The Reduced Rank of Ensemble Kalman Filter to Estimate the Temperature of Non Isothermal Continue Stirred Tank Reactor
Abstract: Kalman filter is an
algorithm to estimate the state variable of dynamical stochastic system. The
square root ensemble Kalman filter is an modification of Kalman filter. The
square root ensemble Kalman
filter is proposed
to keep the
computational stability and
reduce the computational
time. In this paper we study the
efficiency of the reduced rank ensemble Kalman filter. We apply this algorithm
to the non isothermal continue stirred tank reactor problem. We decompose the covariance of the ensemble
estimation by using the singular value decomposition (the SVD), and then we
reduced the rank of the diagonal matrix of those singular values. We make a
simulation by using Matlab program. We took some the number of ensemble such as
100, 200 and 500. We compared the computational time and the accuracy between
the square root ensemble Kalman
filter and the
ensemble Kalman filter.
The reduced rank
ensemble Kalman filter can’t be applied in this problem because the
dimension of state variable is too less.
Author: Erna Apriliani, Dieky
Adzkiya, Arief Baihaqi
Journal Code: jptindustrigg110008
