, Nonlinear, and Integer Modeling and Optimization

• Transitions between Design Classes via a developed Excel Macro VBA Code

• Adjustable variables: unit Recoveries, stream split fractions

Implementation Case Study Example 1

• A Seawater desalination case study example has been carried out using

total inlet feedwater flowrate into the network (kg/s)19.29 / 13.052

composition of component i in inlet feedwater stream 0.03480feedwater pressure into the network (bar) 1final permeate pressure (bar) 1final reject pressure (bar) 1minimum permeate flow required in the network (kg/s) 5.79

i ll bl t ti f t i i th tbeen carried out using the modified representation, and compared to previous research efforts.

• Duo B-10 hollow fiber reverse osmosis membranes were

maximum allowable concentration of component i in the permeate stream

0.00047

membrane Area per module in RO unit j (m2) 152

pressure drop in RO unit j (bar)0.22

maximum allowable feed pressure in RO unit j (bar) 70

pure water permeability ( kg/(s N))1.22x10-

10

transport parameter of solute i (kg/( s m2)) 4.0x10-6

inner radius of hollow fiber (m) 21x10-6

outer radius of hollow fiber (m) 50x10-6

fiber length (m) 0.75

considered in all designs, using geometrical properties provided in the table

g ( )fiber seal length (m) 0.075cost coefficient of an RO module ($/(modue yr)) 1450operating cost coefficient of a pump unit ($/(kg/s) yr)) 80operating cost coefficient of an energy recovery turbine unit ($/(kg/s) yr))

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fixed cost coefficient of a pump unit cost ($/(kg/s)0.79 yr)) 139.93fractional constant corresponding to a pump unit fixed cost 0.79fixed cost coefficient of a turbine unit cost ($/(kg/s)0.47 yr)) 93.62=fractional constant corresponding to a turbine unit fixed cost 0.47

El‐Halwagi, M. 1992, “Synthesis of Reverse Osmosis Networks for Waste Reduction”, AIChE Journal, 38(8), 1185‐1198

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Case Study Results

# of Modules Water Recovery (%)

Optional Streams, Split Fractions

Eliminated Streams not considered in class representation, Split Fractions

Total Cost ($/yr) Computation Time CPUs

Saif et al, 2008 –Previous Work *

Stgae1: 54.8Stage2: 45.2

Stage1:27.2 Stage2:23.5Total: 30

Stream1:0.000622

Feed‐to‐brine bypass: 0.323 $230,906/yr 643.3 using CPLEX

GAMS22.5

# of Modules Water Recovery (%)

Optional Streams, Split Fractions

Eliminated Streams not considered in class representation, Split Fractions

Total Cost ($/yr) Computation Time CPUs

Saif et al, 2008 –Previous Work *

Stgae1: 54.8Stage2: 45.2

Stage1:27.2 Stage2:23.5Total: 30

Stream1:0.000622

Feed‐to‐brine bypass: 0.323 $230,906/yr 643.3 using CPLEX

GAMS22.5

Lean Superstructure

Stgae1: 52Stage2: 47

Stage1:26.2Stage2:26.6Total: 44.4

Stream1:0.00099

None $229,102/yr 135 using LINDO,what’sBest!

Lean Superstructure

Stgae1: 52Stage2: 47


Stream1:0.00099

None $229,102/yr 135 using LINDO,what’sBest!

Class 1 infeasible ‐ ‐ ‐ ‐ ‐

Class 2a Stgae1: 52Stage2: 47


Stream1:0.00099 None $229,102/yr 37 using

LINDO,what’sBest!

Class 2b infeasible ‐ ‐ ‐ ‐ ‐

Class 3aStgae1: 70Stage2: 16Stage3: 17

Stage1:33.4Stage2:21.5Stage3:10.6Total:44.4

Stream1: 0.00058Stream 4: 0.107Stream 5: 0.600

None $239,721/yr 42 using LINDO, what’sBest!

