Electrospun nanofiber membrane diameter prediction using a combined response surface methodology and machine learning approach
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 model...
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| Published in | Scientific reports Vol. 13; no. 1; pp. 9679 - 14 |
|---|---|
| Main Authors | , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
15.06.2023
Nature Publishing Group Nature Portfolio |
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| 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 (
R
2
). 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 R
2
values that could be achieved, reaching 0.9989. |
|---|---|
| AbstractList | 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 (R
). 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 R
values that could be achieved, reaching 0.9989. 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.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. 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 ( R 2 ). 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 R 2 values that could be achieved, reaching 0.9989. 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. 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 (R 2). 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. |
| ArticleNumber | 9679 |
| Author | Pervez, Md. Nahid Islam, Md. Shahinoor Stylios, George K. Zhao, Yaping Naddeo, Vincenzo Yeo, Wan Sieng Cai, Yingjie Mishu, Mst. Monira Rahman Talukder, Md. Eman Roy, Hridoy |
| Author_xml | – sequence: 1 givenname: Md. Nahid surname: Pervez fullname: Pervez, Md. Nahid organization: Hubei Provincial Engineering Laboratory for Clean Production and High Value Utilization of Bio-Based Textile Materials, Wuhan Textile University, Sanitary Environmental Engineering Division (SEED), Department of Civil Engineering, University of Salerno – sequence: 2 givenname: Wan Sieng surname: Yeo fullname: Yeo, Wan Sieng organization: Department of Chemical and Energy Engineering, Faculty of Engineering and Science, Curtin University Malaysia – sequence: 3 givenname: Mst. Monira Rahman surname: Mishu fullname: Mishu, Mst. Monira Rahman organization: Faculty of Nutrition and Food Science, Patuakhali Science and Technology University – sequence: 4 givenname: Md. Eman surname: Talukder fullname: Talukder, Md. Eman organization: Hubei Provincial Engineering Laboratory for Clean Production and High Value Utilization of Bio-Based Textile Materials, Wuhan Textile University – sequence: 5 givenname: Hridoy surname: Roy fullname: Roy, Hridoy organization: Department of Chemical Engineering, Bangladesh University of Engineering and Technology – sequence: 6 givenname: Md. Shahinoor surname: Islam fullname: Islam, Md. Shahinoor organization: Department of Chemical Engineering, Bangladesh University of Engineering and Technology – sequence: 7 givenname: Yaping surname: Zhao fullname: Zhao, Yaping organization: Shanghai Engineering Research Center of Biotransformation of Organic Solid Waste, School of Ecological and Environmental Sciences, East China Normal University, and Institute of Eco-Chongming – sequence: 8 givenname: Yingjie surname: Cai fullname: Cai, Yingjie email: yingjiecai@wtu.edu.cn organization: Hubei Provincial Engineering Laboratory for Clean Production and High Value Utilization of Bio-Based Textile Materials, Wuhan Textile University – sequence: 9 givenname: George K. surname: Stylios fullname: Stylios, George K. email: g.stylios@hw.ac.uk organization: Research Institute for Flexible Materials, School of Textiles and Design, Heriot-Watt University – sequence: 10 givenname: Vincenzo surname: Naddeo fullname: Naddeo, Vincenzo email: vnaddeo@unisa.it organization: Sanitary Environmental Engineering Division (SEED), Department of Civil Engineering, University of Salerno |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37322139$$D View this record in MEDLINE/PubMed |
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Text.2021517279S7292S1:CAS:528:DC%2BB38XitlOms7bK10.1177/15280837211058213 YousefiSHTangCTafreshiHVPourdeyhimiBEmpirical model to simulate morphology of electrospun polycaprolactone matsJ. Appl. Polym.2019136482421:CAS:528:DC%2BC1MXhtlyisLbJ10.1002/app.48242 KhajehsharifiHPourbasheerETavallaliHSarviSSadeghiMThe comparison of partial least squares and principal component regression in simultaneous spectrophotometric determination of ascorbic acid, dopamine and uric acid in real samplesArab. J. Chem.201710S3451S34581:CAS:528:DC%2BC2cXktlGjsrk%3D10.1016/j.arabjc.2014.02.006 GuoYResearch progress, models and simulation of electrospinning technology: A reviewJ. Mater. Sci.202257581042022JMatS..57...58G1:CAS:528:DC%2BB3MXitlSmsrrN10.1007/s10853-021-06575-w34658418 TalukderMEChitosan-functionalized sodium alginate-based electrospun nanofiber membrane for As (III) removal from aqueous 36431_CR46 M Kano (36431_CR50) 2013; 46 Y Guo (36431_CR34) 2022; 57 WS Yeo (36431_CR33) 2017 SH Yousefi (36431_CR37) 2019; 136 S Palani (36431_CR59) 2008; 56 ALM Levada (36431_CR49) 2020; 135 C Wang (36431_CR35) 2006; 39 36431_CR52 MN Pervez (36431_CR47) 2022; 12 C Wang (36431_CR36) 2008; 49 H Ma (36431_CR9) 2012; 1 VR Viana (36431_CR39) 2020; 8 MN Pervez (36431_CR15) 2018; 11 MN Pervez (36431_CR5) 2022; 1 MN Pervez (36431_CR48) 2020; 262 X-H Qin (36431_CR10) 2006; 102 J Xue (36431_CR3) 2017; 50 K Hazama (36431_CR29) 2015; 146 PK Panda (36431_CR21) 2021; 51 MN Pervez (36431_CR12) 2022; 47 Z Li (36431_CR25) 2022; 133 P Škrabánek (36431_CR54) 2021; 9 MN Pervez (36431_CR8) 2018; 8 36431_CR42 D Visser (36431_CR26) 2023; 11 36431_CR23 ME Talukder (36431_CR6) 2022; 12 R Veerasamy (36431_CR58) 2011; 3 H Khajehsharifi (36431_CR27) 2017; 10 MN Pervez (36431_CR24) 2023 F Shafiq (36431_CR14) 2018; 25 K Thirugnanasambandham (36431_CR40) 2016; 14 K Nasouri (36431_CR20) 2012; 126 MN Pervez (36431_CR7) 2018; 8 M Ziabari (36431_CR38) 2010; 27 H Kaneko (36431_CR57) 2021; 35 WS Yeo (36431_CR53) 2021; 28 ACF Guimarães (36431_CR55) 2004; 44 Y Meng (36431_CR22) 2020; 17 WS Yeo (36431_CR45) 2020; 15 A Nasonova (36431_CR30) 2022; 411 JCY Ngu (36431_CR32) 2022; 13 36431_CR56 MN Pervez (36431_CR28) 2023; 9 MN Morshed (36431_CR13) 2020; 10 H He (36431_CR19) 2020; 194 L Lin (36431_CR16) 2022; 12 TF Thien (36431_CR44) 2022; 209 X Wang (36431_CR4) 2016; 12 R Ghelich (36431_CR18) 2019; 166 N Amiri (36431_CR41) 2018; 5 T Khatti (36431_CR17) 2019; 31 WS Yeo (36431_CR43) 2019; 58 36431_CR2 36431_CR1 ME Talukder (36431_CR11) 2021; 9 S Yang (36431_CR51) 2022; 52 X Zhang (36431_CR31) 2017; 104 |
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| Title | Electrospun nanofiber membrane diameter prediction using a combined response surface methodology and machine learning approach |
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