An improved scaling procedure for analysis and simplification of process models

Particle filter combined with data reconciliation for nonlinear state estimation with unknown initial conditions in nonlinear dynamic process systems

State estimation is very crucial for process control and optimization in dynamic processes. The particle filter (PF) is a novel and suitable technique for state estimation of nonlinear dynamic process systems. Conventional PFs for nonlinear dynamic process systems rely on the known initial conditions for state variables, such as the known probability density function (PDF) of initial states or the known values of initial states, but the initial conditions of a nonlinear dynamical system are usually unknown in actual industrial processes. In this paper, a novel methodology, PF combined with data reconciliation, is proposed and applied to nonlinear dynamic process systems for state estimation with unknown initial conditions. The measurement test criterion and data reconciliation with sequentially increasing data information are proposed to derive reliable initial values of the state variables under sufficient information of measurements. The interactive information between PF and data reconciliation problems can improve the initial values iteratively. Finally, accurate results of state estimation can be achieved. The effectiveness of the methodology is demonstrated through two nonlinear dynamic systems.