Transcript Addressing Multi-variable Process Control Applications

Field Experience in Property
Estimation
DeltaV Neural has been used in a variety of applications as a soft sensor for
property estimation. Also, the estimated property may be used in closed loop
control applications. In this short course we will present the features of DeltaV
Neural, some of the implemented applications, and also some of the challenges
and issues faced in developing a soft sensor. Dynamic simulation will be used to
illustrate how a property estimator may be easily created from operating data.
Presenters
• Ashish Mehta
• Lou Heavner
• Nathan Camp
Overview
•
•
•
•
•
•
•
Introduction – DeltaV APC and Soft sensors
DeltaV Neural features
Demo
Installation examples
Emerson services – Lou Heavner
Experiences with a real implementation – Nathan Camp
Q/A
DeltaV Advanced Control
What’s Different?
Classic Advanced Control
DeltaV advanced control
• Embedded in DeltaV
• State-of-the-art technology
• Expands and improves process
control tool set
• Available redundancy
• EASY to implement
• EASY to maintain
• EASY to justify
DeltaV Function Block – Foundation
Fieldbus Approach
Function Blocks
Support Mode
Function Block Inputs and Outputs
Provide an Engineering Unit Value AND Status
Standard Deviation is automatically calculated
What is a soft sensor?
A model (generally nonlinear) of a process to predict a lab
result or to fill in the gaps between sample points from an
automatic sampling sensor.
Samples
ANALYSIS
In the lab,
or automatic on a frequency
Plant
DCS
&
Historian
Results
143.0 ppm
At a fixed
Period
or
Delayed
Example - Kappa Analysis
T
T
S
T
F
F
Amp
Measurements Used In
Constructing NN
Kappa Prediction
For Outlet Stream
– Continuous Digester is
a thermo chemical
process - Time delay
of + 4 hours
– On-line measurements
of Kappa difficult inaccurate, unreliable 1 to 2 hours between
off-line feedback
analysis
Example - Model Results vs. Lab
Target Applications
 Predict critical process measurements available only
through lab analysis (paper, food properties)
 Continuous indication of measurements available only
infrequently from sampled analyzer (gas chromatograph)
 Provide real-time online predictions
 Reduce process variability, improve control
 Validate/backup sampled or continuous analyzers (mass
spectrometer, stack analyzer).
Neural Network is Built From Neurons
Non-linear
Transfer Function
Yj
1
Transfer
Function

Wj1
Wj3
Wj2
X1
X3
X2
Ij 
-1
N
W
ji
Xi
i 1
I
1 e j
Yj 
I
1 e j
Three layer feed-forward Neural net
Input
Layer
Delay to Address
Dynamics
i1
i2
Td1
Hidden
Layer
X1
W11
Output
Layer
S1
h1
X2
Td2
Xi
ii
Tdi
iN
TdN
Sj
Wij
XN
1
1
hj
y
DeltaV Neural Objectives
 Continuous indication for both: lab analysis and analyzer
based measurements
 Ease of use – integration, creation and commissioning
 NN for the process engineer, not the Neural Guru
 Adapt to process drifts and changes
 Improve maintainability and reduce cost
 ‘If-then’ analysis of process change
 Improve the bottom line, save some $$$
DeltaV Neural
– Practical means of creating
virtual sensors for
measurements that are only
available through lab analysis
today
– Easy to understand and use
– Data-based, cost effective
– General nonlinear approach
– Easy to update
Step 1a: Configure NN Function Block
Lab Analysis
References a
maximum of 20
process measurements
for analysis
Analyzer Measurement
Step 1b: Data Collection
L
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•Access data from anywhere within the system
•Automatic assignment to historian
Step 2: Data selection and screening
Step 3: Input Delays and Sensitivity
Step 3: Detail of Input Sensitivity
Step 4: Network training
Number of hidden nodes automatically determined
Step 5: Model Validation
Is the Model Good?
NN Block – Operator view
Lab Entry - Sample Value & Time
Demo - Kamyr Digester Process
Chip Bin
TT
1-7
Heating
Zone
ST
1-4
Heater
Steaming
Vessel
Heaters
TT
1-8
Cooking
Zone
TT
1-3
High Pressure
Feeder
FT
1-3
Flash Tank
Wash
Zone
FT
1-5
White
Liquor
FT
1-6
Cold Blow
IT Outlet
1-1 Device
FT
1-2
Main Blow
AY
1-2
Kappa
Analysis
Demo - Digester Kappa Prediction
On Line Error Correction
Use laboratory feedback to
bias the soft sensor to keep it accurate.
