Unscented Kalman Filter state and parameter estimation in a photobioreactor for microalgae production

Abstract

Microalgae have many applications such as the production of high value compounds (source of long-chain polyunsaturated fatty acids, vitamins, and pigments), in energy production (e.g. photobiological hydrogen, biofuel, methane) or in environmental remediation (especially carbon dioxide fixation and greenhouse gas emissions reduction). However, the photobioreactor microalgae process needs complex and costly hardware sensors, especially for biomass measurement. Thus, state and parameter estimation seems to be a critical issue and is studied in this paper in the case of a culture of the microalga Porphyridium purpureum. This paper is an extension of the previous work of Becerra-Celis et al. (2008) where the principal objective is to design a biomass estimator of this microalga production in a photobioreactor based on the total inorganic carbon measurement. Unscented Kalman filtering is applied to estimation of states and model parameters, producing better performances in comparison with Extented Kalman filtering. Numerical simulations in batch mode, and real-life experiments in continuous mode have been carried out. Corresponding results are given in order to highlight the performance of the proposed estimator.