Stage1:24.9

* Saif, Y., Elkamel, A., and Pritzker, M., 2008, “Global Optimization of Reverse Osmosis Network for Wastewater Treatment and Minimization”, Ind. Eng. Chem. Res. 47(1) 3060‐3070

Class 3cStgae1: 49Stage2: 50Pass 1 : 5

Stage1:24.9Stage2:25.9Pass1:99.4Total:44.4

Stream1:0.00098Stream 5: 0.9179 None $237,917/yr 24 using LINDO,

what’sBest!

Class 3b infeasible ‐ ‐ ‐ ‐ ‐

Class 3d infeasible ‐ ‐ ‐ ‐ ‐

Class 3eStgae1: 30Pass 1 : 4Stage2: 70

Stage1:15.6Pass1:97.6Stage2:34.0Total:44.4

Stream1:0.00118Stream3:0.7

None $239,342yr 32 using LINDO,what’sBest!

Achievements

1. A much leaner representation in the form of multiple condensed superstructures



designs

Goal 2

Done.2. Significant reduction in CPU times

3. Avoids exhaustive searches that could be eliminated based on physical insights



4. Multiple innovative designs can be developed systematically

5. Easy to use implementation

99.5

100 K

Na

Mg

Ca

HCO3 99

99.5

100Ca

Mg

Na

K

Capture Multi‐component Effects

98

98.5

99

% S

alt R

ejec

tion

HCO3

Cl

SO4

Sr

K

Na

HCO3

Cl

Linear (K)

Li (N )97

97.5

98

98.5

99

% S

alt R

ejec

tion

HCO3

SO4

Cl

Sr

Linear (Mg)

Linear (Ca/Mg)Linear (Na)

Linear (K)

97

97.5

18 23 28 33 38 43

Temperature (C)

Linear (Na)

Linear (Mg)

Linear (Ca)

Linear (HCO3)Linear (Cl)

Linear (SO4)

ROSA Plot: Rejection vs. Temperatureat maximum feed pressure conditions and constant feed flow for SW30HRLE – 440i elements, Typical Seawater feed results

96

96.5

18 28 38 48Temperature (C)

( )

Linear (HCO3)Linear (Sr)

Linear (Cl)

IMSDesign Plot: Rejection vs. Temperature at maximum feed pressure conditions and constant feed flow forSWC5 – 4040 elements, Typical Seawater feed results

Economical Assessment

• Synthesis vs. Reality– Synthesis:Synthesis:

• Includes Energy and Membrane Cost

– Reality: • Incorporates additional elements• Typical Breakdown:

Total Annual Cost (TAC)

Total Capital Investment

(TCI)

Total Operating

Cost (TOC)Typical Breakdown:

Direct Capital Costs

(DCC)Soft Capital Costs (SCC)

Variable Operating

Costs (VOC)

Fixed Operating

Costs (FOC)

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Case Study 2 ‐ Results

IonsNetwork Permeate g/L

Post Treatment Permeate g/LConcentrate

g/LIons Feed

g/L

Capital:

K 0.00679 0.00679Na 0.13573 0.13573Mg 0.00335 0.01000Ca 0.00106 0.03000Sr 0.00003 0.00003CO3 0.05974HCO3 0.00289 0.00289

SO4 0.00703 0.00703Cl 0.24778 0.24778TDS 0.4044 0.5Flow(L/s) 138.9 138.9

g/L0.54015.0221.8010.5710.0190.1993.78127.01648.948324.1

g/LK 0.38Na 10.556Mg 1.262Ca 0.400Sr 0.13HCO3 0.140SO4 2.649Cl 18.98TDS 34.43Flow (L/s)

462.96

$695.81 per m3/day

Operating:

$0.6465 per m3

Optimal Design Class: 3c

CPU time for Class 3c: 42s

Case Study ResultsOperating and Maintenance Cost Breakdown

($/m3)

Variable O&MPower

Intake 0.0126Pretreatment 0.0009Reverse Osmosis 0 2237

Capital Cost Breakdown ($/m3/day)Direct Capital (construction costs)Site Preparation 10.0Intake 50.0Pretreatment 100.0RO system Equipment

