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Pervez MN, Yeo WS, Lin L, Xiong X, Naddeo V, Cai Y. Optimization and prediction of the cotton fabric dyeing process using Taguchi design-integrated machine learning approach. Sci Rep 2023; 13:12363. [PMID: 37524835 PMCID: PMC10390507 DOI: 10.1038/s41598-023-39528-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 07/26/2023] [Indexed: 08/02/2023] Open
Abstract
The typical textile dyeing process calls for a wide range of operational parameters, and it has always been difficult to pinpoint which of these qualities is the most important in dyeing performance. Consequently, this research used a combined design of experiments and machine learning prediction models' method to offer a sustainable and beneficial reactive cotton fabric dyeing process. To be more precise, we built a least square support vector regression (LSSVR) model based on Taguchi's statistical orthogonal design (L27) to predict exhaustion percentage (E%), fixation rate (F%), and total fixation efficiency (T%) and color strength (K/S) in the reactive cotton dyeing process. The model's prediction accuracy was assessed using many measures, including root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2). Principal component regression (PCR), partial least square regression (PLSR), and fuzzy modelling were some of the other types of regression models used to compare results. Our findings reveal that the LSSVR model greatly outperformed competing models in predicting the E%, F%, T%, and K/S. This is shown by the LSSVR model's much smaller RMSE and MAE values. Overall, it provided the highest possible R2 values, which reached 0.9819.
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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
- School of Computing, Huanggang Normal University, Huanggang, 438000, 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
| | - Lina Lin
- Hubei Provincial Engineering Laboratory for Clean Production and High Value Utilization of Bio-Based Textile Materials, Wuhan Textile University, Wuhan, 430200, China.
- State Key Laboratory of New Textile Materials and Advanced Processing Technologies, Wuhan Textile University, Wuhan, 430073, China.
| | - Xiaorong Xiong
- School of Computing, Huanggang Normal University, Huanggang, 438000, China.
| | - Vincenzo Naddeo
- Sanitary Environmental Engineering Division (SEED), Department of Civil Engineering, University of Salerno, 84084, Fisciano, Italy.
| | - Yingjie Cai
- Hubei Provincial Engineering Laboratory for Clean Production and High Value Utilization of Bio-Based Textile Materials, Wuhan Textile University, Wuhan, 430200, China
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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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Pervez MN, Yeo WS, Shafiq F, Jilani MM, Sarwar Z, Riza M, Lin L, Xiong X, Naddeo V, Cai Y. Sustainable fashion: Design of the experiment assisted machine learning for the environmental-friendly resin finishing of cotton fabric. Heliyon 2023; 9:e12883. [PMID: 36691543 PMCID: PMC9860286 DOI: 10.1016/j.heliyon.2023.e12883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 01/06/2023] [Accepted: 01/06/2023] [Indexed: 01/11/2023] Open
Abstract
Given the carcinogenic properties of formaldehyde-based chemicals, an alternative method for resin-finishing cotton textiles is urgently needed. Therefore, the primary objective of this study is to introduce a sustainable resin-finishing process for cotton fabric via an industrial procedure. For this purpose, Bluesign® approved a formaldehyde-free Knittex RCT® resin was used, and the process parameters were designed and optimized according to the Taguchi L27 method. XRD analysis confirmed the crosslinking formation between resin and neighboring molecules of cotton fabric, as no change in the cellulose crystallization phase. Several machine learning models were built in a sequence to predict the crease recovery angle (CRA), tearing strength (TE) and whiteness index (WI). Assessment of modelling was evaluated through the use of various metrics such as root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2). Results were compared to those from other regression models, such as principal component regression (PCR), partial least squares regression (PLSR), and fuzzy modelling. Based on the results of our research, the LSSVR model predicted the CRA, TE, and WI with substantially more accuracy than other models, as shown by the fact that its RMSE and MAE values were significantly lower. In addition, it offered the greatest possible R2 values, reaching up to 0.9627.
