System identification of a lumped heat exchanger using the Extended Information Filter

In this study a new Extended Information Filter (EIF) algorithm is applied to compute the heat transfer parameters for lumped heal exchanger. Many industrial applications depend on the prediction of the heat exchanger parameters. This algorithm produces an accurate prediction of the operating states and parameters. A state variable model is derived from the empirical correlation of the lumped heat exchanger. The derived system is nonlinear and stochastic. The problem of estimating the state variables and parameters is considered in the presence of random disturbances and measurement noise. The EIF is then used to produce an optimal estimate of the state and parameters of the heat exchanger. The results obtained by using EIF were compared to the results obtained using EKF and found that the estimation of dynamic nonlinear systems, is best carried out using the EIF rather than the EKF.