RO Skids 50.6Capital Cost Breakdown O&M Cost BreakdownCapital/O&M

Cost DistributionReverse Osmosis 0.2237Product Water 0.0117Membrane Cleaning 0.0018Service Facilities 0.0086

Total Power Cost 0.2593Chemicals 0.05Membrane Replacement(replaced every 5 yrs)

0.0567

Cartridge Filter Replacement 0.0072Waste sream Disposal 0.0333Subtotal 0.4065Fixed O&MLabor 0.05Maintenance 0.0833

Piping 15.9Cartridge Filters 1.8RO modules 40.5RO Pumps 24.5RO ERDs 5.0

Total RO system Eq 138.3Post treatment(without boron removal)

20.0

Waste DisposalMembrane cleaning Chemicals 10.0Solids 10.0

Concentrate stream Disposal (Co-location, desal+Power Plant Discharge)

5.0

Instrumentation and Control 9 1

Direct Capital65%

Soft Costs 30%

Contingency5%

Capital Cost Breakdown

Variable O&M63%

Fixed O&M22%

Indirect OM15%

O&M Cost Breakdown

Direct Capital48%

Soft Capital

Variable O&M19%

Fixed O&M11%

Cost Distribution

Environmental & Performance Monitoring

0.0067

Indirect O&M 0.10Subtotal 0.2400Total O&M 0.6465

Instrumentation and Control 9.1Buildings 50.2Electrical 6.9Auxiliary and Service Equipment 20.0Startup, Commissioning & Acceptance 25.0Subtotal 454.5Soft CostsProject Engineering Services 90.0Project Development 100.0Project Financing 18.2Subtotal 208.2Contingency 33.1Total 695.8

22%

Achievements

Goal 1 Done.• (a) Compact


designs

Goal 2• (a) Capture Multi-

t ff t

Done.

Done.

component effects• (b) More realistic economic

assessment

Mission accomplished ?

Current and Future

– Foundation laid

– Subsequently introduce “excitement”

• Transition into Boron Removal options• Integration of a diverse mix of viable membrane options • Hybrid systems (RO‐NF …)• Explore pretreatment decision options• Explore pretreatment decision options• Explore ZLD and value extraction options

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Sink D1 Sink DjthSink D2

Waterloss jth

Storage 1

Storage 2

Storage sth

Waterloss ith

Macroscopic water management

INTERCEPTOR lthStorage s

Desal. Plant 1

Desal. Plant 2

Ground Water

Brine discharge

Seawater

Main forDomestic

Main forAgriculture

Waterloss lth

Seawater

AquiferSink A1 Sink AkthSink A2

Waterloss kth


Integrated System

Integrated System


Domestic SinksTo brine discharge

23 Losses

45 Losses

22

151

Water network distribution in January(units: MIGD)

Seawater Intake

Groundwater Intake

Desalination Plant

Groundwater/Aquifer

Wastewater Treatment Plant

Agricultural Sinks

Storage

Greening

738

133

63

39

18

231

133

35

Aquifer Recharge

Discharge to Gulf

106

6 Losses












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Development of a systematic method and tool to identify optimal heat recovery and co‐generation strategies

Industrial Zone Waste Heat Recovery & Reuse


Integrated System

Integrated System


1500 m

OutputTool for policy makers and regulators to guide quick Identification of synergies to maximize energy recoveries energy within industrial zones(heat, co‐generation, tri‐generation cases).

Industrial zone

• Many plants, many processes• Each plant runs an independent utility system to serve its• Each plant runs an independent utility system to serve its

processes• No energy integration between plants• Plants belong to different companies• Billion dollar investments 1 hour shutdown, up to $1MM

lost profits reliability crucial• Air quality issues, cooling water discharges environmental factors limit growth

• Need to save energy

Existing approaches

• Only available for plants(multiple processes served common utility system)

• Indirect integration Total SitesA li i l l i d i l– Applies to single plant, not to industrial zone

• Direct integration Not acceptable in industrial zone Need a new approach

Development of a systematic method and tool to identify optimal heat recovery and co‐generation strategies

