Model Identification Process:

Transcription

1 Model Identification Process: from data collection to calibrated, empirical, dynamic, linear models Antonio Marchionna, Consultant/Engineer 7 th July 2017

2 Contents Introduction: the identification problem and APC workflow Data collection Data analysis and conditioning Running identification cases Model quality evaluation 2

3 Linear Dynamic Model: general form and identification problem d CV = A * DI Calculate Known Known 3

4 APC Workflow with DMC3 Builder Single environment Create a new project / Open existing project Import dataset Identify Model Controller deployment Configuration 4

5 How does APC make money? Specification or Hi limit 1. Suppress (minimize) process variances enabling the pushing of constraints D X Average Average Number Of Samples On Control Distribution Off Control Distribution F( Z C ) F( Z D ) Current Operation Variations Reduced with Advanced Control Move Average Closer to Specification or Limit Quality/Target X D X L X C 2. Controller maximises profit by moving plant to optimal set of plant constraints Increase operational stability, reliability and safety Maximize throughput & yield of process units 3-5% Reduce energy consumption by 5-10% 5

6 Data Collection (1) data requirements Data type Miscellaneous data points (measurements, signals, etc ) PID (PV, SP and OP) Sample frequency 1 minute 30 sec 15 sec in fast scan (option available in AW) 6

7 Data Collection (2) collectors Collect DOS based No interface Output: *.clc file InfoPlus 21 and AspenWatch Structured database (SQL) User friendly interface Output: *.csv, *.vec, etc Advanced tools for data analysis 7

8 Plant test: dataset production Manual step test: Necessary at the beginning Time consuming Automatic step test: Smart step: step test within all the feasible control region Calibrate mode: step test close to the optimal point (half testing half optimizing) 8

9 Dataset Workflow Select tags to import Auto-interpolate Import Data Review Data Establish good regions Slice bad data Segregate vector tags Add Transforms Build Calculations Customize Dataset Supported data formats:.clc,.txt,.apcdataset,.vec,.dpv 9

10 Importing a Dataset (1) Use the Datasets View Tools Ribbon to import dataset Dataset: CLC, TXT, APCDATASET Vectors: VEC, DPV 10

11 Reviewing Dataset List of Datasets List of Vectors within selected Dataset Plot Window 11

12 Slicing Options (1) Data to slice: Communication failures Valve saturation Missing communication Interpolation Instrument failures Inconsistent PID performance (spikes, saturation, etc.) Unmeasured process disturbances Closed loop feedback 12

13 Slicing Options (2) Bad Slice Global Slice List Add/Remove Slice Slice List Bad Slice Vector List 13

14 Data conditioning Data make-up : Descrete to continuous data functions Filters Shift Calculation Transforms 14

15 Advanced Data Conditioning tools: Smart Slice (1) Smart Slice in DMC3 Builder is similar to Auto-Slicing online Optionally select a PID scheme to allow the autoslicing analysis to look for PID saturation Bad slices are marked as case slices along with an explanation Option to convert case slices to dataset slices 15

16 Advanced Data Conditioning tools: Transformation (2) Transform can be done in the Case editor 16

17 Model Identification: building the master model (1) Model Properties (TTSS, Execution Frequency) Build Controller Structure (Independents & Dependents ) 17

18 Model Identification: software structure (2) Master Model Models Tool Ribbon ID Cases Master Model Structure 18

19 Model Identification: building ID case (3) Include all Independent variables that impact Dependent variables in the case Indicate Ramp variables Perform any case specific transformations or vector calculations Model will use the specified slicing to select: Only smallest sub-set of good data is used Data that is good for all variables in the case Identification Options: Finite Impulse Response (Traditional DMC) MISO Subspace Identification MIMO 19

20 Model Identification: building ID case (4) 20

21 Constrained Identification (1) DMC3 Builder allows performing constrained model identification when working with subspace identification package (only). Gain constraints can be entered in three different formats- Gain constraints: Incorporates the specified (fixed) gain values Gain ratios: Constrained model ID incorporating specified gain ratios General gain constraints: Constrained model ID using material balance summation gain equations The algorithm runs both constrained and unconstrained identification for each case that is defined for identification. Use the Constrained Identification to enforce: Mass/Energy Balances Same gains on Parallel MVs Gains obtained through other means (Steady-State simulator, process knowledge ) 21

