PSE 480 A Few Notes On Paper Machine Process Modeling

There are a many things to consider when performing process testing for modeling. We will only look at a limited scope here as it pertains to the simulated paper machine problem. OSI PI Software Packages You will be required to become familiar with two software programs. The programs are made by OSI and they are PI ProcessBook and PI Datalink. These programs allow you to manipulate and access process data in the OSI PI database to use in the modeling and control exercises you will be performing on your paper machine. Many of you may already be familiar with these packages as PI is a very common mill wide information system used in the pulp and paper industry. Computer Based Training PICBT can be found in the references section on the class website. There are two sections you are required to become familiar with. They are “Section 3. Using Run Mode” and “Section 12. PI-DataLink”. These files have sound so depending on the computer you are using you may need a set of head phones to listen to the audio portion. You will find ProcessBook in the start menu under PI System on the computers in Bloedel 261 and 156. PI DataLink is also on these computers. You may need to configure this Excel add-in. If you do not see a PI menu when you open Excel go to Tools\Add-Ins….When the dialog box opens select Browse and go to “C:\Program Files\PIPC\Excel\pipc32.xll” and select OK. Go to the PI menu and select Settings… You will want to select “In a row” and “Connect to PI server on startup”. Basic Process Modeling The basic idea is to use bump tests or PRBS (Pseudo Random Binary Sequence) input of the manipulated variables to generate process output data for process modeling. To model the process all the variables need to be sampled at a regular interval. This is usually determined by the availability of output measurements. Additionally all the variables need to be sampled at essentially the same moment in time. This provides a snapshot or discrete representation of the process at an instance in time. Implications of not sampling data at the same time for model development will be discussed later. Ideally you start from a steady state condition where everything is stationary (variables aren’t drifting over time) and bump one of the input variables while holding the other input variables constant and measure its affect on all output variables (for the paper machine problem we only have one output). This can be difficult in a mill situation. If the process variables are relatively stationary we can get by with the methods described in the text without resorting to more advanced process identification techniques.

Often there is noise in the process variables due to unmeasured disturbances and sensor noise and drift. The magnitude of the input perturbations must be large enough so you can differentiate between the noise and the actual effect of the manipulated variables on the process outputs. This is also referred to as a high signal to noise ratio. This needs to be balanced with maintaining the process within its target operating range. For the paper machine you should keep the basis weight within of ? 4 lb/ream of its target value. A PRBS testing pattern can give adequate data for process modeling at a lower signal to noise ratio than simple bump tests. However, the model identification step for this type of input data is more advanced then what is presented in the text. A modeling issue that is site specific is data compression routines. The process data you collect will come from some type of data historian. These typically have data compression algorithms to reduce redundancy in data collection. For process testing it is usually best to turn off any compression algorithms so you can collect all the data being transmitted by various input and output signals. Any necessary data conditioning can be done after the data collection step. Paper Machine Modeling The steps for modeling the paper machine simulation are ? Use PI ProcessBook to view and manipulate paper machine variables ? Perform visual inspection to see if process appears to be first order gain- lag-delay ? Retrieve data for analysis using PI Datalink ? Use Excel for process modeling and controller design It was stated previously that you want to sample all of the variables at exactly the same time. As an example let’s look at a bump test of the thick stock flow. In an ideal process at the exact moment when the basis weight sensor is taking a sample you would change the set point of the thick stock flow and the thick stock flow would immediately go to the set point value. The real process behaves somewhat differently. The paper machine basis weight sensor sample interval can be specified, but its default sampling rate is 10 seconds. This is the sampling rate you are to use unless told otherwise. Let’s assume the thick stock flow can be manipulated at any instance in time and the thick stock flow valve dynamics are very fast compared to the process dynamics. If the thick stock flow is arbitrarily bumped, at most it will be off five seconds of a basis weight sensor sampling interval (half the sampling interval). If the process lag and the delay (this comes later) are large compared to this offset it will have a minimal effect on the process model with respect to the process time constant or delay. For example if the time constant for this process were 90 seconds with no delay this would be a small factor. Alternately if the basis weight sampling interval were 30 seconds the offset could be as much as 15 seconds. This now can have a significant impact on process modeling

especially if there is a delay in the process of the same order. In this case we need to be “smarter” in how the thick stock flow is manipulated to get accurate process data. The rest of this paper shows how to set up a spreadsheet to collect the necessary process data you will need for process modeling, control, and validation. A model identification exercise is provided as an example.

