john hedengren brigham young university university of utah
TRANSCRIPT
John HedengrenBrigham Young University
University of Utah Graduate Seminar
30 Oct 2013
30 Oct 2013
PRISM Group Overview
Monitoring / Intelli-Fields Unmanned Aerial Vehicles Oil and Gas Exploration and Production
Uncertainty Analysis Investment Planning Under Uncertainty Energy Storage and the Smart Grid
Next Generation Simulation with Optimization Solid Oxide Fuel Cells Systems Biology
2
OverviewOverview
30 Oct 2013
PRISM Group OverviewPRISM Group Overview
PRISM: Process Research and Intelligent Systems Modeling
Methods Mixed Integer Nonlinear Programming (MINLP) Dynamic Planning and Optimization Uncertain, Forecasted, Complex Systems
Fit Systems into Standard Problem Formulation
Solver development: Large-scale MINLP (100,000+ variables)
3
max
subjectto , , , 0
h , , 0
30 Oct 2013
PRISM Group Overview
Monitoring / Intelli-Fields Unmanned Aerial Vehicles Oil and Gas Exploration and Production
Uncertainty Analysis Investment Planning Under Uncertainty Energy Storage and the Smart Grid
Next Generation Simulation with Optimization Solid Oxide Fuel Cells Systems Biology
4
OverviewOverview
30 Oct 2013
UAV PlatformUAV Platform
30 Oct 2013
Monitor InfrastructureMonitor Infrastructure
30 Oct 2013
Canal – Change DetectionCanal – Change Detection
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Dynamic Optimization with UAVsDynamic Optimization with UAVs
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2
3
8
Courtesy Sentix Corp
30 Oct 2013
Information SourcesInformation Sources
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Controlled Airspace
Population Density
Aircraft Performance Model
Geospatial / Urban Dev Air Traffic Density
Define Risk
Compute Risk
Minimize RiskWeather
Airspace + Pop DensityAirspace + Pop Density
Multiple Sources of Information Can Be Utilized
Safety Thresholds
Functions
Courtesy Sentix Corp
30 Oct 2013
Failures to Monitor and PredictFailures to Monitor and Predict
Detect early warning signs
Automate monitoring of critical systems
Give critical data to key decision makers
30 Oct 2013
First use of fiber optic sensors on a subsea pipeline to monitor pressure, strain and vibration in external casing pipe bundle during fabrication.
Bullwinkle Platform
Troika - Gulf of Mexico
30 Oct 2013
Fiber Bragg GratingsFiber Bragg Gratings
30 Oct 2013
Tendon Tension MonitoringTendon Tension Monitoring
30 Oct 2013
Adhesion Testing for Subsea InstallationAdhesion Testing for Subsea Installation
FEA Test Article Pipe for Tension TestingAvoid Local Areas of Inelastic Deformation (>50 ksi)
30 Oct 2013
Uniform Loading in 4 Point BendingUniform Loading in 4 Point Bending
0 10 20 30 40 50 60 70-2000
0
2000
0 10 20 30 40 50 60 70-2000
0
2000
0 10 20 30 40 50 60 70-2000
0
2000
0 10 20 30 40 50 60 70-2000
0
2000
Time (minutes)
Conventional Strain GaugeFiber Optic Strain Gauge
30 Oct 2013
Tendon Band PreparationTendon Band Preparation
Cleaning with a Water Jet Polishing to Bare Metal
Marine Growth (Before) Clean Band (After)
30 Oct 2013
Diver InstallationDiver Installation
Diver with Clamp Riser Preparation
Clamp Installation Clamp Inspection
30 Oct 2013
Drilling and ProductionDrilling and Production
IntelliServ, 2013
30 Oct 2013
PRISM Group Overview
Monitoring / Intelli-Fields Unmanned Aerial Vehicles Oil and Gas Exploration and Production
Uncertainty Analysis Investment Planning Under Uncertainty Energy Storage and the Smart Grid
