Biomass estimation of an industrial raceway photobioreactor using an extended Kalman filter and a dynamic model for microalgae production

Abstract

Production of microalgae is one of the emerging biotechnological processes due to its potential applications to produce high value-added compounds. In photobioreactors for microalgae production, the biomass concentration is a desirable variable to be measured on-line to optimize the yield of the systems. However, biomass concentration can hardly be monitored in real time. There are few expensive commercial sensors that in fact provide uncertain measurements. State estimators, also known as software sensors, are algorithms designed to estimate unmeasured (or non-easily measurable) variables of a process. In this work, a state estimator using the extended Kalman filter algorithm is developed to estimate biomass concentration for an outdoor industrial raceway photobioreactor. The state estimator is based on a dynamic model for microalgae production specifically designed for this type of photobioreactor. Results demonstrate that, despite the complex non-linear dynamics that characterise this kind of bioprocess, a state estimator can provide a relatively accurate estimation of the biomass concentration. Furthermore, a state estimator could be used to optimize the operation of industrial photobioreactors by utilizing the estimated biomass concentration for automatic control of the process.