Inputs
Lab results
Soft
Sensor
Prediction
VOA estimates
should be biased
with Lab data
Statistical
Bias Correction
CV prediction
Online Operation: Adaptive NN Block
INPUTS
I
O
O
O
Feedforward
Neural Net
Model
+
OUT_SCALE
SAMPLE
FOLLOW
OUT
CORR_BIAS
o
DELAY
FUTURE
+
CORR_ENABLE
Delay
MODE
CORR_LIM
CORR_FILTER
Limit
Filter
0
Future Prediction
•
Trained Neural Network block automatically provides a predicted
output into the future - ‘FUTURE’ along with OUT.
•
Calculated by setting the input delays to zero - steady state solution
for the given input values.
•
Make immediate corrections for input changes.
•
Perform ‘what-if’ analysis.
•
Extremely valuable for processes with large delay time.
Automatic adaptation response
Lab Value
Future
NN Out
Bias Value
Changed
Simple Control with DeltaV Neural
DeltaV Neural Model output
as PV of a PID controller
APC with DeltaV Neural?
Operator
Adjustment
KF
Target
Kappa Factor
Control
Unbleached kappa
measurement
Bleach Chemical
Dosage Target
Chemical
Strength
Production
Rate
Bleach Chemical
Flow Setpoint calc.
Regulatory Controls
APC with DeltaV Neural
Inputs
Neural net
MPC
Operator target
(DEK or brightness)
KF
Target
Analyser or
Lab test
Kappa Factor
Control
Unbleached kappa
measurement
Bleach Chemical
Dosage Target
Chemical
Strength
Production
Rate
Bleach Chemical
Flow Setpoint calc.
Regulatory Controls
DeltaV Neural - Control Engineering’s 2001
Editors Choice Award
DeltaV Neural
Receives recognition for
technological
advancement, service to
the industry, and impact
on the control market.
March ’02 Issue of Control
Engineering Magazine.
Creating Virtual Sensors with neural network
technology has never been this easy!
DeltaV Neural - Control Magazine’s
Readers Choice Award
Software, Neural Network
1. Emerson's DeltaV Neural
2. Pavilion Technologies
Application: NuSoft Technologies
• Paper Machine Soft Sensors (Offline)
– Developed a model for CONCORA (strength property) on a
medium liner board machine.
– Developed a model for STFI (strength property) on a linerboard
machine.
– Developed models for brightness and opacity on a fine paper
machine.
• The objective of the effort was to compare DeltaV Neural
with other neural modeling tools. All of the applications
were from models that were existing and had been
operating for over a year. The results very closely
correlated with each other.
Application: Concora Measurement
HOLEFLOW
HD
Storage
Tank
pH
HOLE-HPDT
Hole
Refiners
62AR129
FREE255
TICKLER-HPDT
FREE355
HD
Storage
Tank
Tickler
Refiners
Stuff Box
CN219
M/c
Chest
PIC203TH
WETAGENT
IN
SLICEOPEING
2HB1-CTRL
Press
PIC901RP-SETP
CDSTMUSE
Reel
Dryer
WIRESPD
COUCHVAC
HB-LEVEL
TS-FLOW
ARTONH
BASISWT
MOISTURE
Concora
(Lab Delay)
~ 45 mins
Application: Concora Measurement
62AR129
HOLE-HPDT
Tickler-HPDT
WETAGENT
TS-FLOW
PIC203TH
2HB1-CNTRL
COUCHVAC
CDSTMUSE
BASISWT
HOLEFLOW
FREE255
FREE355
CN219
HB-LEVEL
SLICEOPENING
WIRESPD
PIC901RP-SETP
ARTONH
MOISTURE
Concora
(Online)
5 mins
Application: Concora Measurement
Application: Sasol Agri
•
•
•
•
2 Phosphoric Acid Plants
5 Evaporators on Each Plant
DeltaV/AMS/Devicenet MCC
Rosemount Hart Based Field
Application: Sasol Agri
EVAPORATOR
CONDENSOR
RULES
•
•
•
PIC1104
TI1120
Density
LIC1113
TIC1103
•
Measure SG
Control Evap SG
Controlling retention in
Evap
ACID
SG
orSTORAGE
Concentration ( 1.3
to 1.8)
PC-J3404 AM
FIC1115-1
Application: Sasol Agri
•
•
•
•
•
Density Temp
Evap Vacuum
Heater Outlet Acid Temp
Heater Acid Inlet Temp
SG Lab Entry
Application: Sasol Agri
Application: Georgia-Pacific Corp.
