Modeling and Estimation of Flow Boiling System Using Kalman Filtering

Abstract

The Kalman filter (KF) is a set of mathematical equations that provides an efficient computational (recursive) means to estimate the state of a process, in a way that minimizes the mean of the squared error. The filter is very powerful in several aspects: it supports estimations of past, present, and even future states, and it can do so even when the precise nature of the modeled system is unknown. In this paper, the mathematical model of a continuous and discrete flow boiling system has been developed. Kalman filtering was used to estimate the states of a linearizead boiling system. Extended Kalman filtering (KF) was applied to estimate the states of the nonlinear boiling system. It follows from the obtained results that the KF and EKF do the job. KF gives a good result due to optimality and structure. Since it is difficult to install sensors inside the boiler, it will be more convenient to design a state feedback controller using the Kalman filter for the system to track some desired set points (e.g.. Temperature, pressure, etc). The results were presented using MATLAB-Simulink simulations.