Chapter 15 - Optimal state and parameter estimation for fault detection and diagnosis of a nonlinear batch beer fermentation process

Abstract

Optimal state estimation is an integral part of bioprocess systems engineering with applications related to process monitoring, fault diagnosis, and control. There has been an increasing interest in the application of state and parameter identification techniques to increase the efficiency and performance of bioprocess systems. Process monitoring and fault diagnosis of bioprocesses typically require reliable real-time available process variable information. However, many of the important bioprocess variables cannot be measured online and require the need for online state and parameter estimation techniques. This chapter presents the method of extended Kalman filter (EKF) and various two-level methods for fault detection and diagnosis in nonlinear, time varying, and stochastic processes. Optimal state and parameter estimation by these methods aid in detecting and diagnosing faults in bioprocesses. The two-level methods presented in this chapter are formulated using different versions of EKF, recursive least squares, and a reduced-order extended Luenberger observer. These methods are specified for state estimation in the first level, and fault diagnosis via parameter identification in the second level. The performances of these methods are evaluated by applying them to a nonlinear batch beer fermentation. Among these methods, the method of reduced-order extended Luenberger observer and EKF is found advantageous as it provides the estimates unmeasured states in the presence of faults. The results show better performance of the method of two-level EKF for fault detection and diagnosis in transient beer fermentation.