• Kamyr Digester Soda Loss Model (Offline)
– Developed a model for soda loss in a Kamyr digester.
• The objective of the effort was to use DeltaV Neural to
develop a model and properly identify the time delay
between the dilution factor controlled variable and soda
loss.
• Did a very good job of properly identifying the dead
time.
• Was very easy to use compared to other tools
available.
Applications: Ergon
• Refinery application – atmospheric crude column
– SR Naphtha Endpoint
– AGO Endpoint
• Refinery application – vacuum crude column
– Wax Distillate 95% point
Applications: Ergon, Atm Column
Column Temps & Yields
Predicted NA
End Point
TC
FC
Column Temps
& Yields
FC
Naphtha
FC
Kero
FC
Predicted AGO
End Point
FC
Hvy Kero
FC
FC
AGO
Crude
FC
Fuel Gas
TC
Resid to VAC
Column
Applications: Ergon, Vacuum Column
PC
FC
FC
LC
VGO
FC
Column Temps
& Yields
TC
FC
TI
Wax Dist
VAC P/A
FC
TI
Hvy Wax Dist
FC
TC
Atm Btms
FC
Fuel Gas
VAC Resid
Predicted
Wax Distillate
95% Point
More Applications
• Phosphoric Acid Concentrator
– Triple Effect Evaporator
– Predict Acid Concentration (Density)
• Lime Kiln
– Residual Carbonate
• Coffee Roaster
– Aroma (Temperature Target)
• Brewing
– Diacetyl
• Bleach Plant
– Extracted Kappa
– Brightness
Neural Applications: Hunting Tips
• What business objectives are we looking to
affect?
–
–
–
–
–
–
Quality
Throughput
Yield
Environmental
Energy
Uptime
Neural Applications: Hunting Tips
• Continuous or batch chemical processes where the
dynamic response of variables is important
• Processes that are non-linear in nature
• Processes with significant cycle times
• Key parameter dependent on upstream variables
which are measured in real-time
• Any parameter that is sampled and analyzed
• Any parameter measured online by analytical
equipment that needs validation/backup
Neural Applications: Hunting Tips
•
•
•
•
•
•
•
•
•
Specific Gravity
Composition
NOx emmissions
SOx Emmissions
Melt index
Vapor pressure
Cloud point
Pour point
Particle Size
•
•
•
•
•
•
•
•
pH
Kappa
Diacetyl
Concora
Viscosity
Octane Number
Cetane Number
Etc…
Presenters
• Ashish Mehta
• Lou Heavner
• Nathan Camp
Approach to Quality Control
•
•
•
Where Analyzers are available (and reliable) use them for
Controlled Variables ( and Disturbance Variables ).
Use intermediate measurements to estimate Quality when
Analyzers are not functioning.
Develop Virtual Sensors when Online Analyzers are not practical
Introduction to Quality Estimators
•
•
Small Process Models that provide an indication of stream Quality
from Process measurements.
Applications:
–
–
–
–
When an Analyzer is not available.
When an Analyzer is unreliable or in maintenance.
When an Analyzer response is dynamically slow due to Analyzer
sample processing time (eg, GLCs).
Process equipment between where the Quality is determined and
where the stream is available for sampling.
Purpose of Quality Estimators
•
•
To assist in operations achieving Quality Targets and Quality
Constraints using Lab Results as the feedback mechanism.
To improve the performance of closed loop Quality Control.
–
–
•
FeedBack or FeedForward Control
Model Predictive Constraint Control
To give Real Time Optimization a means to predict the Qualities
resulting from its (potential) adjustments.
Quality Estimator Formulation
•
GENERAL FORMULA ...
–
Quality = f ( Temperature, Pressure, Flow ) + Calibration Constant
–
Many Estimators are a function of pressure compensated
temperature.
