Humidification Pipe Structure Design and Evaluation in Qu Fang
Article
Figures
Metrics
Preview PDF
Reference
Related
Cited by
Materials
Abstract:
The relative humidity of traditional Qu workshops is controlled by opening and closing doors and windows as well as spraying water manually in the workshop. This humidity control method is greatly affected by the natural environment, and it is difficult to ensure a constant humidity. Consequently, it is difficult to maintain a consistent quality of Daqu at different locations in the workshop. To realize real-time control of relative humidity in the workshop, it is necessary to understand how the humidifying devices in the workshop affect humidity control during Daqu fermentation. A three-dimensional turbulent flow model of the Qu workshop, coupled with porous media and component transport models, was adopted to study the relative humidity during humidification of the workshop. Numerical simulation of the humidification process in the workshop was performed using Fluent. Furthermore, the model of the workshop was verified using the humidification test setup currently available. The maximum deviation between the simulated and experimental values of the relative humidity during humidification was 1.1%, and that for the humidifying time was only 6.5 s. These results demonstrate the effectiveness of the humidification model established for the workshop. The effects of the pipeline diameter, number of openings, and opening size on the humidification performance were examined experimentally. The humidification device parameters and their combination were optimized with the help of the single-factor and orthogonal test methods. Humidification was found to be optimized with 80 mm-diameter pipelines with six 30 mm-diameter openings. The humidifying time needed was reduced by 6.6% after parameter optimization compared to that before optimization. The findings of this research provide a reference for model construction in subsequent humidity control research for Qu workshops in terms of both theory and data.