Linear system identification of a cold flow circulating fluidized bed

Abstract

Knowledge of the solids circulation rate (SCR) is essential to the control and improved performance of a circulating fluidized bed system. In the present work, the noise model is derived using the prediction error method considering process and measurement noises acting on the cold flow circulating fluidized bed (CFCFB) with a cork particulate material. The outputs of the initial model are the total pressure drop across the riser, the pressure drop across the crossover, the pressure drop across the primary cyclone, the total pressure drop across the stand-pipe, the pressure drop across the loop seal, and the SCR. The stochastic estimate of SCR is determined from the noise model using the stochastic pressure drop estimates. The deterministic estimate is obtained through the inputs taken as move air flow, riser aeration, and loop seal fluidization air that are all independent variables of the given setup and under the control of the user. The theory has been developed to convert a complete blackbox model to a grey box model through the output-to-state transformation such that both the models of the CFCFB consists of all these output variables as the states of the system, and only pressure drops across the system as the output measurements. Thus, the final models do not include any fictitious terms and they are defined only in terms of physical parameters of the given system. Both components of SCR are separately analysed. The combined SCR response of both the noise model and deterministic model is compared with the validation data set of this state variable in terms of modelfit, and the results are shown.