• Function may be a simple constant term:
– E.g. K * ( Temperature )
–
Some estimators are complex nonlinear functions
• Functions based on first principles
• Functions based on empirical data
– Statistical techniques
– Artificial Neural Networks
Modeling & Analysis Approaches
– First principles-based models
– Statistical Approaches
– Nonlinear Regression
– Neural Networks
First Principles-based Modeling
Based on physical and chemical relationships
Examples: Kinetics, Fluid flow,
Thermodynamics
 Based on decades of experience
 Can be highly accurate when process is well
understood and relatively stable
 Requires in-depth knowledge of process
 Does not account for process behavior changes
over time
• Sometimes available through combustion unit
manufacturer

Statistical Approaches
• Techniques such as:
 Data analysis/curve fitting
 Regression techniques
 Probability analysis
• Require lots of data
• Require understanding of statistical techniques
• Better for analysis than modeling
Neural Network-based Models







Fairly new in the marketplace
Practical
Minimal process knowledge is necessary
Easy to apply to a variety of applications
Training requires good data
Easily re-trained to adapt to new conditions
Do not extrapolate well
Emerson Services
•
•
•
•
Feasibility Analysis
Feasibility Study
Project Execution
Model Support
Feasibility Analysis
•
•
•
•
Sensitivity Analysis
Existing Customer Data
No Site Visit
Outputs:
– Best model identified
– Recommendations to improve model
• Option: Benefit analysis
Offline Sensitivity Analysis
• Try DeltaV Neural on real plant data
–
–
–
–
Gather Plant Historical Data
Use all available measurements (up to 20)
Include Lab Data
Train and Verify
• Voila!
– It’s that easy…
Feasibility Study
• Site visit
– Process review
– Data collection planning
• Sensitivity Analysis
• Outputs:
– Identified model
– Implementation proposal
Project Execution
• Implement DeltaV Neural
– Feasibility study
– DeltaV Configuration
– Online model development
• Setup
• Training
• Testing
– Verification
• Short term
• Long term plan
Model Support
• Model Updating & Retraining
• Consulting
– Troubleshooting
– Accommodating process and I&C changes
– Using model in control strategies
Emerson Value Addition
• Familiarity with DeltaV Neural
• Process Expertise
• Neural Net Modeling Expertise
Leads to:
• Faster Implementation
• Lower Risk
• Appropriate Application
– Alternative approaches considered
– Taking the next step to control
Oops!
• I thought I had a good model…
– But it doesn’t look so good on new data
• I thought I had lots and lots of data…
– But the model isn’t as good as advertised
– How much data do I really need
• I thought for sure that this variable was critically
important…
– But DeltaV Neural ignored it
Practical Considerations
• Data is the key
–
–
–
–
Correct time-stamps
Raw snapshot data - no data compression
Sufficient variability
Data Density – clustering and voids
• Don’t confuse correlation and causality
Data Requirements
•
DeltaV Neural can capture dynamics…
–
but time stamps must be accurate
•
–
Time delays should be constant or compensated
Selection of time to steady-state is critical
•
Auto-correlation can lead to unusual results
Data Requirements
•
Quality of empirical data
–
–
–
–
–
Use raw (snapshot) data, avoid filtering and averaging
There must be variability and it should span the range of expected operation
Minimal Data Clustering and Data Voids
Signal to noise ratio must be high
Correlation vs. causality
Data Requirements
•
Quantity of empirical data
–
More is usually better
Data Requirements
•
Know the process
–
–
–
–
Avoid redundant information
Ensure dominant affects are incorporated
Use calculated variables (first principles based inputs)
Understand process dynamics
Common Questions
• How many samples do I need?
– Technically
• Complexity (number of inputs and time to SS vs sample interval)
• Train vs test split & verify unseen data
– Practically
• > 100 is good rule of thumb
• Why was this variable deselected?
–
–
–
–
–
–
Redundant
No variability
Too much noise
Bad measurements
Bad timestamps
Correlated w/out causality
Troubleshooting
•
Verify views
– Predicted & Actual vs Sample
• Identify trends
• Identify nature of error (bias, peak offset, etc)
– Predicted vs Actual
• Identify clustering and voids
• Identify outliers
•
Analysis w/ Excel (Pre-processing)
– Plot variables
• Vs Time
• Vs Actual
• From least to greatest
– Statistical checks
• Max, Min, Delta (span)
• Mean, Median, midpoint
• Standard Deviation & 6 Sigma
Controlling Product Quality
•
•
Direct Analyzer : product property measured by On-line Analyzer.
Inferential : product property inferred from product state or
another product property.
–
Utilizes easy to measure states or properties to infer properties
that are difficult or impossible to measure on-line.
• E.g. Temperature and pressure of vapor leaving top tray of a column
indicating composition of top product
–
Provide redundancy for online analyzers with poor
availability/reliability
Direct Analyzer Control
•
Pros of Direct Analyzer Control
–
–
–
–
•
Accuracy, good repeatability
NIR now available e.g for on-line octane
Reduces lab, work
Faster results than lab
Cons of Direct Analyzer Control
–
–
–
–
Expensive
High level of mechanical maintenance required to retain accuracy
Sample extraction
Often non-continuous read-out.
