Advanced fluidized bed reactor performance: Optimizing residence time distribution through helical screw induced rotation ( <scp>HSIR</scp> )

Abstract Optimizing flow behaviour and residence time distribution (RTD) is crucial for achieving steady‐state operation in continuous reactors. This study employs a multi‐objective optimization framework to enhance reactor performance by investigating the effects of feeder rotation speed (FRS) and helical screw rotation speed (HSRS) on key RTD parameters. Unlike traditional response surface methodology (RSM), which may struggle with complex factor interactions, this approach integrates definitive screening design (DSD), I‐optimal Design, and desirability analysis to achieve a more precise and robust optimization. Residual analysis confirmed model validity, while perturbation, contour, and 3D surface plots revealed significant non‐linear interactions between FRS and HSRS. The desirability plot identified an optimal region at lower HSRS (10–40 rpm) and moderate to high FRS (50–100 rpm), maximizing mean residence time (MRT), minimizing axial dispersion (D a ), and ensuring stable flow conditions. The overlay plot validated this optimal region by confirming that all constraints were simultaneously satisfied. The strong alignment between desirability‐based optimization and constraint‐based feasibility analysis underscores the superiority of this method over RSM, which often struggles to capture such complex interactions effectively. The findings demonstrate that this optimization framework successfully enhances steady‐state operation by precisely controlling MRT, dispersion, and flow behaviour. Moreover, this methodology is not reactor‐specific and can be effectively applied to any continuous reactor system, providing a versatile tool for improving performance in various industrial processes. The study highlights the advantages of a modern, data‐driven optimization approach in accurately predicting and fine‐tuning reactor conditions, making it a superior alternative to conventional RSM‐based methods.