Conceptual Design of a Vision 21 Planning Model
Transcript of Conceptual Design of a Vision 21 Planning Model
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Conceptual Design of aVision 21 Planning Model
Ed Rubin, Mike Berkenpas,Urmila Diwekar and Karen Kietzke
Center for Energy and Environmental StudiesCarnegie Mellon University
July 19, 1999
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Objectives
Develop a flexible and easy-to-use modeling system to estimate the performance, environmental emissions and cost of a preliminary Vision 21 plant design
Develop a framework for comparing alternative options and on a systematic basis, including effects of uncertainty
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Current FETC Projects
Development of the Integrated Environmental Control Model (IECM)Duration: September 1992 - April 1999Amount: $1.3 millionCOR: Gerst Gibbon
Development and Application of Optimal Design Capability for Coal Gasification SystemsDuration: September 1992 - February 2000Amount: $1.5 millionCOR: Gerst Gibbon
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Increasing complexity of advanced processesMultiple options for component design & selectionStrong interactions among system componentsSignificant uncertainties in the performance and cost of new technologies
Advanced Design and Analysis Methods are Needed
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Approach
Process Technology ModelsEngineering Economic ModelsAdvanced Software CapabilitiesSystems Analysis Framework
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Technologies Modeled and Evaluated
Pulverized Coal Combustion Plants– Selective catalytic reduction (SCR)– Wet lime/limestone FGD– Lime spray dryer– Electrostatic precipitators– Fabric filters
Advanced Environmental Control Systems– Combined SO2/NOx removal
Coal Beneficiation Processes
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Integrated EnvironmentalControl Model (IECM)
CoalCleaning
CombustionControls
Flue Gas Cleanup & Waste Management
NOxRemoval
ParticulateRemoval
CombinedSOx/NOxRemoval
AdvancedParticulateRemoval
SO2Removal
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Technologies Modeled (con’t)
Integrated Gasification Combined Cycles (IGCC)– Air and oxygen blown gasifiers– Fixed bed and fluidized bed gasifiers– Hot gas and cold gas cleanup systems– Byproduct recovery options (e.g., sulfuric acid,
Claus plant, direct sulfur reduction process)– Other environmental controls (e.g., SCR)
Pressurized Fluidized Bed Combustion (PFBC) Externally-Fired Combined Cycle (EFCC)
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ASPEN Model of an IGCC System
CoalHandling
Gasification,Particulate &Ash Removal,Fines Recycle
GasTurbines
BoilerFeedwaterTreatment
SteamTurbine
SteamCycle
& SCR
SteamCycle
& SCR
ZincFerriteProcess
ZincFerriteProcess
SulfuricAcid PlantSulfuric
Acid Plant
SulfuricAcid
Rawwater
Coal Coal
CleanSyngas
Exhaust Gas
Gasifier Steam
Boiler Feedwater
Exhaust Gas
Shift & Regen.Steam
Cyclone
RawSyngas
Cyclone
Blowdown
Return Water
CoolingWater
Makeup
CoolingWaterBlowdown
Air
NetElectricityOutput
InternalElectricLoads
Ash Gasifier Air
AirTailgas
Off-GasCaptured Fines
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Employ detailed mass and energy balancesEmpirical relationships and models used for complex process chemistryCalculate component and system mass flows, energy flows, and efficiencyCalculate multi-media environmental emissionsApproximately 10-20 performance parameters for each process technology
Process Performance Models
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Direct cost models for each major process area (typically 5-10 areas per technology)Explicit links to process performance modelsCalculate total capital costCalculate variable operating costsCalculate fixed operating costsCalculate annualized cost of electricityApproximately 20-30 cost parameters for each process technology
Process Cost Models
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New Modeling Capabilities
System
Simulation
Optimization
Synthesis
Deterministic
√
√
√
Stochastic
√
√
√
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Conventional Process Modeling(Deterministic Simulation)
ProcessModel
ParameterValues
ResultsResults
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Parameter UncertaintyDistributions
NORMAL UNIFORM LOGNORMAL
FRACTILETRIANGULAR BETA
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Stochastic Simulation
StochasticModeler
SAMPLINGLOOP
ProcessModel
ParameterUncertainty
DistributionsResultsResults
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Externally-Fired Combined Cycle (EFCC) Plant Efficiency
444342414039380.0
0.2
0.4
0.6
0.8
1.0
ProbabilisticDeterministic
Net Plant Efficiency (%, HHV basis)
Cum
ulat
ive
Prob
abili
ty
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Second Generation PFBC SystemTotal Capital Cost
Cum
ulat
ive
Prob
abili
ty
Total Capital Requirement ($1994/kW)1000 1100 1200 1300 1400 1500
DOE (1989)Probabilistic
0.0
0.2
0.4
0.6
0.8
1.0
524 MW net
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Some Questions Addressed byStochastic Simulation
What performance, emissions and cost can we expect given current uncertainties?What is the likelihood of performance shortfalls? Of cost overruns?What factors or process parameters contribute most to the overall uncertainty in performance and cost?How does this system or process compare to other competing technologies?What is the potential payoff of R&D to reduce the key uncertainties and risks?
