Yunfei Chu

Model based methods become increasingly popular in process engineering. A process model is often required for controller design, state filtering, process monitoring and optimization. The performance of model based technologies is closely dependent on the quality of the built model. Even though tremendous progress has been made in system identification over the last few decades and a variety of techniques have been developed, identification of complex chemical systems with many correlated parameters from scarce noisy data is still a challenging problem. It should be noted that model identification is not just an approach for fitting data. My work focuses on several steps involved in the model building process. These include, model analysis, optimal experiment design, regularization of estimation problems, nonlinear parameter estimation and filtering, as well as model validation.

Sensitivity analysis of complex dynamic systems

Sensitivity analysis provides a powerful tool to analyze large-scale complex models. The analysis can improve the understanding of a system as it can be used to identify the contribution of individual parts of the model to the system functions. A variety of approaches to sensitivity analysis have been developed and four important techniques are investigated in my work (a local approach. the Morris method, a sampling-based approach, and the FAST method).

Parameter selection and experimental design under the parameter uncertainty

A common solution for improving accuracy of a process model is to estimate some of the model parameters from process data. However, the question of which model parameters should be estimated from data is often not systematically addressed and instead parameter set selection is performed based upon experience with the process. This practice can be problematic as determining a good set of parameters for estimation becomes less intuitive as more sophisticated models containing dozens or even hundreds of parameters are used, of which only a handful can be estimated from process data. An additional problem results from nonlinearity of the process. While techniques for parameter set selection exist for linear systems, they cannot take into account that the set of parameters to be estimated may depend upon operating conditions or even upon the nominal values of the parameters to be estimated.

The objective of my work is to systematically address the issue of determining a set of parameters to be estimated for models used for model predictive control. The procedure is specifically geared towards nonlinear systems as changing operating conditions, but also uncertainty in the nominal values of the parameters may change the importance of a chosen parameter set. The main contribution of this work is that parameter sets and data requirements will be determined simultaneously and no limiting assumptions about the values of the parameters to be estimated are required.

Application in system biology

Systems biology is a relatively new interdisciplinary field focusing on the systematic study of complex interactions in biological systems. Systems biology is an important application area for our sensitivity analysis, parameter set selection, and experimental design procedures. The techniques developed have been applied to models of signal transduction networks.