Process Chemometrics in the Dow Chemical company. Zdravko Stefanov and Leo Chiang Analytical Technology Center The Dow Chemical Company

Transcription

1 Process Chemometrics in the Dow Chemical company Zdravko Stefanov and Leo Chiang Analytical Technology Center The Dow Chemical Company 1

2 Outline Who we are and how we approach problems Process chemometrics for batch processes Process chemometrics for continuous processes Online multivariate monitoring Summary 2

3 Who we are and 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 3

4 Process chemometrics for batch processes example 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 4

5 Process chemometrics for batch processes example 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 5

6 YVar(%CI (< 0.4%)) Process chemometrics for batch processes example y=1*x-3.893e-008 R2= Number of unfolded variables Number of principal components R 2 X R 2 Y Q 2 Y Important variables R2 agitator amps during step 34 R2 weight during step YPred[2](%CI (< 0.4%)) SIMCA-P :28:44 (UTC-6) 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. 6

7 Process chemometrics for batch processes example 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) 7

8 Process chemometrics for batch processes example 2 The approach batch-unfolded PLS X process data from all steps Y the reaction extent The two reactors are modeled separately Y 8

9 Process chemometrics for batch processes example 2 Calibration Validation 9

10 Process chemometrics for batch processes example 2 Step 214 Step 214 Step 214 Step 221 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 10

11 Process chemometrics for continuous 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? 11

12 Process chemometrics for continuous 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 Every 3 hr Result Every minute Process inputs (Temp, Pressure) Inferential Sensor; y = f(x) 12

13 Process chemometrics for continuous processes Measured quality Predicted quality 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 13

14 Process chemometrics for continuous processes Specification = 10 ppm Trigger limit = 6.8 ppm Measured quality Predicted quality Good online performance We have implemented a real-time diagnostics tool (i.e., T2 and Q statistics) to monitor model performance 14

15 Process chemometrics for continuous processes Statistical Quality Control Operating Discipline 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 15

16 Process chemometrics for continuous processes Summary 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. Now, both the plant and the customer are happy! 16

17 Multivariate 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 17

18 Multivariate 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 18

19 Multivariate 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 19

20 Multivariate monitoring The T2 and DModX control charts 20

21 Multivariate monitoring If a point in the control charts is clicked, a contribution chart will appear 21

22 Multivariate monitoring If a bar on the contribution chart is clicked, it will plot a trend of the actual variable 22

23 Summary 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. 23

24 Questions Thank you for your attention. Questions? 24