Electrospun nanofiber membrane diameter prediction using a combined response surface methodology and machine learning approach

Md. Nahid Pervez, Wan Sieng Yeo, Mst. Monira Rahman Mishu, Md. Eman Talukder, Hridoy Roy, Md. Shahinoor Islam, Yaping Zhao, Yingjie Cai*, George K. Stylios*, Vincenzo Naddeo*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)
49 Downloads (Pure)

Abstract

Despite the widespread interest in electrospinning technology, very few simulation studies have been conducted. Thus, the current research produced a system for providing a sustainable and effective electrospinning process by combining the design of experiments with machine learning prediction models. Specifically, in order to estimate the diameter of the electrospun nanofiber membrane, we developed a locally weighted kernel partial least squares regression (LW-KPLSR) model based on a response surface methodology (RSM). The accuracy of the model's predictions was evaluated based on its root mean square error (RMSE), its mean absolute error (MAE), and its coefficient of determination (R2). In addition to principal component regression (PCR), locally weighted partial least squares regression (LW-PLSR), partial least square regression (PLSR), and least square support vector regression model (LSSVR), some of the other types of regression models used to verify and compare the results were fuzzy modelling and least square support vector regression model (LSSVR). According to the results of our research, the LW-KPLSR model performed far better than other competing models when attempting to forecast the membrane's diameter. This is made clear by the much lower RMSE and MAE values of the LW-KPLSR model. In addition, it offered the highest R2 values that could be achieved, reaching 0.9989.
Original languageEnglish
Article number9679
JournalScientific Reports
Volume13
DOIs
Publication statusPublished - 15 Jun 2023

Keywords

  • Computer Simulation
  • Least-Squares Analysis
  • Machine Learning
  • Membranes
  • Nanofibers

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