A predictive framework for adaptive control in spouted bed coffee roaster: Coupling bean transformations with spouting hydrodynamics

Spouted bed roasting is widely used in coffee processing for uniform heat and mass transfer. However, continuous changes in bean mass, volume, moisture, and density during roasting induce hydrodynamic instabilities and reduce energy efficiency. Previous studies have mainly employed static-particle assumptions, leaving opportunities to further explore continuous property changes. To overcome this limitation, this study develops a novel dynamic, one-dimensional computational framework that explicitly links the evolving physical properties of Arabica and Robusta beans to real-time hydrodynamic adjustments. The model couples heat and mass transfer balances with a minimum spouting velocity correlation to dynamically update the required inlet air mass flow rate. Results demonstrate both species exhibit a 30%–33% density reduction and accumulate approximately 200kJ/kg of heat. This density change lowers aerodynamic resistance and minimum spouting velocity, dictating an 18%–19% reduction in the required airflow to maintain optimal spouting. Temporally, Robusta exhibits a steeper, more abrupt airflow decline initiating earlier in the process. While Arabica begins structural changes at a lower temperature (180◦C) due to a porous structure, showing restrained expansion (38%–40%), Robusta’s denser matrix traps internal gases effectively. This leads to a delayed but more extensive (40%–44%) and rapid expansion phase. Ultimately, this framework advances beyond conventional static models by translating transient thermophysical phenomena into a deployable feedforward control strategy, enabling automated roasting systems to optimize energy and maintain stable fluidization.