Modelling and optimization study to improve the filtration performance of fibrous filter

Mohd Danish; Syed Mohd Yahya; Syed Ali Ammar Taqvi; Saeed Rubaiee; Anas Ahmed; Sayed Ameenuddin Irfan; Mustafa Alsaady

doi:10.1016/j.chemosphere.2022.137667

Modelling and optimization study to improve the filtration performance of fibrous filter

Chemosphere. 2023 Feb:314:137667. doi: 10.1016/j.chemosphere.2022.137667. Epub 2022 Dec 27.

Authors

Mohd Danish¹, Syed Mohd Yahya², Syed Ali Ammar Taqvi³, Saeed Rubaiee⁴, Anas Ahmed⁵, Sayed Ameenuddin Irfan⁶, Mustafa Alsaady⁷

Affiliations

¹ Department of Mechanical and Materials Engineering, University of Jeddah, Jeddah, 21589, Saudi Arabia. Electronic address: mdanish@uj.edu.sa.
² Sustainable Energy & Acoustics Research Lab, Mechanical Engineering, Z.H.C.E.T, Aligarh Muslim University, Aligarh, 202002, India. Electronic address: smyahya@zhcet.ac.in.
³ Department of Chemical Engineering, NED University of Engineering and Tachnology, Karachi, 75270, Pakistan.
⁴ Department of Mechanical and Materials Engineering, University of Jeddah, Jeddah, 21589, Saudi Arabia; Department of Industrial and Systems Engineering, University of Jeddah, Jeddah, 21589, Saudi Arabia.
⁵ Department of Industrial and Systems Engineering, University of Jeddah, Jeddah, 21589, Saudi Arabia.
⁶ Department of Continuing Education, Digipen Institute of Technology, Singapore.
⁷ Department of Chemical Engineering, University of Jeddah, Jeddah, 21589, Saudi Arabia.

PMID: 36581127
DOI: 10.1016/j.chemosphere.2022.137667

Abstract

Fibrous filter made up of non-woven material was utilized in many industrial applications for increasing the collection efficiency and the quality factor. But there exists a competing effect among the fibre diameter, filtration efficiency, pressure drop, and sometime type of aerosol (liquid or solid) plays a crucial role in the performance of the fibrous filter. To avoid overdesigning of the filter along with better performance, optimum set of parameters are to be decided before the manufacturing process. In the current effort, the desirability approach and along with the "Response Surface Methodology (RSM)" were considered to optimize filtration efficiency and pressure drop simultaneously. In this perspective, the impact of Filtration velocity (v), Basis weight (φ), Particle diameter (dp), and Packing fraction (α) on filtration efficiency (η) and pressure drop (Pd) was studied. Based on the outcome, the predicted values lie within experimental data through smart agreement. The maximum percentage (%) error was only 3% and 6% filtration efficiency (η) and pressure drop (Pd), which determine the effectiveness of this useful model. The most dominant factor which affects the filtration efficiency (η) was found to be the Basis weight (φ), followed by packing fraction. However, in the case of pressure drop, the most dominant factors were filtration speed followed by the pachining fraction. Moreover, artificial neural network (ANN) models are developed for the prediction of filtration efficiency and pressure drop. The model accuracy has been estimated by calculating "Mean Square Error (MSE), Mean Absolute Error (MAE), and coefficient of determination (R2)". Both models show promising results when compared with experimental data with the R2 value of 98.50-99.86. The optimized values of the maximum filtration efficiency and minimum pressure drop simultaneously were obtained for v = 5, φ = 59.60, dp = 52.23, α = 0.24 according to desirability approach.

Keywords: Artificial neural network; Fibrous filter; Filtration performance; Multi-objective optimization; Response surface methodology.

MeSH terms

Aerosols
Filtration*
Neural Networks, Computer*

Substances

Aerosols