Abstract:
This study addresses the challenging task of predicting the hydrodynamics of fluidized bed
reactors, with a particular focus on bubbling beds involving fine Geldart A particles having
limited successful simulations in existing literature. Our investigation explores into the
influence of various parameters, such as height, width, velocity, particle diameter, initial
height, solid fraction, particle density, drag scaling factor, restitution coefficient,
specularity coefficient, and mesh size, on both solid volume fraction and a modified drag
accounting for interparticle forces neglected in conventional drag models. Throughout the
simulations, data was systematically generated and subsequently utilized to train four
machine learning models: Decision Trees (DT), Extreme Learning Trees (ELT), Gaussian
Process Regression (GPR), and Support Vector Machines (SVM). Optimization
techniques, including Genetic Algorithm (GA) and Particle Swarm Optimization (PSO),
were employed to enhance model performance. Notably, the GPR-GA model exhibited
superior results, achieving an R2 of 0.99 and 0.97 for training and testing, respectively. To
facilitate user-friendly application and practical implementation, a Graphical User
Interface (GUI) was developed. This GUI empowers users to predict the hydrodynamics
of gas-solid fluidized bed reactors by inputting specific parameters and leveraging the
optimized machine learning models. This comprehensive approach contributes valuable
insights and tools for understanding and predicting the intricate dynamics of fluidized bed
reactors.
