Parameter estimation in chemical process models

• Main Author: Halim Kusuma Hambalie
• Other Authors: School of Chemical and Biomedical Engineering
• Format: Final Year Project
• Language: English
• Published: 2009
• Subjects: DRNTU::Engineering::Chemical engineering::Chemical processes
• Online Access: http://hdl.handle.net/10356/16644
• Institution: Nanyang Technological University

This project concerns itself with the study of parameter estimation in chemical process models that are critical in process optimisation, monitoring and control. The chemical models are constructed from law of conservation of mass and energy. The original models are described by a set of ordinary differential, algebraic equations before being discretised into state-space model. Kalman Filter (KF), Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) were implemented on the models to approximate the state and parameter of the dynamic system that were obtained via measurement tools and were corrupted with noise. Simulations were performed by varying the model, Filter parameters and tuning the Filter variables. Mean- Squared-Error(MSE) and computing speed was used as performance criteria. The Filters performed efficiently with minimal MSE under general simple situation. However, with higher order model and initial guess values far deviating from actual, the EKF was not able to perform satisfactorily. UKF was suggested as drop-in for EKF as it offers great improvement over EKF - providing accurate estimate for chemical models that are often highly nonlinear with uncertain initial condition and estimates.