Multiscale pulsating intensification and wavelet entropy-driven AI optimization of fluidized biomass hydrogen production

To address the issue of suboptimal hydrogen production efficiency resulting from particle agglomeration and channeling during biomass fluidized bed gasification, this paper presents a method for process intensification and prediction that integrates multiscale flow analysis with deep learning. Initially, pressure pulsation signals are decomposed using power spectral density (PSD) and wavelet transforms to identify the system's eigenfrequencies. The mechanisms by which pulsed gas flows are intensified across the micro, meso, and macroscales are examined. This involves overcoming diffusion limitations at the microscale, disrupting bubbles and particle agglomerates at the mesoscale, and enhancing bed mixing and promoting reaction equilibrium at the macroscale. Secondly, the multiscale coupling relationship between flow complexity and hydrogen yield is elucidated. The trends of wavelet flow-reaction entropy and gas yield exhibit synchronization with increasing pulsation frequency. This study establishes wavelet flow-reaction entropy as a key feature characterizing the correlation between flow complexity and reaction performance. Finally, a two-level Deep Neural Network model driven by wavelet flow-reaction entropy is developed. This model follows the flow trends of the fluidized bed system and enables the accurate prediction of hydrogen yield under various operating conditions, achieving an overall R² of 0.969. Furthermore, the optimal pressure and pulsation frequency ranges (Zones A and B) are identified, enhances hydrogen production performance. The predicted maximum H₂ yield is 12.67 mol/kg-biomass under conditions of 4.4 bar and 3.5 Hz. This study not only unveils the multiscale flow-reaction coupling mechanism underlying pulsed fluidized hydrogen production but also offers a novel theoretical foundation for the intelligent control of biomass pyrolysis gasification processes.