Data Collection And Modeling At a minimum you need to familiarize yourself with sections 1, 2, 3, and 12. You will find ProcessBook in the start menu under PI System on the computers in Bloedel 261 and 156. PI DataLink is also on these computers. You may need to configure this Excel add-in. If you do not see a PI menu when you open Excel go to Tools\Add-Ins…. When the dialog box opens select Browse and go to “C:\Program Files\PIPC\Excel\pipc32.xll” and select OK. Go to the PI menu and select Settings… You will want to select “In a row” and “Connect to PI server on startup”. Process Data Using PI Datalink Each student will be assigned a paper machine to perform process testing and develop control strategies. The tags you will need for this are the following where # designates your papermachine number. If you have a papermachine that is less than 10 then you need to place a zero in front of your machine number.

Tag Description PMOPC:#:bw.pv Basis weight process value PMOPC:#:bw.sp Basis weight setpoint PMOPC:#:tsf.pv Thick stock flow process value PMOPC:#:tsf.sp Thick stock flow setpoint PMOPC:#:cons.pv Thick stock consistency process value PMOPC:#:cons.sp Thick stock consistency setpoint PMOPC:#:speed.pv Machine speed process value PMOPC:#:speed.sp Machine speed setpoint PMOPC:#:ctrl.ds Controller mode PMOPC:#:ctrl.gn Controller gain PMOPC:#:ctrl.ti Controller integral time PMOPC:#:ctrl.sf Basis weight sampling frequency

Set up an Excel spreadsheet that looks like the following that includes all the tags listed above. You can either type in the tag names manually or you can use the tag search feature in the PI menu.

Next include the descriptors for all of the tags. This is done using Tag attributes… under the PI menu. It should look something like the image below.

One thing you might want to do is trim the extra characters that the tag descriptor function returns. To do this go into the formula and use the trim function. As an example the formula in cell D2 would be

=TRIM(PITagAtt(D$1,"descriptor",)) Finally we are going to collect timed data for the process variables. Since the sampling frequency of the basis weight is relatively fast compared to the process dynamics we won’t need to be worried about the exact times the basis weight sensor is sampling. One of the limiting factors in modeling and control can be the availability of the controlled variable. Typically manipulated and disturbance variables are available at a much higher frequency than the controlled variable. From the PI menu select Sampled Data… Fill out the box so it looks like the one below. Make sure you save this spreadsheet as you will be using this data collection template throughout the course

Now that you have a spreadsheet set up to retrieve process data from PI the next step is

to use PI ProcessBook to perform bump tests on the paper machine for process modeling. Process Testing Using PI ProcessBook Each student is assigned his or her own paper machine. The ProcessBook files used for the paper machine can be found under in a zip file on the web. Open your designated paper machine and open the Control Center display. It should look something like the following:

Here we can observe the process and set point values for the basis weight, thick stock flow, thick stock consistency, and paper machine speed. By entering values for the set points and selecting the Send Set Point button you can perform bump tests on the process. The following is an example of developing a discrete gain lag model for the basis weight using the thick stock flow as the process input. The following figure shows the results of a step input of +100 gpm in the thick stock flow.

From the output response this looks like a first order gain lag process. The next step is to retrieve the data from PI using the spreadsheet created above. First we find the time when the thick stock flow was bumped. This is time zero for the model.

From the plot of the deviation of the basis weight we see that it lines out around 2.75.

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We can now calculate the gain.

gpmlb/ream 028.0/100/75.2/ ???? TSFBWTSFBW Now let’s find the time constant. First we calculate what the value of the basis weight should be at one and two time constants.

37.2)75.2)(86.0()2(73.1)75.2)(63.0()(

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BWBW

From the data we see that at 130 seconds the basis weight reached approximately 1.73 lb/ream. If the process is a first order gain lag process then at 260 seconds the process should reach 2.37.

If we go purely by the value of the data the process doesn’t reach 2.37 lb/ream until 300 seconds. This would suggest the process isn’t a first order gain lag process. However, there appears to be some noise in the data. If you kind of eyeball the path of the basis weight it looks like it would come to the 2.37 lb/ream in about 260 seconds if it weren’t for the process noise. So we will select a time constant of 130 seconds for the process. You can now create a gain lag model of the process and plot it against the process data to see how well it matches the process.

The model we are trying to fit to the process is a first order discrete gain lag model of the form

)(*)1(*)(*)( tTSFAKtBWATtBW ????

The figure below shows one way you could set up a gain lag model using Excel.

The plot below shows a reasonable fit to the process. The gain looks good, but the time constant seems a little too small since the model rises faster than the process.

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process model The next plot shows the model with a time constant of 140 seconds. This fit appears to be a little better. Note that this is only one bump test. Several bump tests of the thick stock flow would be required to develop a representative model. To validate the model you would perform several bump tests to get the model parameters and check the model against data that wasn’t used in the model development stage.

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