Industrial Zone = Multiple plants

1500 m

• Feasibility for waste heat recovery and reuse for each process stream – cold utility match wrt T, P, distance

• Integrate periphery and keep utility systems intact (reliability)• Cost of connection ultimately determines decision

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Methodology – 5 steps

1. Data acquisition2. Screening for waste heat recovery potential3. Feasible integration options for waste heat recovery

and reuse4. Targeting the maximum waste heat recovery and reuse

potential(involves solution of a linear program)

5. Design optimal waste heat recovery and reuse networks(involves solution of a mixed integer non‐linear programs)

Case study –Four Petrochemical Plants

Existing utility requirements

Plant No. Type Tin(°C)

Tout(°C)

P (bar)

Heat required (kW)

1 Hot oil 400 340 2 50000

1 Steam 280 240 33.48 23000

1 Steam 240 200 15.55 29000

2 Steam 320 240 33.48 20000

2 Steam 250 220 23.2 39000

2 Steam 200 170 7.92 21000

2 S 1 0 120 1 98 280002 Steam 150 120 1.98 28000

4 Steam 220 190 12.53 19000

4 Steam 150 130 2.70 25000

4 Steam 120 105 1.21 23000

Step 1 – Hot process stream and cold utility heat exchanger data

Process stream Utility

Plant No. Tin (°C) Tout (°C) ΔH (kW) Type Tin (°C) Tout (°C) P (bar)

1 230 60 30000 Air 30 40 1.1

1 200 55 20000 Air 30 40 1.1

Plant No.

Tin (°C)

Tout (°C)

ΔH (kW) Type Tin

(°C)Tout

(°C) P (bar) Ip

1 200 55 20000 Air 30 40 1.1

1 55 40 10000 Water 30 35 1

2 200 60 20000 Steam 30 170 2.7

3 330 60 25000 Water 30 40 1

3 300 70 20000 Water 30 40 1

4 180 50 12000 Steam 30 150 4.76

o. ( C) ( C) ( W) ( C) ( C)

1 230 60 30000 Steam 30 200 7.92 18.16

1 200 55 20000 Steam 30 170 2.7 14.50

1 55 40 10000 Water 30 45 1 1.94

2 210 60 20000 Steam 30 170 2.7 1

3 330 60 25000 Steam 30 300 33.48 15.78

3 300 70 20000 Steam 30 270 23.2 14.93

4 180 50 12000 Steam 30 150 4.76 1

Step 2 –New process stream ‐ utility

matches

Source Sink Distance (m)

ΔT (°C)

Transport temperature

(°C)

Feasible

RU11 U23 1000 15 215 0

RU11 U24 1000 15 165 1

RU11 U42 900 13.5 163.5 1

RU11 U43 900 13.5 133.5 1

RU12 U24 1000 15 165 1

RU12 U42 900 13.5 163.5 112 42

RU12 U43 900 13.5 133.5 1

RU31 U12 700 17.5 297.5 1

RU31 U13 700 17.5 257.5 1

RU31 U22 800 20 270 1

RU31 U23 800 20 220 1

RU31 U24 800 12 162 1

RU31 U41 850 21.25 245.5 1

RU31 U42 850 12.75 162.75 1

Step 3 –Feasibility of links

RU31 U43 850 12.75 132.75 1

RU32 U13 700 17.5 257.5 1

RU32 U22 800 20 270 1

RU32 U23 800 12 212 1

RU32 U24 800 12 174 1

RU32 U41 850 21.25 241.25 1

RU32 U42 850 12.75 162.75 1

RU32 U43 850 12.75 132.75 1

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LinkUtilit

y type

Mass flowrate (kg/s)

Heat in process utility exchanger (kW) Heat recovered (kW)

1124 LP 3.01 8078.15 6826.94

Step 4 – Targeting (LP)

1142 LP 8.17 21921.85 18147.03

1242 LP 3.09 8278.49 6852.97

1243 LP 4.47 11721.51 10191.98

3124 LP 9.33 25000 21173.06

3223 MP 1.89 5277.86 4000.45

3243 LP 5.62 14722.14 12808.02

Waste heat recovery and reuse target at 25.6 %of heat currently supplied by the utility systems in the industrial zone