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Value of Targeted Research in Reducing the Cost of an IGCC System
1201101009080706050400.0
0.2
0.4
0.6
0.8
1.0
Base Case UncertaintiesReduced Uncertainties inSelected Performanceand Cost Parameters
Levelized Cost of Electricity, Constant 1989 Mills/kWh
Input Uncertainty Assumptions
Cum
ulat
ive
Prob
abili
ty
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Stochastic Optimization
StochasticModeler
OPTIMIZATIONLOOP
Optimizer
SAMPLINGLOOP
ProcessModel
ParameterUncertainty
Distributions
ProbabilisticObjective Function
and ConstraintsResultsResults
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Process Synthesis
MILPMaster
Optimizer
Process Model
SYNTHESISLOOP
OPTIMIZATIONLOOP
SelectedFlowsheetTopology
SuperstructureAlternatives
Start
ResultsResults
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Is there a better choice of parameter values for this process to improve its performance? To lower its cost?What levels of performance, emissions and cost can we expect from an optimized design?How do uncertainties in process performance and cost parameters affect the optimal design?What design choices will minimize the risk of a performance shortfall? Or the risk of a cost overrun?
Some Questions Addressed byOptimization Capabilities
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How should the flowsheet be configured to achieve performance goals at lowest cost?What are the feasible flowsheet options to meet specified goals and constraints? Which options are not feasible?What are the cost savings (or performance and environmental gains) from moving to a more optimal design?
Some Questions Addressed by Process Synthesis Capabilities
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New Work in Progress
Expansion of IECM modules
Vision 21 systems analysis framework (The Vision 21 Planner)
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The Vision 21 Planner Would . . .
Bring together a spectrum of performance and cost models for plant components and integrated systems, suitable for preliminary design and analysisRun quickly and easily on a desktop or laptop computerUse publically available softwareAllow new process concepts to be easily modeledAllow uncertainties to be characterized explicitlyFacilitate selection of optimal (most promising) designs
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Com
mun
icat
ion
A Hierarchy of Process Models
EnterpriseEnterpriseEnterprise
System ModelsSystem ModelsSystem Models
Integrated ModelsIntegrated ModelsIntegrated Models
Component ModelsComponent ModelsComponent Models
Mechanistic/Empirical ModelsMechanistic/Empirical ModelsMechanistic/Empirical Models
Val
idat
ion
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Attributes of Process ModelsTurnaroundTime
Scope
Complexity
Micro-Scale
PlantComponent
Mult-Component
IntegratedPlant
Sec
Min
Days
Hrs
Weeks
Algebraic
Mechanistic
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Integrated EnvironmentalControl Model (IECM)
CoalCleaning
CombustionControls
Flue Gas Cleanup & Waste Management
NOxRemoval
ParticulateRemoval
CombinedSOx/NOxRemoval
AdvancedParticulateRemoval
SO2Removal
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(live demo of the IECM)
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Schematic of the Proposed Vision 21 Planner
ProcessOptions
SeparationCatalysisTurbines
Fuel CellsHeat Exch.
Gas StreamCleanup
NOxSOxTSP
EnergyConversion
IGCCBoilerPFBC
FuelUpgrading
Coal Cleaning
FeedstockCoalOilNat. Gas
BiomassMSW
Output OptionsElectricityChemicalsTrans. Fuels
SyngasHydrogenSteam
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Vision 21 Planner:Operation Overview
Optimization/Synthesis
Select ComponentOptions
Optimization
Set Objective& Constraints
Set DecisionVariablesSpecify a
Flowsheet
OpenSession
Simulation Choose ProcessParameters
GetResults
Specify aFlowsheet
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Welcome to the
Vision 21Planner
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Opening Screen:A Menu of Technology Options
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Select Gasification Combined Cycle (IGCC) Options
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Select KRW Gasifier
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Select Oxygen Plant
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Select Cold Gas Cleanup
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Select NOx Control
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Select Byproduct Recovery
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Set Process Parameters
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Open Vision 21 Plant Options
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Vision 21 Workbench
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Select Existing Flowsheet - 1
Water
Air
Air
GT
GT
ST
GC
HRSG
GA
ASU
FC
FC
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Select Existing Flowsheet - 2
Air
Water
PFBC
ASU
CBTC
HRSG
GTST
FCGC
GC
GT
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Configure a New System
Air
H2
CO2
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Linkage to More Detailed Process Models
Where appropriate, use a Response Surface Model (RSM) to faithfully reproduce the results of a more detailed process modelCaptures effect of key process design variablesServes as a validation tool for desktop modelsSubstantially reduces computational requirements and turnaround time
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Response Surface Model Development
Range of ParameterInputs, Ii
DetailedPerformance
Model
PerformanceOutputs (Oj)
PerformanceOutputs
(Oj)
Response SurfaceModel
Oj = f (Ii)
RegressionAnalysis
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Desktop Model of a Process
ResponseSurface
Model (RSM)
CostModel
InputAssumptions
Performance
Emissions
Cost
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Evaluation Of Desktop Model:IGCC Plant Efficiency
0.39
0.4
0.41
0.42
0.43
0.39 0.4 0.41 0.42 0.43
Actual Efficiency
Pred
icte
d Ef
ficie
ncy
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Benefits of Desktop Models
Precise and accurate representation of detailed modelsExecution takes seconds, not hoursCan run on any desktop PCAmenable to “what if” analysesIncorporates process performance, emissions, and cost models in one packageUseful by analysts and decision makers who have no time, ability or resources (staff, software, hardware, funds) to run complex models
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Model Applications
Process designTechnology evaluationCost estimationR&D management
Risk analysisEnvironmental complianceMarketing studiesStrategic planning
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Where Do We Go from Here?
Current project will implement and demonstrate:– Response surface models of several IGCC system
configurations– Process optimization capability
Further development would:– Use the Vision 21 Planner as a testbed for systems
integration development– Add preliminary versions of enabling technology models– Add process synthesis capability– Explore system dynamics modeling
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So, What Do You Think?