Improving the performance of fluidized bed reactor using rotating distributor and intelligent modeling

TL;DR

This study improves fluidized bed reactor performance using a rotating distributor and an AI model, enhancing efficiency for renewable energy applications.

Contribution

The study introduces a rotating distributor and a novel hybrid AI model for optimizing fluidized bed reactor performance.

Findings

Using a rotating distributor reduced bed pressure drop by 20% and increased heat transfer coefficient by 30%.

The proposed ANN–PSO model accurately predicts multiphase flow with an R² of 0.991 for heat transfer.

The hybrid model offers a fast and interpretable approach for reactor optimization.

Abstract

A swirling fluidized bed reactor (SFBR) has been used as a promising technique for renewable energy applications, such as biomass combustion, gasification, and pyrolysis systems, due to its superior solid-gas thermal efficiency and mixing. This study presents an innovative approach to improve SFBR performance through the use of a rotating annular blade air distributor, considering the limitations of traditional fluidized beds. This study also provides a novel hybrid artificial intelligence (AI) model for further optimization and predicting the complex multiphase flow and heat transfer processes in SFBRs. The hybrid model integrates an artificial neural network (ANN) with particle swarm optimization (PSO) and is trained on a comprehensive experimental dataset across a range of operating conditions, including inlet gas velocity, distributor rotational speed, and radial and axial positions of the reactors. The study findings highlight that using a rotating distributor significantly enhances reactor performance, as increasing the rotational speed of the distributor decreased the bed pressure drop by 20% and increased the heat transfer coefficient by up to 30%. Furthermore, the proposed ANN–PSO model can provide an interpretable, fast, and efficient approach to predict and model the multiphase flow in SFBRs with high accuracy (R² = 0.991 for heat transfer). The study findings provide a foundation for future investigations on fluidized bed technologies for energy applications.

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