Step 5 – Network design (MINLP)

Overall annual gross profit GP = USD 4,441,870 Heat recovery and reuse of 72,741 kW(23.3% of heat currently supplied by the utility systems in the industrial zone)

, , , , , , , , , , , ,_P I P K

i j k l i j k l i j k l i j k li I j J k K l L

TCC = b Cost Hex Pcul L

, , , , , , , , ,($ / )P I P K

i j k l i j k l i j k li I j J k K l L

R year b m P

5 %23.3 %

10‐15 %25 %

20 % 25.6 %

Next steps

• Expansion to co‐generation– In utility plants– In utility plants– Decentralised

• Expansion to integrate desalination – thermal and membrane (tri‐generation)

• Policy and regulatory issues (how to stimulate savings in different owner‐regulator situations)

• Application to full industrial zone

17












Low to medium grade heat (60 … 400C)Organic Working Fluid

Systematic Design and Working Fluid Selection for Organic Rankine Cycles

Power(kW … MW)

• Installed ORC capacity > 1.5GWel

• Applications• Geothermal• Solar thermal

(lower reported costs compared t PV d CSP)

Waste heat into cooling medium(or CHP application)

to PV and conv. CSP)

• Waste heat• Biomass

Working fluid selection‐General

• Economic, environmental and operating performance of an ORC depends on:– the properties of the working fluids – design and operating characteristics of the cycle.

• Suitable fluid for an ORC must exhibit favorable properties – physical, – chemical,

i l– environmental, – Safety

• Usually involves known fluids and a “trial and error approach”

Working fluid selection –Computer‐Aided Molecular Design

• A systematic working fluid selection approach that employs rigorous screening of vast numbers of workingemploys rigorous screening of vast numbers of working fluids

• Method employs a computer aided molecular design approach to ensure known and yet unknown ORC working fluids are explored in conceptual ORC design

• CAMD approach highlights the importance of rigorous screening of working fluids as opposed to searches basedscreening of working fluids as opposed to searches based on trial‐and‐error.

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Integrated ORC Design Framework

Create benign and efficient WF using GC methods and

CAMD framework

Simulate and optimise ORC for maximum

economic performanceCAMD framework economic performance

In‐house toolsIn‐house and commercial (AspenPlus / Icarus) tools

Case study

Thermodynamic Environmental Safety Process‐related

Criteria considered in CAMD‐WF design

1. Density (ρ)

2. Latent heat of vaporisation (Hv)

3. Liquid heat capacity (Cpl)

4. Viscosity (μ)

5. Thermal conductivity (λ)

6. Melting point temperature (Τm)

1. Ozone depletion

potential (ODP)

2. Global warming potential

(GWP)

1. Toxicity (C)

2. Flammability (F)

1. Efficiency (η)

2. Maximum operating pressure

(Pmax)

3. Mass flowrate (mf)

“Default” performance criterion for ORC: Ratio of gross revenue over equipment cost for given electricity cost

Results: Designed molecules available in databases

ID Molecule Name CAS Registry

η (%)

Pmax (atm)

Pmin (atm)

mf (kg/hr) I20,90,l F C

Hydrocarbons 1 Butane 106-97-8 7.51 9.85 3.23 13069 0.26 0.564 1.94

2 2-Methyl-1,3-butadiene 78-79-5 8.02 3.77 1.08 12840 -2.21 0.589 2.49

3 2-Methyl-1-butene 563-46-2 7.88 4.14 1.15 12869 -1.92 0.594 2.83 4 1,4-Pentadiene 591-93-5 7.94 4.62 1.47 14230 -1.68 0.622 1.54 5 1,3,5-Hexatriene 821-07-8 8.18 1.28 0.28 12152 2.00 0.665 1.91

Results are illustrated only for I20,90, (20m3/hr, 90oC).

