Simulation of Beer Fermentation Combining CFD and Fermentation Reaction Models

Beer fermentation is a critical process that directly influences product quality and flavor. However, traditional fermentation practices often rely on empirical methods, leading to prolonged production cycles and inconsistent product quality. This study presents a multiphysics-coupled simulation model that integrates computational fluid dynamics (CFD) with fermentation reaction kinetics to address challenges in temperature control and monitoring in large-scale fermenters. The model incorporates the Navier–Stokes equations for fluid flow, energy equations for heat transfer, fermentation kinetics for sugar metabolism, and a yeast cell proliferation model based on population balance theory. The model is validated through experiments at both lab scale (0.3 m3) and industrial scale (375 m3). Statistical analysis shows excellent agreement, with coefficients of determination (R2) for alcohol and sugar content reaching up to 0.99 and 0.96 at the lab scale, and 0.93 and 0.85 at the industrial scale, respectively. Key quantitative results from the industrial-scale validation demonstrate that the model accurately predicts the primary fermentation dynamics: within a 100 h period, alcohol concentration increased from 0% to approximately 6%, while sugar content decreased from 13 °P to 2 °P, closely matching experimental data. Crucially, the simulation captures a significant temperature overshoot at approximately 48 h, where the peak temperature at the top of the fermenter reached 16.01 °C (a 3 °C overshoot above process requirements). This pronounced vertical temperature gradient, arising from symmetry-breaking thermal conditions on the fermenter walls, was found to induce strong, asymmetric natural convection with flow velocities up to 13.2 mm·s−1, revealing spatial heterogeneities that are critical for optimizing fermenter design. At the lab scale, the simulation also accurately captures the observed quadratic temperature rise, further confirming the model’s robustness. This study provides a theoretical foundation for optimizing cooling jacket configurations and control strategies, ultimately improving fermentation efficiency and ensuring consistent product quality.