ON-LINE ESTIMATION OF UNMEASURED INPUTS FOR ANAEROBIC PROCESS DESCRIBED BY INTERPOLATED L.T.I. MODELS

摘要

A method for unknown input estimation in nonlinear stochastic system is presented. A key problem in bioprocess systems is the absence, in some cases, of reliable on line measurements for real time monitoring applications. In this paper, a software sensor for an anaerobic digester is presented. Unmeasured components of the influent are estimated from available on line measurements. Based on a multiple model scheme, a bank of unknown input Kalman filters are discussed to estimate a probabilistic weighting state and unknown input of the process. The performances of the method are tested in simulation using a validated model of an anaerobic fixed bed pilot plant.