In this paper, a method for unknown input estimation in 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. Unknown input Kalman filter is discussed to estimate the state and unknown input of the process. First, the theory of unknown inputs optimal filtering in the stochastic case is exposed and a design procedure is proposed. The observer is applied to an anaerobic fluidized bed reactor to estimate the variations in Chemical Oxygen Demand (COD) concentration and experimental results are presented.

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