Real-time model based process monitoring of enzymatic biodiesel production

Indirect Measurement of Variables in a Heterogeneous Reaction for Biodiesel Production

This research focuses on the development of a state observer for performing indirect measurements of the main variables involved in the soybean oil transesterification reaction with a guishe biochar-based heterogeneous catalyst; the studied reaction takes place in a batch reactor. The mathematical model required for the observer design includes the triglycerides' conversion rate, and the reaction temperature. Since these variables are represented by nonlinear differential equations, the model is linearized around an operation point; after that, the pole placement and linear quadratic regulator (LQR) methods are considered for calculating the observer gain vector L(x). Then, the estimation of the conversion rate and the reaction temperature provided by the observer are used to indirectly measure other variables such as esters, alcohol, and byproducts. The observer performance is evaluated with three error indexes considering initial condition variations up to 30%. With both methods, a fast convergence (less than 3 h in the worst case) of the observer is remarked.

Transforming data to information: A parallel hybrid model for real-time state estimation in lignocellulosic ethanol fermentation

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.