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.

On-Line Monitoring of Biological Parameters in Microalgal Bioprocesses Using Optical Methods

“…The constant gain can also be continuously adapted by some available technique [84], and interval observers can limit the state trajectory based on the known intervals of uncertain model parameters [73]. The second category are variants of the Kalman filter which alters the correction gain in each iteration recursively using the comparison between noisy state estimations and noisy measurements, using covariance matrices of the system and measurement noises whose selection is critical for the proper functioning of the filter [85].…”

“…A state estimator with the EKF was developed to estimate biomass concentration in an outdoor raceway cultivating Scenedesmus [85], based on a dynamic model of the process [89]. As inputs for the EKF, on-line measured values of dissolved oxygen, pH, injected flows of air and CO 2 and solar radiation were used.…”

Modelado y control de la producción de microalgas en fotobiorreactores industriales

“…Aunque en la literatura existen algunos resultados aplicados a escala de laboratorio (Dochain, 2000;Li et al, 2003;Tebbani et al, 2013), no existen muchos trabajos aplicados a fotobioreactores a escala industrial. En (García-Mañas et al, 2019), se desarrolló un estimador de estado basado en el filtro de Kalman extendido para estimar la concentración de biomasa en fotobioreactores abiertos. Dicho estimador se basa en el modelo basado en primeros principios desarrollado para estos reactores en (Fernández et al, 2016a) y sigue la estructura que se observa en la Figura 7.…”

“…Como se puede apreciar, el estimador sigue correctamente la evolución de las medidas reales obtenidas mediante análisis de muestras en laboratorio marcadas en color rojo. Más información se puede encontrar en (García-Mañas et al, 2019). dependiendo de la cepa utilizada, siendo necesario conseguir aproximarse a los mismos para alcanzar una velocidad de crecimientoóptima (Costache et al, 2013).…”

“…Sin embargo, todos estos métodos generalmente permiten aportar medidas discontinuas e imposibles de usar para control en línea. Es por ello que se requiere desarrollar observadores de estado para la estimación en línea de estas variables (García-Mañas et al, 2019).…”