The extended Kalman filter (EKF) provides an efficient method for generating approximate maximum-likelihood estimates of the states or parameters of discrete-time nonlinear dynamical systems. In this paper, we consider the dual-estimation problem, the so-called dual EKF, in which both the states of a dynamical system and its parameters are estimated simultaneously, given only noisy observations. The main contribution of this paper is to show the efficacy of a proposed simplified dual-EKF technique (which in this work will be referred to as the dual EKF-2) in comparison with the conventional joint EKF. This has been demonstrated by conducting simulation studies on a CSTR which has been dynamically simulated using the HYSYS simulation package. Extensive analysis revealed that, not only the dual-EKF approach can achieve optimal state- and parameter-estimation performances comparable to the joint EKF, but also it has the main advantage of carrying out separate estimations of the states and parameters. (C) 2010 Elsevier Ltd. All rights reserved.