Operating lignocellulosic fermentation processes to produce fuels and chemicals is challenging due to the inherent complexity and variability of the fermentation media. Real‐time monitoring is necessary to compensate for these challenges, but the traditional process monitoring methods fail to deliver actionable information that can be used to implement advanced control strategies. In this study, a hybrid‐modeling approach is presented to monitor cellulose‐to‐ethanol (EtOH) fermentations in real‐time. The hybrid approach uses a continuous‐discrete extended Kalman filter to reconciliate the predictions of a data‐driven model and a kinetic model and to estimate the concentration of glucose (Glu), xylose (Xyl), and EtOH. The data‐driven model is based on partial least squares (PLS) regression and predicts in real‐time the concentration of Glu, Xyl, and EtOH from spectra collected with attenuated total reflectance mid‐infrared spectroscopy. The estimations made by the hybrid approach, the data‐driven models and the internal model were compared in two validation experiments showing that the hybrid model significantly outperformed the PLS and improved the predictions of the internal model. Furthermore, the hybrid model delivered consistent estimates even when disturbances in the measurements occurred, demonstrating the robustness of the method. The consistency of the proposed hybrid model opens the doors towards the implementation of advanced feedback control schemes.

中文翻译：

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将数据转化为信息：木质纤维素乙醇发酵实时状态估计的并行混合模型

由于发酵介质固有的复杂性和可变性，操作木质纤维素发酵工艺以生产燃料和化学品具有挑战性。实时监控对于弥补这些挑战是必要的，但传统的过程监控方法无法提供可用于实施高级控制策略的可操作信息。在这项研究中，提出了一种混合建模方法来实时监测纤维素到乙醇 (EtOH) 的发酵。混合方法使用连续离散扩展卡尔曼滤波器来协调数据驱动模型和动力学模型的预测，并估计葡萄糖 (Glu)、木糖 (Xyl) 和 EtOH 的浓度。数据驱动模型基于偏最小二乘 (PLS) 回归并实时预测 Glu、Xyl、和来自用衰减全反射中红外光谱收集的光谱中的乙醇。在两个验证实验中比较了混合方法、数据驱动模型和内部模型所做的估计，表明混合模型显着优于 PLS，并改进了内部模型的预测。此外，即使在测量中发生干扰时，混合模型也能提供一致的估计，证明了该方法的稳健性。所提出的混合模型的一致性为高级反馈控制方案的实施打开了大门。在两个验证实验中比较了数据驱动模型和内部模型，表明混合模型显着优于 PLS 并改进了内部模型的预测。此外，即使在测量中发生干扰时，混合模型也能提供一致的估计，证明了该方法的稳健性。所提出的混合模型的一致性为高级反馈控制方案的实施打开了大门。在两个验证实验中比较了数据驱动模型和内部模型，表明混合模型显着优于 PLS 并改进了内部模型的预测。此外，即使在测量中发生干扰时，混合模型也能提供一致的估计，证明了该方法的稳健性。所提出的混合模型的一致性为高级反馈控制方案的实施打开了大门。