Increasing the number of pleats in dust collection filters is commonly associated with improved filtration performance due to the enlargement of the effective filtration surface area. However, this increase in surface area does not always result in enhanced filtration efficiency or extended filter lifetime. As the pleat number increases, pleat angles become narrower, airflow distribution deteriorates, and pressure drop may rise significantly beyond a certain threshold. This indicates that evaluating filter performance solely based on surface area is insufficient and highlights the necessity of an engineering-based optimization approach.
In this study, the influence of pleat number on the filtration performance of dust collection filters is investigated using an integrated engineering approach combining experimental testing and numerical analysis. Filter samples with identical overall geometry but different pleat numbers are examined using a specially designed test rig. During the experiments, filtration media properties, airflow rate, and dust type are systematically varied, and their effects on pressure drop and filtration efficiency are comparatively analyzed. Throughout the testing process, inlet and outlet dust concentrations are continuously monitored to determine filtration efficiency, while the evolution of pressure drop over time is recorded.
The experimental results are validated by comparing them with theoretical data obtained from Computational Fluid Dynamics (CFD) analyses. CFD simulations are employed to analyze airflow distribution, local velocity profiles, and pressure drop mechanisms for different pleat geometries. Additionally, under regular dust loading conditions, the relationship between initial pressure drop and clogging-induced pressure increase is evaluated to predict filter clogging behavior.
In the advanced stage of the study, an artificial neural network (ANN)-based model is planned to be developed using the experimental data. Considering that artificial intelligence-based approaches are becoming increasingly inevitable in the filtration industry, the proposed ANN model aims to predict pressure drop and filtration performance for new filter geometries and operating conditions. The presented methodology...