An Experimental Comparison of a Sliding Mode Estimator to an Extended Kalman Filter Applied to a Cold Flow Circulating Fluidized Bed

Circulating fluidized beds (CFB) have been applied to a wide variety of chemical industry processes to reduce pollution and increase efficiency. Identifying the void fractions and the bed-height in the standpipe of the CFB is required for designing a controller to improve the overall system operation. An extended Kalman filter (EKF) algorithm has been applied in order to successfully estimate the states and the bed-height of the standpipe in the cold flow circulating fluidized bed (CFCFB) at the Department of Energy, National Energy Technology Laboratory. However, for some oscillating input cases, this method does not perform well. In addition, covariance matrices Q and R need to be assumed initially and depending upon initial conditions, for some cases, the EKF behaves unstably. In this research, a sliding mode estimator (SME) is applied in order to estimate the state, and the bed-height of the standpipe in the CFCFB. The sliding mode estimator requires the proper gain for tuning in order to have proper estimations. Test results show improvement in state estimation performance of SME over EKF.