On-line state estimation of bioprocesses with full horizon observers

Abstract

Software sensors (or state observers) are able to provide a continuous estimation of some signals (e.g. concentrations of important culture components, like biomass) which are not measured by hardware sensors. They need a mathematical model of the process and (discrete) hardware measurements of some other signals, like the concentrations of the main substrates. In this contribution, the state observer (called full horizon observer) is based on the identification of the most likely initial conditions of the experiment, e.g. the initial concentrations of the culture, these latter being identified at each time where new measurements are available. The basic principles of this observer are given in the general framework of nonlinear systems. Some properties and extensions of this state estimation method are presented. Some comparisons with the linear and extended Kalman filters are also given. The observer performances are illustrated in the case of the biomass concentration estimation within CHO animal cell cultures, for which only rare and asynchronous measurement samples of the glutamine, glucose and lactate concentrations are available.