Evaluating and Estimating the Complex Dynamic Phenomena in Nonlinear Chemical Systems

Abstract

Successful operation and control of complex dynamic systems heavily rely on the availability of fast and accurate evaluation of the system performance. The measurement problems and the delays associated with these systems require the need for on-line state estimators as alternative measurement tools. In this work, a state estimation method based on extended Kalman filter (EKF) is presented for nonlinear dynamical systems that are characterized by complex dynamic phenomena such as multiple steady state behavior, limit cycle oscillations and chaos. The estimator uses the mathematical model of the process in conjunction with the known process measurements to provide the unmeasured process states that capture the fast changing nonlinear dynamics of the process. The design and performance of the state estimator is evaluated by applying two typical continuous non-isothermal nonlinear processes, a chemical reactor and a polymerization reactor, which show rich dynamical behavior ranging from stable situations to chaos. In order to understand the dynamic phenomena and to analyze the conditions that lead to an improved operation, prior to state estimation, these processes are thoroughly analyzed for multiplicity, stability and bifurcation studies. The sensitivity of the state estimator is also studied towards the effect of the design parameters involved in the method. The results demonstrate the efficacy of the model based method for state estimation in nonlinear chemical processes associated with complex dynamic behavior.