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1
Process Chemometrics in the
Dow Chemical company
Zdravko Stefanov and Leo Chiang
Analytical Technology Center
The Dow Chemical Company
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OutlineOutline
• Who we are and how we approach
problems
• Process chemometrics for batch
processes
• Process chemometrics for continuous
processes
• Online multivariate monitoring
• Summary
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Who we areWho we are
and problem approachand problem approach
• The chemometrics group:
Ivan Castillo, Leo Chiang, Swee-Teng Chin, Kedar Dave, Eldad Herceg, Bryant LaFreniere,
Randy Pell, Mary Beth Seasholtz, Zdravko (Z) Stefanov
• We are part of the Analytical Technology Center at Dow
• We are serious contributors to a fundamental approach to solve plant problems based on hypothesis testing and hard data
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Process chemometrics forProcess chemometrics for
batch processes batch processes –– example 1example 1
• High Impurity Level: Big Problem!
• One week to fix the problem before loss of the entire campaign
• Multimillion USD of profit margin at risk
• Process
• System of 2 batch reactors in series
• Reactor sequences are multistep with complicated solid/2 liquid phases chemistry
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Process chemometrics forProcess chemometrics for
batch processes batch processes –– example 1example 1
• The approach – batch-unfolded PLS
• X – process data from all steps
• Y – the impurity concentration for each batch
• R1 and R2 data were combined into one matrix
Y
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Process chemometrics forProcess chemometrics for
batch processes batch processes –– example 1example 1
Number of
unfolded
variables
Number of principal
components
R2X R2Y Q2Y
77 2 0.733 0.824 0.746
0.0
1.0
2.0
3.0
4.0
-0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5
YV
ar(
%C
I (<
0.4
%))
YPred[2](%CI (< 0.4%))
y=1*x-3.893e-008R2=0.8236
SIMCA-P+ 12.0.1 - 2010-09-12 16:28:44 (UTC-6)
• Important variables • R2 agitator amps during
step 34 • R2 weight during step 34
• The first two variables reflect the amount of work put by the agitator in R2, respectively the mixing quality. • The batches were made “skinnier”, i.e. the reactant amounts
were reduced by 10% compared to the original recipe. This was done in order to improve the mixing.
• After these changes were implemented, the plant resumed operation and all consecutive batches of the product were produced within specification.
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Process chemometrics forProcess chemometrics for
batch processes batch processes –– example 2example 2
• Problem with too many batches out of specification
• Poor control of reaction extent
• The reactors are not trajectory controlled, but recipe controlled
• Large variability in quality
• Two trains in parallel, experience similar problem
• The process is complex fed-batch with two stages and multiple steps (35 in two stages)
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Process chemometrics forProcess chemometrics for
batch processes batch processes –– example 2example 2
• The approach – batch-unfolded PLS
• X – process data from all steps
• Y – the reaction extent
• The two reactors are modeled separately
Y
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Process chemometrics forProcess chemometrics for
batch processes batch processes –– example 2example 2
Calibration Validation
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Process chemometrics forProcess chemometrics for
batch processes batch processes –– example 2example 2
• High (red) and low (blue) reaction extent batches
• Mass and energy balance related variables show large differences
• Significant oscillations are also observed
• Control system changes were implemented
• Quality was improved to the effect of multimillion USD
Step 214 Step 214
Step 214 Step 221
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Process chemometrics forProcess chemometrics for
continuous processescontinuous processes
• For a continuous plant, a product stream was sampled every 3 hours for product quality control purpose
• The plant proposed to reduce sample frequency to once every 6 hours (i.e. grab 4 samples per day) to reduce cost and minimize safety risk
• However their customer rejected this proposal because of higher quality risk. A single event of accepting offgrade material (i.e., type-II error) can lead to a huge loss.
• Question: Can we come up with a solution that will satisfy both the plant and the customer?
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Process chemometrics forProcess chemometrics for
continuous processescontinuous processes
• Solution – inferential sensor • Implement inferential sensor as a conditional sampling
solution • If key process conditions (e.g., temperature, reflux flow,
feed flow, etc.) predict that product quality will be unusual (or out of spec), then we can sample more often (i.e., every 3 hr) to confirm
Process inputs
(Temp, Pressure)
Inferential Sensor;
y = f(x)
Every 3 hr
Every minute
Result
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Process chemometrics forProcess chemometrics for
continuous processescontinuous processes
• Training data contain 60 variables for 3 months
• PLS model uses 10 variables
• Model quality is good (R2 = 0.93; RMSE error = 0.74 ppm)
• The model is implemented on-line
Measured quality
Predicted quality
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Process chemometrics forProcess chemometrics for
continuous processescontinuous processes
• Good online performance
• We have implemented a real-time diagnostics tool (i.e., T2 and Q statistics) to monitor model performance
Specification = 10 ppm
Trigger limit = 6.8 ppm
Measured quality
Predicted quality
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Process chemometrics forProcess chemometrics for
continuous processescontinuous processes
• If predicted quality > 6.8 ppm, this triggers use of Critical Quality Control procedure.
• Critical procedure calls for increased sampling frequency (3 hours between samples).
• This continues until 2 consecutive results are within control limits.
• If predicted quality > 8.5 ppm, the plant will follow the above critical procedure and also divert product to a offgrade tank
Statistical Quality Control
Operating Discipline
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Process chemometrics forProcess chemometrics for
continuous processescontinuous processes
• An inferential model was implemented in real time since April 2011 and proved to be accurate and effective.
• The customer approves a new Operating Discipline that uses model prediction to infer sampling frequency.
• The plant now samples at a rate of every 6 hours unless the model prediction is above the critical limit.
• This is the first ever inferential sensor application for sample size optimization in Dow.
Summary
Now, both the plant and the customer are happy!
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Multivariate monitoringMultivariate monitoring
• The plant from batch example 1 loved the technology!
• So we installed online multivariate monitoring software on R1 and R2
• Caught a multitude of other issues not known before, such as
• Catalyst tank failed to stir before loading due to control system limit being set wrong
• Vibrations from a steam tracing line distorting a weigh cell and therefore the recipe was not loaded properly
• And others
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Multivariate monitoringMultivariate monitoring
• The classic multivariate analysis has proven to be very useful for troubleshooting of plant problems, but • It is reactive – it is applied only after the problems
are well developed • Leads to missed opportunity for even more
savings • Online multivariate analysis / monitoring / fault
detection is real time • It is proactive • Will detect the onset of the problems early before
they lead to large losses • We are looking for a commercial vendor but until we
find it • In-house tool based on Excel now can perform online
monitoring for batch and continuous processes
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Multivariate monitoringMultivariate monitoring
• Supported features
• Batch and continuous processes
• Multiple products/recipes/operating conditions
• Automatic/manual model selection
• Aspen IP.21 and legacy DOW GPI DAP data sources
• The user can save the configuration for easy reloading
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Multivariate monitoringMultivariate monitoring
The T2 and DModX control charts
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Multivariate monitoringMultivariate monitoring
If a point in the control charts is clicked, a contribution
chart will appear
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Multivariate monitoringMultivariate monitoring
If a bar on the contribution chart is clicked,
it will plot a trend of the actual variable
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SummarySummary
• Process chemometrics has been very successful at Dow Chemical
• The process chemometrics group is steadily growing, however
• Recent searches for European hire were not successful
• We are looking to collaborate with universities to help develop students that can be successful chemometricians at Dow.
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QuestionsQuestions
Thank you for your attention.
Questions?
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