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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
2
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
• 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
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
3
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
4
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 & RegulationsPolicies & Regulations
• 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, …)
5
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
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
TechnologiesTechnologies
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
– Environmental impact assessment and management
– 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
– Molecular design and selection for energy applications
• Systems analysis, integration and optimization
– Water and energy systems analysis, integration and optimization
– Energy / Desalination infrastructure planning
Design of optimal desalination processes and systems
Process
Policies & RegulationsPolicies & Regulations
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
6
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
7
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
8
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
Goal 1• (a) Compact
Representation• (b) Explore different
designs
Goal 2
Goal 1(a): done.
• (a) Capture Multi-component effects
• (b) More realistic economic assessment
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
9
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
10
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))
34
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
11
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
Stage1:26.2Stage2:26.6Total: 44.4
Stream1:0.00099
None $229,102/yr 135 using LINDO,what’sBest!
Class 1 infeasible ‐ ‐ ‐ ‐ ‐
Class 2a Stgae1: 52Stage2: 47
Stage1:26.2Stage2:26.6Total: 44.4
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
Goal 1• (a) Compact
Representation• (b) Explore different
designs
Goal 2
Done.2. Significant reduction in CPU times
3. Avoids exhaustive searches that could be eliminated based on physical insights
• (a) Capture Multi-component effects
• (b) More realistic economic assessment
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)
12
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
Representation• (b) Explore different
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
13
Current Research Activities ‐ Detail
• Sustainable Water utilization
– Environmental impact assessment and management
– 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
– Molecular design and selection for energy applications
• Systems analysis, integration and optimization
– Water and energy systems analysis, integration and optimization
– Energy / Desalination infrastructure planning
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
Policies & RegulationsPolicies & Regulations
Integrated System
Integrated System
TechnologiesTechnologies
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
Current Research Activities ‐ Detail
• Sustainable Water utilization
– Environmental impact assessment and management
– 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
– Molecular design and selection for energy applications
• Systems analysis, integration and optimization
– Water and energy systems analysis, integration and optimization
– Energy / Desalination infrastructure planning
14
Development of a systematic method and tool to identify optimal heat recovery and co‐generation strategies
Industrial Zone Waste Heat Recovery & Reuse
Policies & RegulationsPolicies & Regulations
Integrated System
Integrated System
TechnologiesTechnologies
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
15
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
16
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
Current Research Activities ‐ Detail
• Sustainable Water utilization
– Environmental impact assessment and management
– 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
– Molecular design and selection for energy applications
• Systems analysis, integration and optimization
– Water and energy systems analysis, integration and optimization
– Energy / Desalination infrastructure planning
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.
18
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.
19
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
• 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
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.