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Pervez MN, Yeo WS, Mishu MMR, Talukder ME, Roy H, Islam MS, Zhao Y, Cai Y, Stylios GK, Naddeo V. Electrospun nanofiber membrane diameter prediction using a combined response surface methodology and machine learning approach. Sci Rep 2023; 13:9679. [PMID: 37322139 DOI: 10.1038/s41598-023-36431-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 06/03/2023] [Indexed: 06/17/2023] Open
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.
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Affiliation(s)
- Md Nahid Pervez
- Hubei Provincial Engineering Laboratory for Clean Production and High Value Utilization of Bio-Based Textile Materials, Wuhan Textile University, Wuhan, 430200, China
- Sanitary Environmental Engineering Division (SEED), Department of Civil Engineering, University of Salerno, 84084, Fisciano, Italy
| | - Wan Sieng Yeo
- Department of Chemical and Energy Engineering, Faculty of Engineering and Science, Curtin University Malaysia, CDT 250, 98009, Miri, Sarawak, Malaysia
| | - Mst Monira Rahman Mishu
- Faculty of Nutrition and Food Science, Patuakhali Science and Technology University, Patuakhali, 8602, Bangladesh
| | - Md Eman Talukder
- Hubei Provincial Engineering Laboratory for Clean Production and High Value Utilization of Bio-Based Textile Materials, Wuhan Textile University, Wuhan, 430200, China
| | - Hridoy Roy
- Department of Chemical Engineering, Bangladesh University of Engineering and Technology, Dhaka, 1000, Bangladesh
| | - Md Shahinoor Islam
- Department of Chemical Engineering, Bangladesh University of Engineering and Technology, Dhaka, 1000, Bangladesh
| | - Yaping Zhao
- 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, Shanghai, 200241, China
| | - Yingjie Cai
- Hubei Provincial Engineering Laboratory for Clean Production and High Value Utilization of Bio-Based Textile Materials, Wuhan Textile University, Wuhan, 430200, China.
| | - George K Stylios
- Research Institute for Flexible Materials, School of Textiles and Design, Heriot-Watt University, Galashiels, TD1 3HF, UK.
| | - Vincenzo Naddeo
- Sanitary Environmental Engineering Division (SEED), Department of Civil Engineering, University of Salerno, 84084, Fisciano, Italy.
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Quality Prediction Model of KICA-JITL-LWPLS Based on Wavelet Kernel Function. Processes (Basel) 2022. [DOI: 10.3390/pr10081562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
To obtain quality variables that cannot be measured in real time during the production process but reflect information on the quality of the final product, the batch production process has the characteristics of a strong time-varying nature, non-Gaussian data distribution and high nonlinearity. A locally weighted partial least squares regression quality prediction model (KICA-JITL-LWPLS), based on wavelet kernel function independent meta-analysis with immediate learning, is proposed. The model first measures the similarity between the normalized input data and the historical data and assigns the input data to the group of historical data with high similarity to it, based on the posterior probability of the Bayesian classifier; subsequently, wavelet kernel functions are selected and kernel learning methods are introduced into the independent meta-analysis algorithm. An independent meta-analysis, based on the wavelet kernel function, is performed on the classified input data to obtain probabilistically significant independent sets of variables. Finally, a real-time learning-based LWPLS regression analysis is performed on this variable set to construct a local prediction model for the current sample by calculating the similarity between the local input data. The local predictions from the PLS output are fused with the posterior probability output from the Bayesian classifier to produce the final prediction. The method was used to predict the product concentration and bacteriophage concentration during penicillin fermentation through a simulation platform. The prediction results were basically consistent with the real values, proving that the proposed KICA-JITL-LWPLS quality prediction model, based on wavelet kernel functions, has reliable prediction results.
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Thien TF, Yeo WS. A comparative study between PCR, PLSR, and LW-PLS on the predictive performance at different data splitting ratios. CHEM ENG COMMUN 2021. [DOI: 10.1080/00986445.2021.1957853] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Teck Fu Thien
- Department of Chemical Engineering, Curtin University, Malaysia, Miri, Sarawak, Malaysia
| | - Wan Sieng Yeo
- Department of Chemical Engineering, Curtin University, Malaysia, Miri, Sarawak, Malaysia
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Yeo WS, Saptoro A, Kumar P. Adaptive Soft Sensor Development for Non-Gaussian and Nonlinear Processes. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b03821] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Wan Sieng Yeo
- Chemical Engineering Department, Curtin University Malaysia, CDT 250, Miri 98009, Sarawak, Malaysia
| | - Agus Saptoro
- Chemical Engineering Department, Curtin University Malaysia, CDT 250, Miri 98009, Sarawak, Malaysia
| | - Perumal Kumar
- Chemical Engineering Department, Curtin University Malaysia, CDT 250, Miri 98009, Sarawak, Malaysia
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