8
Data Reconciliation with MATLAB ... from “actual experience”! Felipe Aristizábal Department of Chemical Engineering McGill University Montréal, Québec July 31, 2013

MATLAB Reconciliation

  • Upload
    savan

  • View
    15

  • Download
    1

Embed Size (px)

DESCRIPTION

linear algebra

Citation preview

Page 1: MATLAB Reconciliation

Data Reconciliation with MATLAB... from “actual experience”!

Felipe Aristizábal

Department of Chemical EngineeringMcGill University

Montréal, QuébecJuly 31, 2013

Page 2: MATLAB Reconciliation

Objectives

We will cover:The “basic fundamentals” of data reconciliation.Linear and Bilinear systems.MATLAB implementation.

Page 3: MATLAB Reconciliation

Problem Description

Using mass balances, calculate the value of F6:

FI101

FI103

FI105

FI106

FI102

FI104

1

2

3

4

5 6

Stream Name kt/hFI-101 F1 110.5FI-102 F2 60.8FI-103 F3 35.0FI-104 F4 68.9FI-105 F5 38.6FI-106 F6 ????

Many solutions possible. Redundant measurements.

Page 4: MATLAB Reconciliation

Data Reconciliation

All measurements have errors.Take advantage of redundant information.Formulate as constrained optimization problem.Analytical solution for linear and bilinear constraints.

Linear Constraints

Minimize: J(y) =(y − y)TV−1(y − y)Subject to: Ay =0

Where:

y : m by 1 vector of raw measurements.y : m by 1 vector of estimates.V : m by m covariance matrix.A: Incidence matrix - mass balances.

Page 5: MATLAB Reconciliation

Analytical Solution: Linear Data Reconciliation

Using Lagrange multipliers ....... the estimates, y , can be calculated from:

W = I − VAT (AVAT )−1Ay = Wy

Cov(y) = WVW T

Procedure (MATLAB)1 Create y from measurements2 Obtain V from measurement standard deviations (V = diag(σ2

i ))3 Use mass balances to obtain A4 Calculate W , y5 Extra: what are estimates standard deviations, σi?

σi =√

diag(Cov(y))

Page 6: MATLAB Reconciliation

Example: Linear DR

1

2

3

4

5 6

Stream Raw Standar Reconciled StandarNo. Measurement Deviation, σ Flow Deviation σ

(kt/h) (kt/h) (kt/h) (kt/h)1 110.5 0.82 103.2 0.422 60.8 0.53 65.4 0.373 35.0 0.46 37.8 0.304 68.9 0.71 65.4 0.375 38.6 0.45 37.8 0.306 101.4 1.20 103.2 0.42

Page 7: MATLAB Reconciliation

Example: Bilinear System

F1x1,1x1,2

F2x2,1x2,2

F3x3,1x3,2

Non-Linear Constraints

F1x1,1 − F2x2,1 − F3x3,1 = 0x1,1 + x1,2 = 1...

Transform tocomponent flow

fij = Fixij

F1f1,1f1,2

F2f2,1f2,2

F3f3,1f3,2

Linear Constraints

f1,1 − f2,1 − f3,1 = 0F1 − f1,1 − f1,2 = 0...

Page 8: MATLAB Reconciliation

Example: Bilinear System (cont.)

Approximate the variance of the new variables,

V (fij) ≈ x2ijV (Fi ) + F 2

i V (xij)

Variable Raw Standar ReconciledName Measurement Deviation, σ Measurement

F1 1095.5 54.8 1010.2x1,1 0.4822 0.0048 0.4808x1,2 0.5170 0.0052 0.5192F2 478.4 23.9 500.1x2,1 0.9410 0.0094 0.9518x2,2 0.0501 0.0005 0.0480F3 488.2 24.4 510.1x3,1 0.0197 0.0002 0.0190x3,2 0.9748 0.0097 0.9810