Modeling of Biodiesel Production via Transesterification using Inline Raman Spectroscopy

We present a reaction kinetics model for biodiesel production via transesterification, which is calibrated using concentration measurements from inline Raman spectroscopy. The novel application of Raman spectroscopy in biodiesel production provides real-time monitoring of key reaction species, e.g., fatty acid methyl esters, triglycerides, methanol, and glycerol. We employ an automated semi-batch reactor to perform reaction experiments. A robust offline calibration process of the Raman device allows achieving high accuracy for concentration predictions (R 2 = 0.99). Moreover, using sodium methylate as the catalyst, we address a gap in the literature where kinetic parameter values for transesterification with this catalyst are unavailable. For accurate parameter estimation, we use genetic algorithms. A global sensitivity-based estimability analysis confirms the sufficiency of the experimental data. We determine confidence intervals through Hessian matrix estimation. Our model predictions are validated against experimental data at 60 °C, demonstrating excellent agreement. These results highlight the effectiveness of integrating Raman spectroscopy for modeling reaction kinetics, hence offering promising tools and models for monitoring, optimization, and control of biodiesel production processes.