Enhancing Nanocomposite Filtration Membranes: Refined SVM Approach for Precise Estimation of Permeate Flux and Foulant Rejection
| Authors | P.M. Yawalkar1, P. William2 , V.M. Tidake3, P.M. Patare4, P.B. Khatkale5, , A.A. Khatri6, S.S. Ingle4 |
| Affiliations |
1Department of Computer Engineering, MET's Institute of Engineering, Nashik, India 2Department of Information Technology, Sanjivani College Engineering, Kopargaon, SPPU, Pune, India 3Department of MBA, Sanjivani College of Engineering, Kopargaon, SPPU, Pune, India 4Department of Mechanical Engineering, Sanjivani College of Engineering Kopargaon, SPPU, Pune, India 5Sanjivani University, Kopargaon, MH, India 6Department of Computer Engineering, Jaihind College of Engineering, Kuran, SPPU, Pune, MH, India |
| Е-mail | praveenkhatkale@gmail.com |
| Issue | Volume 16, Year 2024, Number 3 |
| Dates | Received 17 April 2024; revised manuscript received 23 June 2024; published online 28 June 2024 |
| Citation | P.M. Yawalkar, P. William, V.M. Tidake, et al., J. Nano- Electron. Phys. 16 No 3, 03016 (2024) |
| DOI | https://doi.org/10.21272/jnep.16(3).03016 |
| PACS Number(s) | 74.25.Qt, 78.67.Sc |
| Keywords | Thin-film nanocomposite (TFN), Machine learning, Permeate flux, Foulant rejection, Refined Support Vector Machine (RSVM). |
| Annotation |
The nanocomposite filtration membranes have emerged as potential water purification and separation technologies. However, reliable estimation of foulant rejection and permeate flux remains difficult due to the complicated interaction of many components. Traditional modeling techniques fail to capture the complex dynamics at work. In this paper, we provide a Refined Support Vector Machine (RSVM) strategy to solve this issue and increase the performance of nanocomposite filtration membranes. To normalize the features, the data are pre-processed using min-max normalization. Data features like foulant rejection rates, permeate flux values, membrane features, and experimental setup are displayed. Furthermore, the proposed RSVM to determine the best input factors for the effectiveness of each nanocomposite membrane. Due to the strong resilience of RSVM and the great generalization ability of the ML model, the obtained results demonstrated that the RSVM model's prediction efficiency (R2 = 0.995) outperformed the mathematical model in terms of prediction performance. To conduct training, validation and testing for this work, we employed statistical data including 764 samples of the input variables (five) and output variables (two). The RSVM approach provides a dependable and effective way to forecast membrane fouling and water filtration by predicting foulant rejection and permeate flux. |
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