22 Constrained Identification (2) 22

23 Dead times Identification & Constraints (1) DMC3 Builder provides a feature to perform dead time estimation for each dependent\independent pair when working with subspace identification package (only) The identify dead time feature analyzes the selected dataset when estimating the dead time values The estimated dead times values obtained by running this feature should be used as a reference point and analyzed to determine if any manual edits might be required The estimated dead time values are used as constraints when performing the model identification just like the gain constraints 23

24 Dead times Identification & Constraints: Recommendations (2) Dead times can commonly be mis-identified as fast dynamics by both FIR and SubSpace (more so in FIR) Enforcing the dead times as constraints when identifying response curves allows the model ID algorithm to correctly distribute the fast dynamics of the process to the remaining potential candidates (ones that do not have dead times set of have shorter dead times specified) This leads to improvements in the overall quality of the identified model and specifically the fast dynamics response curves The user has the option to overridden the estimated dead times if need be 24

25 Dead times Identification & Constraints: Results (3) 25

26 Model identification results (1) Identification using dataset from pre-test activity (manual step test) 26

27 Model identification results (2) Identification using dataset from plant test (automatic step test using SmartStep) 27

28 Tools for Tracking Model Convergence DMC3 Builder allows you to perform statistical analysis to guide the testing efforts Correlation Estimates (SSID) Model Uncertainty Estimates Time Domain Step Response Bode Frequency Domain Response Colinearity Analysis Run the above for the Identification Run with the final estimate of the time to steady state 28

29 Correlation Plots (1) Extent of correlation 29

30 Correlation Plots (2) Aspen APC Builder Correlation plots An MV test pattern can mask the true cause/effect relationship (e.g., moving reboiler duty and consistently having to correct by moving reflux duty) Use MV Cross-Correlation plots to spot patterns < 50% OK > 50% (yellow) > 80% (red) NOTE: Auto-correlation w ill alw ays peak w ith 1 at lag = 0 Unusable data (correlation coefficient of 0.64) 30

31 Time Domain Uncertainty The 2σ certainty band represents 95% probability that the curve lies within the band 2σ 31

32 Frequency Domain Uncertainty Same response curve in the frequency domain 32

33 Controller Model Creation Click on the Update Curves to bring up the Model Update window. Select the Independent- Dependent response that seem unreasonable and Mask (disable copy) these models. Click Next then Update to copy the selected responses over to the controller model Masked Models 33

34 Curve Operations Option to override identified models using curve operations Option to construct FOPDT and SOPDT models Available Options- Unity Shift Multiplier Rate Gain Scaling Rate Scaling First Order Second Order Lead Lag Etc. 34

35 Matrix Conditioning Important to verify the quality of the model matrix for colinearity, before proceeding with the next step controller configuration Even slight differences in the model gains of collinear variables result in the controller to consider the variables to be separate and not indistinguishable Ill-conditioned model matrices can cause the controller to perform errant calculations and therefore incorrect moves Need to identify ill-conditioned models and collinearize specific models 35

36 Maintain APC benefits Adaptive modelling (1) The goal is to maximize controller up time and minimize lost profit due to APC model degradation. Degradation Time Degradation Time Process units, equipment & operation changes ov er time reduce the adv anced control benef its Lost Prof it Controller/Optimizer switched ON Update model Using Adaptiv e Modeling Update model Using Adaptiv e Modeling 36

37 Maintain APC benefits Adaptive modelling (2) Extract Data Collect Data, Export Aspen Watch Miscellaneous Point Data, or use APC Builder builtin data collection Model ID Use DMCPlus Model or APC Builder to build construct model ID cases to analyze extracted data and evaluating results Assess model quality Use Cross Correlation, Model Uncertainty, Predictions and engineering judgment Update and deploy improved model Use model results to determine where to focus the rest of the test 37

38 Summary Data collection requirements for model ID How to perform data analysis specifically and systematically How to improve the dataset Running ID cases Review the results Iterate the process to improve your models calibration before deploying 38

39 Q&A Antonio Marchionna 39

40 Thank You Antonio Marchionna, EMEA Professional Services