Dynamic reconciliation of process measurements.

Abstract

All process measurements obtained from measurement devices are corrupted with noise. In any modern chemical plant, thousands of measurements are performed every few seconds. Before such data can be used for plant optimization and control, it is typically filtered / reconciled to allow better estimates of key process states (e.g. temperatures, pressures, concentrations).

This thesis focuses on the classic data reconciliation technique for linear systems, the Kalman filter, published by Rudolph Emil Kalman in the 1960s. A brief overview of the technique will be discussed and how it can be modified to yield the extended Kalman filter, suited for data reconciliation of nonlinear systems.

Within the scope of this thesis, an extended Kalman filter MATLAB toolbox was written.

State estimation using the MATLAB toolbox will be demonstrated on a continuous stirred tank reactor having three irreversible parallel reactions.