Next Generation Simulation with Optimization Solid Oxide Fuel Cells Systems Biology
19
OverviewOverview
Smart Grid Optimization
Smart grid integration with solar, wind, coal, biomass, natural gas, and energy storage
Nuclear integration withpetrochemicalproduction, processing, and distribution
District Heating and Cooling District Heating and Cooling
Uncertainty in Natural Gas PricesUncertainty in Natural Gas Prices
Uncertainty in Electricity PricesUncertainty in Electricity Prices
Dynamic Model for Dynamic SystemDynamic Model for Dynamic SystemH
eat
Dem
and
(MM
BTU
/hr)
Electricity Dem
and (MW
)
MW
MW
MW
Simplifying SystemSimplifying System
Create Model: Electric and Heating Demand Model (winter and summer)
Model Predictive Control ApproachModel Predictive Control Approach
Optimize to a Target RangeOptimize to a Target Range
0 2 4 6 8 10 12 14 16 18 201
2
3
4
5
Man
ipul
ated
uopt
0 2 4 6 8 10 12 14 16 18 200
5
10
15
Con
trolle
d
xopt
Optimal sequence of moves given uncertainty in the parameters
Distribution of Controlled Variables
0 2 4 6 8 10 12 14 16 18 201
2
3
4
Man
ipul
ated
uopt
0 2 4 6 8 10 12 14 16 18 200
5
10
15
Con
trolle
d
xopt
Optimize to a LimitOptimize to a Limit
Conservative movement based on worst case CV
Upper Limit
PRISM Group Overview
Monitoring / Intelli-Fields Unmanned Aerial Vehicles Oil and Gas Exploration and Production
Uncertainty Analysis Investment Planning Under Uncertainty Energy Storage and the Smart Grid
Next Generation Simulation with Optimization Solid Oxide Fuel Cells Systems Biology
29
OverviewOverview
Systems BiologySystems Biology
Objective: Improve extraction of information from clinical trial data
Dynamic data reconciliation Dynamic pharmacokinetic models (large-scale) Data sets over many patients (distributed) Uncertain parameters (stochastic)
05
1015
1.9
1.95
21
2
3
4
5
6
7
Log(
Viru
s)
HIV Virus Simulation
time (years)Log(kr1)
1.828
2.364
2.899
3.435
3.970
4.506
5.041
5.576
6.112
6.647
0 5 10 151
2
3
4
5
6
7
8
Time
log1
0 vi
rus
Dynamic Energy System Tools
Solid Oxide Fuel Cell (SOFC)
Toolbox for Object Oriented Modeling in MATLAB, Simulink, and Python
Advanced tools are required for collaborative modeling and high performance computing
Optimization BenchmarkOptimization Benchmark
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 510
20
30
40
50
60
70
80
90
100
Not worse than 2 times slower than the best solver ()
Per
cent
age
(%)
APOPT+BPOPTAPOPT1.0
BPOPT1.0IPOPT3.10
IPOPT2.3
SNOPT6.1
MINOS5.5
Summary of 494 Benchmark Problems
Speed
Success
Speed and Successwith combined approach
Survey of DAE SolversSurvey of DAE Solvers
Software Package Max DAE Index
Form Adaptive Time Step
Sparse Partial‐DAEs
Simultaneous Estimation / Optimization
APMonitor 3+ Open No Yes Yes Yes
DASPK / CVODE / Jacobian
2 Open Yes No No No
gProms 1 (3+ with transforms)
Open Yes Yes Yes No
MATLAB 1 Semi‐explicit
Yes No No No
Modelica 1 Open Yes Yes No No
DAE = Differential and Algebraic Equation
ConclusionsConclusions
Powerful insights can be gained from modeling and data reconciliation over long periods of historical data
When data, modeling, and optimization are combined, hidden savings are discovered through dynamic optimization
Simulation and optimization can give realistic options to evaluate risks and rewards
Simulation results can then be directly applied in practice to continuously monitor and optimize
AcknowledgmentsAcknowledgments
Extra SlidesExtra Slides