Inferential Control
•
Pros of Inferential Control
–
–
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–
•
Inexpensive - No capital cost.
Less mechanical maintenance.
Continuous read-out
Faster to implement from scratch.
Cons of Inferential Control
–
–
–
–
Models often inaccurate, particularly if non-linear.
Potentially high maintenance if no On-line Analyzer available ( i.e.
monitoring and updating of correlations )
Generally, test runs must be done to develop accurate relationships
Often limited rangeability.
Developing New Models
• Monitor Model Performance
– Trend vs Lab Analyses
• Identify if error is random or persistent
• Identify source of error
• Update Model as Required
– Correlation with New Data
• Short term variance > Adjust Bias
• Long term variance > Recalculate Correlation (New Model)
• Test New Model
– Verify Against Old Data
– Continue to Trend vs Lab Data
Presenters
• Ashish Mehta
• Lou Heavner
• Nathan Camp
Introduction
• Neural Networks
–
–
–
–
–
–
When to use them and when not to
Selecting Inputs
Data Robustness
Offline Training
Overview of SFK’s Neural Networks
Problems, Solutions, Troubleshooting, and Tools
When to use and when not to
• When not to use a Neural Network
– Process Models or Equations are already well
established
Selecting Inputs
• Use as many inputs as possible. Unimportant
inputs may be ignored.
• Inputs should not be related.
• Use calculated values instead of raw inputs if
relationships are known.
• Inputs must vary over the range in which the
Neural will be used.
• Unmeasured Disturbances can hurt.
Data Robustness
• Inputs must vary over a range. The NN output is
not valid outside the range of training.
SFK’s Neural Networks
• Two Neural Networks were required
– Extracted Kappa
– D1 Brightness
• DeltaV sits on top of Foxboro I/A
• Communications via OPC
• NNs provide feedback to MPC (Model Predictive
Control) loops.
System Architecture
Extracted Kappa NN
• Analyzer
Provides
Sample
every
15min.
• NN
Generates a
Continuous
Output for
MPC
Extracted Kappa NN
• Look at the
inputs
Extracted Kappa NN
• Evaluate the
Inputs
• Should make
sense
• Adjust the time
delays if
necessary
Extracted Kappa NN
• Train the NN
Extracted Kappa NN
• Check the validity of the predictions.
• This can be an iterative process
Error Checking and Overrides
• NN Provides
Signal to MPC
for Control
• Check for Errors
to provide
Overrides
Problems Commissioning Delig
• Initially, we could not get a good fit.
– A couple of inputs were dependent (co-linear) on
other inputs. Eliminated these inputs and replaced
with others.
– Also introduced calculated inputs where possible.
Problems Commissioning Delig
• Neural output unstable for MPC
– Due to noise from the inputs. Added extra blocks to
allow the NN inputs to be filtered separately.
Problems Commissioning Delig
• Neural Net Output went uncertain
– Major cause was inputs going outside the trained
ranges.
– Retrained Neural with larger set of data. Needed to
use PI-Datalink to pull data out and combine multiple
time periods into one file.
– Offline training with this data provided a more robust
Neural Net.
Problems Commissioning Delig
• Neural Net Output went uncertain
– Built tools to pinpoint the problem.
– Build error checking into the configuration to look for
range issues and take action if an input causes a
problem.
Model Based Control
• Sets the Kappa
Factor Target
– Injects a preset
amount of ClO2
per ton of pulp.
– Biased by
incoming
Unbleached
Kappa
– Corrected via
Model
CyberBLEACH
APC
Manual Control
KF
Target
Kappa Factor
Control
Unbleached kappa
measurement
Bleach Chemical
Dosage Target
Chemical
Strength
Production
Rate
Bleach Chemical
Flow Setpoint calc.
Regulatory Controls
Ext Kappa Results Achieved
• Reduced Variability
5.00
4.50
4.00
3.50
3.00
After APC
Before APC
2.50
Time Based View
Brightness NN
• After the learning curve on the Extracted Kappa
Neural, we were ready to attempt the Brightness
Neural.
• Several attempts were made at getting the
Neural Net to fit.
Could Not Achieve a Good Fit
• Statistical
Hint – If the
pattern looks
like a shotgun
blast, it is a
bad thing.
Problems
•
•
•
•
•
Large Variations in Dead Times.
Time Stamping of Lab Entries.
Repeatability of Lab Tests.
