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Mill Wide Information Systems In a mill there are typically many independent DCS’s, PLC’s, Data Historians, etc. Several companies offer mill wide data systems (PI, DataParc,CIM21). Mill wide information systems combine data from various sources in one central location and provide tools for accessing and utilizing data
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PI System Set of software modules for plant-wide monitoring and analysis. The data archive is the foundation of the system. It handles the collection, storage, and retrieval of time oriented numerical and string data. It also acts as a data server for Microsoft Windows-based client applications.
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PI Distributed Data Collection PINet PINet node NT or UNIX PI Base Package Windows NT or UNIX PIonPINet node Home node VMS * * * Windows 3.1, 95, or NT Client node Interface node PI-DL API Profile API PI-PB API Batch API PI-CM API PI-API Virtual Paper Machine * Sources of data, such as DCS’s, PLC’s, lab systems, process models, etc.
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Data Storage Memory wasn’t always cheap. Algorithms were devised for data storage. Enough data is stored to reconstruct original data. Amount of data compression can be specified as required.
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Data Compression
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Process Testing Bump tests or PRBS (pseudo random binary steps) are used to generate process data for process modeling. Idea is to start at steady-state and bump one input variable while holding the others constant and measure its effect on all output variables. Can be difficult in mill situation.
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Process Testing Bumps must be large enough to differentiate effects on output from noise in output measurement. They should be in the typical operating range of the process. You should take samples at a regular interval. This is usually determined by availability of output measurement.
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Paper Machine Testing and Modeling Use PI Process Book to view and manipulate papermachine. Retrieve data for analysis using PI Datalink which is an Excel Add-In. Rely heavily on Excel for modeling and control analysis
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Paper Machine Testing and Modeling The sensor for the basis weight gives a measurement every 10 seconds. Collect sampled data for all the process variables at 10 seconds intervals. The sampling interval is small compared to our process dynamics we don’t need to bump the process and collect data at the exact time the basis weight sensor reports a data point.
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Data Synchronization BW sensor sample interval of 10 s – Maximum out of sync by 5 s
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Data Synchronization BW sensor sample interval of 30 s – Maximum out of sync by 15 s Fast Dynamics Slow dynamics
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Sampled Data…
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Sample Bump Test Sampling interval of 10s T=10 seconds
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Discrete Gain Lag Model The process looks like a linear first order response so lets model it with a discrete gain lag model. We know that for a first order discrete gain lag model X(t + T) = A*X(t) + K*(1-A)*U(t) or X(t) = A*X(t-T) + K*(1-A)*U(t-T) Where X is the output, U is the input, A is the lag factor, and K is the process gain (i.e. X/ U). Sometimes you will see B in place of K(1-A). We can calculate A by the formula A = exp(-T/
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Data Analysis A = exp(-T/ ) A = exp(-10/60) A = 0.85 B = K*(1-A) B = 0.5*(1-0.85) B = 0.075 X(t+10) = A*X(t) + B*U(t) X(t+10) = 0.85*X(t) + 0.075*U(t)
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Simulated Step Response
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