A comparative study of soft sensor design for lipid estimation of microalgal photobioreactor system with experimental validation

This study examines the applicability of various nonlinear estimators for online estimation of the lipid concentration in microalgae cultivation system. Lipid is a useful bio-product that has many applications including biofuels and bioactives. However, the improvement of lipid productivity using real-time monitoring and control with experimental validation is limited because measurement of lipid in microalgae is a difficult and time-consuming task. In this study, estimation of lipid concentration from other measurable sources such as biomass or glucose sensor was studied. Extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter (PF) were compared in various cases for their applicability to photobioreactor systems. Furthermore, simulation studies to identify appropriate types of sensors for estimating lipid were also performed. Based on the case studies, the most effective case was validated with experimental data and found that UKF and PF with time-varying system noise covariance is effective for microalgal photobioreactor system.