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Optimization of microalgal photobioreactor system using model predictive control with experimental validation

机译:利用模型预测控制和实验验证优化微藻光生物反应器系统

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摘要

To maximize biomass and lipid concentrations, various optimization methods were investigated in microalgal photobioreactor systems under mixotrophic conditions. Lipid concentration was estimated using unscented Kalman filter (UKF) with other measurable sources and subsequently used as lipid data for performing model predictive control (MPC). In addition, the maximized biomass and lipid trajectory obtained by open-loop optimization were used as target trajectory for tracking by MPC. Simulation studies and experimental validation were performed and significant improvements in biomass and lipid productivity were achieved in the case where MPC was applied. However, occurence of a lag phase was observed while manipulating the feed flow rates, which is induced by large amount of inputs. This is an important phenomenon that can lead to model-plant mismatch and requires further study for the optimization of microalgal photobioreactors.
机译:为了最大化生物量和脂质浓度,在混合营养条件下,在微藻光生物反应器系统中研究了各种优化方法。使用无味卡尔曼滤波器(UKF)和其他可测量来源估算脂质浓度,然后将其用作进行模型预测控制(MPC)的脂质数据。此外,将通过开环优化获得的最大生物量和脂质轨迹用作目标轨迹,以进行MPC跟踪。进行了模拟研究和实验验证,在应用MPC的情况下,生物量和脂质生产率得到了显着提高。然而,在操纵进料流速时观察到了滞后阶段的发生,这是由大量输入引起的。这是一个重要现象,可能导致模型工厂不匹配,需要进一步研究以优化微藻光生物反应器。

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