Soft sensor design with state estimator for lipid estimation of microalgal photobioreactor system

Sung Jin Yoo, J. H. Kim, J. M. Lee
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Abstract

Microalgae have received considerable attention as a source of biodiesel since they contain large amounts of lipid. However, microalgal biodiesel is not economically competitive compared to other sources of biodiesel or petrodiesel. To improve the productivity of lipid and biomass in photobioreactor systems, real-time monitoring and control are prerequisite. However, measurement of lipid in microalgae itself is very difficult and time consuming task, and it is not easy to know the lipid concentration in real-time. In this study, estimation of lipid concentration using other online measurements from 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 find appropriate types of sensors for estimating lipid have also been performed. This study suggests what filters and what type of sensors are suitable for estimating lipid concentration on-line.
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基于状态估计器的微藻光生物反应器系统脂质估计软传感器设计
由于微藻含有大量的脂质,因此作为生物柴油的一种来源受到了相当大的关注。然而,与其他生物柴油或石油柴油相比,微藻生物柴油在经济上并不具有竞争力。为了提高光生物反应器系统中脂质和生物质的生产效率,实时监测和控制是先决条件。然而,微藻脂质本身的测量是一项非常困难和耗时的任务,并且不容易实时了解脂质浓度。在这项研究中,脂质浓度的估计使用其他在线测量从生物量或葡萄糖传感器进行了研究。扩展卡尔曼滤波器(EKF)、无气味卡尔曼滤波器(UKF)和粒子滤波器(PF)在各种情况下对光生物反应器系统的适用性进行了比较。此外,还进行了模拟研究,以找到适当类型的传感器来估计脂质。本研究提出了适合在线估计脂质浓度的过滤器和传感器类型。
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