Data rangeability poor over training set
Unmeasured Disturbances – due to not having
input measurements for all necessary variables greatly affect the brightness .
Brightness NN Plan 2
• Develop Dynamic Estimator based on published
data.
• Modify Lab Test to provide minor biases to the
Estimator.
Trouble Shooting Tools
• Excel Spread
Sheet using both
PI Datalink and
DeltaV Excel
Addin to Pinpoint
Problems
Trouble Shooting Tools
• Process History View will give a good indication
of dynamics.
Off Line Training
• The expert mode allows sensitivity analysis from
.dat files.
• Provides capability to combine data from
multiple time frames.
• Data Manipulation can clean up noise and
unwanted disturbances.
What Lessons Were Learned?
• Careful up front design time will save a lot of
time later.
• Use care in selecting which data to use in
training the Neural Networks.
• Time Stamping is extremely important even on
slow acting processes.
• A Neural is a good tool provided prerequisites
are available.
Problems and Solutions
• Neural Network may need different filtering than
other processes
– Use Second Input (AI or Pseudo AI) to provide
secondary filtering.
• Output will be invalid outside the trained range
– Check valid ranges and program error handling
Problems and Solutions
• Historian does not hold enough information to
cover full sets of inputs.
– Increase Historian Archive capabilities by increasing
the number of archives and/or size of archives
– Use PI Datalink or other tools to save data into Excel
spreadsheets. Combine data and use off line training
Summary
• Neural Networks are a very powerful tool.
• The Extracted Kappa Neural Net and associated
MPC provide a good solution for our customer.
• The Brightness Neural Net attempt shows that
the NN is not a magic solution for all cases. In
this case, the addition of instrumentation would
have allowed the Neural to work.
• Questions???
Presenters
• Ashish Mehta
• Lou Heavner
• Nathan Camp
DeltaV Neural – preview into future
• Data pre-processing tools:
–
–
–
–
Statistical info like mean, std. deviation for data sets
Input filtering
Calculations/transforms (e.g., log, exp) on inputs
Improved metrics for sorting data into test/train segments
• Improve input time delay and correlation analysis –
use expert user inputs
• Training Limit handling:
–
–
–
–
Allow user entry
Indicate outliers and limits
Online operation should indicate violated variable
Applicable limits shown during online
DeltaV Neural – preview into future
• Adding new data set for retraining, both graphical and
file data
• Indication of sensitivity after training a model
• Residual analysis: graphical, statistical
• Output filtering - essential when used in control
• Allow DELAY value of up to 72 hours, currently
limited to TSS (max. 24 hours)
• Clearer indication for Batch processes
– end of batch quality prediction
– prediction of end of batch time
• Enhance ease of use
DeltaV APC and TDC – Using OPC
DeltaV Workstation
OPC server
on AMNT
With OPC Server
OPC
I/F
Operator Station
(US or GUS)
IOP Modules
Highway
Gateway
Controller
PM APM
HPPM
Serial I/F Options
FTA
DeltaV
Controller
DeltaV APC and Provox
DeltaV Workstation
OPC server
on Chip
Any Provox
Operator Console
With OPC Server
OPC
I/F
DeltaV
Controller
Provox
Controller
Serial I/F Options
IDI
Intelligent
Device
Interface
Summary
• The capability of DeltaV Neural as an effective
soft sensor has been demonstrated
• Application examples / advanced features
• Value addition by Emerson solutions group
• Real-world challenges and improvements
• Further information:
– [email protected]
– [email protected]
– [email protected]
DeltaV Neural and other DeltaV Advanced Control Products
Overview - Courses 7201, 7202, & 7203
These courses, beginning with the 7201, overview all of the major DeltaV
advanced control tools. Courses 7202, & 7203 each drill deeper into a specific
advanced control product and its application.
•
• DeltaV advanced controls are unique in the process control industry, in that
users do not need detailed knowledge of the underlying mathematical
principles to successfully apply the DeltaV advanced controls technology.
Course # 7201
DeltaV Advanced Controls
Overview
Course # 7202
DeltaV PredictPro
Implementation
Course # 7203
DeltaV Neural
Implementation
Learning More About DeltaV Advanced
Control
• Book was inspired by DeltaV
Advanced Control Products. This
book was introduced at ISA2002
may also be ordered through ISA,
Amazon.com or at
EasyDeltaV.com/Bookstore
• The application sections include
guided tours based on DeltaV
Advanced Control Products
• CD provides an overview video for
each section and examples. Copies
of the displays, modules, and
HYSYS Cases are included on the
CD.