The obtained molecules involve hydrocarbons, HFCs, Fluorinated

5 1,3,5 Hexatriene 821 07 8 8.18 1.28 0.28 12152 2.00 0.665 1.91 6 1,3-Butadiene 106-99-0 7.64 11.29 3.72 12699 0.42 0.592 1.25

Hydrofluorocarbons (HFCs)

7 1,1,1,3,3,3-Hexafluoro-propane 690-39-1 6.61 18.00 6.07 37611 6.16 NF 2.84

8 3,3,3-Trifluoro-propene 677-21-4 6.82 21.22 7.54 27580 5.94 0.409 2.19

Fluorinated ethers

9 Methoxy methyl-fluoride 460-22-0 8.12 6.27 1.75 12683 -1.75 0.584 0.97

10 Methyl-trifluoromethyl-ether 421-14-7 7.27 16.98 5.61 24892 3.663 0.513 1.54

11 1,1,1-Trifluoro-methoxy-ethane 460-43-5 7.88 4.24 1.09 19695 -1.27 0.528 1.84

12 2,2,2-Trifluoro-ethyl-ethyl-ether 461-24-5 7.85 3.05 0.72 20098 2.64 0.538 2.13

Ethers 13 Methoxy-ethene 107-25-5 7.88 9.38 2.82 12469 -0.57 0.721 0.89 14 Methoxy-ethane 540-67-0 7.78 8.82 2.68 12620 -0.54 0.555 1.24 15 Dimethyl-ether 115-10-6 7.32 22.19 7.72 12828 4.00 0.567 0.95 16 Dimethoxy-methane 109-87-5 8.20 3.23 0.79 13030 1.34 0.601 0.83

ethers, Ethers, Amines, Formates, Aldehydes, Alcohols.

Molecules with high overall performance: 2‐Methyl‐1,3‐butadiene, 2‐Methyl‐1‐butene, Methyl‐formate, Acetaldehyde

They all indicate high efficiency, low maximum and minimum operating

17 2-Methoxy-1-propene 116-11-0 8.19 2.29 0.55 11845 1.58 0.642 2.13 18 Methyl-propyl-ether 557-17-5 8.02 3.44 0.87 12585 1.45 0.600 1.54 19 3-Methoxy-1-propene 627-40-7 8.19 2.63 0.63 11983 1.48 0.642 1.19

20 3-Ethenyloxy-1-propene 3917-15-5 8.19 1.72 0.38 12875 1.93 0.664 1.13

Amines

21 N-Methyl-methanamine 124-40-3 7.94 9.98 2.82 9246 -0.87 0.559 1.24

22 N-Methyl-ethylamine 624-78-2 8.29 2.31 0.51 9300 1.28 0.578 1.54

23 N,N-Dimethylallyl amine 2155-94-4 8.17 1.02 0.20 11928 2.11 NA 1.57

Formates 24 Methyl-formate 107-31-3 8.33 4.57 1.14 10806 -2.72 0.558 1.60

Aldehydes 25 Trifluoro-acetaldehyde 75-90-1 7.92 8.65 2.37 19206 -0.26 0.443 2.60 26 Acetaldehyde 75-07-0 8.28 5.96 1.54 8370 -2.53 0.667 2.01

Alcohols 27 Methanol 67-56-1 8.60 1.84 0.27 4204 0.74 0.591 1.02

pressure (but greater than 1 atm) and low mass flowrate compared to the other choices.

They also show flammability less that 0.6, except for acetaldehyde that is strongly flammable

Toxicity is low for all molecules

Results: Novel molecular structures

ID Molecule name η (%)

Pmax (atm)

Pmin (atm)

mf (kg/hr) I20,90,l F C

Fluorinated Amines and Ether-Amines

28 CF3-CH2-NH2 8.05 4.65 1.12 16685 -1.75 0.447 1.75 29 CF3-CH(CF3)-NH2 7.97 1.78 0.34 22592 3.26 0.406 3.06 30 CH NH CF 8 04 4 71 1 17 17178 1 65 0 477 1 8430 CH3-NH-CF3 8.04 4.71 1.17 17178 -1.65 0.477 1.8431 CH3-O-CH2-NH2 8.42 1.31 0.24 8565 1.40 0.584 0.75 Fluorinated ethers

32 FCH2-O-CH2-O-CF3 8.15 1.11 0.20 19325 2.88 0.524 1.44 33 FCH2-O-CF3 7.62 9.59 2.82 24190 0.95 0.406 1.56 34 CH2=CH-CH2-O-CF3 7.82 3.78 0.94 20880 2.42 0.559 1.78

The obtained molecules involve Fluorinated Amines, Ether‐Amines and Fluorinated Ethers.