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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,School of Computing, Huanggang Normal University, Huanggang 438000, China,Sanitary Environmental Engineering Division (SEED), Department of Civil Engineering, University of Salerno, Fisciano 84084, Italy
| | - Wan Sieng Yeo
- Department of Chemical and Energy Engineering, Faculty of Engineering and Science, Curtin University Malaysia, CDT 250, 98009 Miri, Sarawak, Malaysia
| | - Faizan Shafiq
- Hubei Provincial Engineering Laboratory for Clean Production and High Value Utilization of Bio-based Textile Materials, Wuhan Textile University, Wuhan 430200, China
| | - Muhammad Munib Jilani
- Department of Textile Processing, National Textile University, Faisalabad, Punjab 37610, Pakistan
| | - Zahid Sarwar
- School of Engineering and Technology, National Textile University, Faisalabad, Punjab 37610, Pakistan
| | - Mumtahina Riza
- Department of Applied Ecology, North Carolina State University, Campus Box 7617 Raleigh, NC 27695-7617, USA
| | - Lina Lin
- Hubei Provincial Engineering Laboratory for Clean Production and High Value Utilization of Bio-based Textile Materials, Wuhan Textile University, Wuhan 430200, China,Corresponding author. .
| | - Xiaorong Xiong
- School of Computing, Huanggang Normal University, Huanggang 438000, China,Corresponding author. .
| | - Vincenzo Naddeo
- Sanitary Environmental Engineering Division (SEED), Department of Civil Engineering, University of Salerno, Fisciano 84084, Italy,Corresponding author. .
| | - Yingjie Cai
- Hubei Provincial Engineering Laboratory for Clean Production and High Value Utilization of Bio-based Textile Materials, Wuhan Textile University, Wuhan 430200, China
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Wang P, Yin Y, Deng X, Bo Y, Shao W. Semi-supervised echo state network with temporal-spatial graph regularization for dynamic soft sensor modeling of industrial processes. ISA TRANSACTIONS 2022; 130:306-315. [PMID: 35473770 DOI: 10.1016/j.isatra.2022.04.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 04/06/2022] [Accepted: 04/06/2022] [Indexed: 06/14/2023]
Abstract
Echo state network (ESN) has been successfully applied to industrial soft sensor field because of its strong nonlinear and dynamic modeling capability. Nevertheless, the traditional ESN is intrinsically a supervised learning technique, which only depends on labeled samples, but omits a large number of unlabeled samples. In order to eliminate this limitation, this work proposes a semi-supervised ESN method assisted by a temporal-spatial graph regularization (TSG-SSESN) for constructing soft sensor model with all the available samples. Firstly, the traditional supervised ESN is enhanced to construct the semi-supervised ESN (SSESN) model by integrating both unlabeled and labeled samples in the reservoir computing procedure. The SSESN computes the reservoir states under high sampling rate for better process dynamic information mining. Furthermore, the SSESN's output optimization objective is modified by applying the local adjacency graph of all training samples as a regularization term. Especially, in view of the dynamic data characteristic, a temporal-spatial graph is constructed by considering both the temporal relationship and the spatial distances. The applications to a debutanizer column process and a wastewater treatment plant demonstrate that the TSG-SSESN model can build much smoother model and has better generalization capability than the basic ESN models in terms of soft sensor prediction results.
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Affiliation(s)
- Ping Wang
- College of Control Science and Engineering, China University of Petroleum, Qingdao 266580, China
| | - Yichao Yin
- College of Control Science and Engineering, China University of Petroleum, Qingdao 266580, China
| | - Xiaogang Deng
- College of Control Science and Engineering, China University of Petroleum, Qingdao 266580, China.
| | - Yingchun Bo
- College of Control Science and Engineering, China University of Petroleum, Qingdao 266580, China
| | - Weiming Shao
- College of New Energy, China University of Petroleum, Qingdao 266580, China
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Qiu K, Wang J, Zhou X, Guo Y, Wang R. Soft Sensor Framework Based on Semisupervised Just-in-Time Relevance Vector Regression for Multiphase Batch Processes with Unlabeled Data. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c03806] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Kepeng Qiu
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Jianlin Wang
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Xinjie Zhou
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Yongqi Guo
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Rutong Wang
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
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Joshi T, Goyal V, Kodamana H. A Novel Dynamic Just-in-Time Learning Framework for Modeling of Batch Processes. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c02979] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Tanuja Joshi
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi 110016, India
| | - Vishesh Goyal
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi 110016, India
| | - Hariprasad Kodamana
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi 110016, India
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Yeo WS, Saptoro A, Kumar P. Missing data treatment for locally weighted partial least square‐based modelling: A comparative study. ASIA-PAC J CHEM ENG 2020. [DOI: 10.1002/apj.2422] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Affiliation(s)
- Wan Sieng Yeo
- Department of Chemical EngineeringCurtin University Malaysia Miri Malaysia
| | - Agus Saptoro
- Department of Chemical EngineeringCurtin University Malaysia Miri Malaysia
| | - Perumal Kumar
- Department of Chemical EngineeringCurtin University Malaysia Miri Malaysia
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