The molecules are novel structures, not available in databases. The molecular structures indicate combinations of groups that may

lead to high overall performance. The performance index value is slightly lower than the high

performance molecules selected previously, due to the higher mass flowrate.

However, high mass flowrate is traded‐off for significantly lower flammability and toxicity.

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ID Molecule Vaporizer CondenserRgross/

CCAP

Area (m2) Cost ($) Area (m2) Cost ($)

31 Methyl-formate (R611) 42 15390 32.9 14450 4.88

17 Methoxy-ethane 37.6 14720 30.6 13670 4.77

44 FCH2-O-O-H2CF 45 15410 40.7 15390 4.76

M1 (34) Methanol 45 15390 47 8 17510 4 58M1 (34) Methanol 45 15390 47.8 17510 4.58

26 N-methyl-ethylamine 45.2 15300 44.8 15950 4.54

4 2-Methyl-1,3-butadiene 45.2 15190 44.8 15900 4.51

32 Trifluoro acetaldehyde 50.6 16770 37 14760 4.37

40 FCH2-O-CH2-O-CF3 79.2 19330 37.7 14580 4.20

M2 (3) Butane (R600) 37.6 14970 52 16600 4.13

15 2,2,2-Trifluoro-ethyl-ethyl-

A new winner

ether 55.7 16660 52.8 17130 4.05

39 CH3-O-CH2-NH2 50.6 16060 44 20810 3.99

M3 Difluoro-1,1-ethane 27.3 15700 38.3 15030 3.98

M4 Tetrafluoro-ethane 27.3 16310 41.8 15470 3.55

35 CF3-CH2-NH2 50.6 16060 107.2 23990 3.53

M5 (2) Propane (R290) 24.5 16470 41.8 16470 3.23

• ORC‐WF Design Framework identified many high performance Working Fluids fast and reliably

P f f id tifi d ( ) WF i t

Remarks

– Performance of identified (new) WFs superior to those of conventional WFs (i.e. those that had been suggested previously)

– New WFs are not necessarily new molecules (best molecule is a known refrigerant)

• Incorporation of economic objectives easily possible• Safety and Environment considered as part of design

framework

Systematic innovation framework for ORCs !

Today

Broad overview





The Qatar National Food Security ProgrammeSecurity Programme –

Water & Energy Dimensions

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Program Overview QNFSP OBJECTIVES

• Improve self-sufficiency (Infrastructure)

Via a sustainable domestic food production model using environmentally sound technology and market stabilization processenvironmentally sound technology and market stabilization process

• Economic Diversification (Sustainability)Sustain food security infrastructure and maximize opportunities through intelligent investment strategies within the supply chain(develop industries)

INFRASTRUCTURE CONCEPT

Guiding Principles

• Low impactLow impact

• Technology neutral

• Flexible

• Performance oriented

• Synergeticy g

• Integrated

RANGE OF ACCESSIBLE TECHNOLOGIES

Grid/Transmission Desalination Water

StoragePower

Solar Power

Water Treatment Systems

CSP-Thermal PV CPV CSP Sterling…

Wind Farms Offshore

RO CDI TVC…

Heat MED MD FO

Waste Heat ORC…

…

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Water & Energy Technology Industries

• Many emerging technologies

• Identify & exploit opportunities for Qatar

• Pull with infrastructure investments

• Develop and retain capacity to attain market leadership in areas with strategic opportunities

• Develop and monetize know how globally

… and help diversify Qatar’s economy… and help diversify Qatar s economy

Thank you.

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