Using machine learning to predict the performance of a cross-flow ultrafiltration membrane in xylose reductase separation
This study provides a new perspective for xylose reductase enzyme separation from the reaction mixtures—obtained in the production of xylitol—by means of machine learning technique for large-scale production. Two types of machine learning models, including an adaptive neuro-fuzzy inference system ba...
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| Format: | Article |
| Language: | English |
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MDPI
2023
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| Online Access: | http://umpir.ump.edu.my/id/eprint/37619/1/Using%20machine%20learning%20to%20predict%20the%20performance%20of%20a%20cross-flow%20ultrafiltration.pdf |
| _version_ | 1796995717607194624 |
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| author | Salehi, Reza Krishnan, Santhana Mohd Nasrullah, Zulkifli Chaiprapat, Sumate |
| author_facet | Salehi, Reza Krishnan, Santhana Mohd Nasrullah, Zulkifli Chaiprapat, Sumate |
| author_sort | Salehi, Reza |
| collection | UMP |
| description | This study provides a new perspective for xylose reductase enzyme separation from the reaction mixtures—obtained in the production of xylitol—by means of machine learning technique for large-scale production. Two types of machine learning models, including an adaptive neuro-fuzzy inference system based on grid partitioning of the input space and a boosted regression tree were developed, validated, and tested. The models’ inputs were cross-flow velocity, transmembrane pressure, and filtration time, whereas the membrane permeability (called membrane flux) and xylitol concentration were considered as the outputs. According to the results, the boosted regression tree model demonstrated the highest predictive performance in forecasting the membrane flux and the amount of xylitol produced with a coefficient of determination of 0.994 and 0.967, respectively, against 0.985 and 0.946 for the grid partitioning-based adaptive neuro-fuzzy inference system, 0.865 and 0.820 for the best nonlinear regression picked from among 143 different equations, and 0.815 and 0.752 for the linear regression. The boosted regression tree modeling approach demonstrated a superior capability of predictive accuracy of the critical separation performances in the enzymatic-based cross-flow ultrafiltration membrane for xylitol synthesis. |
| first_indexed | 2024-03-06T13:06:24Z |
| format | Article |
| id | UMPir37619 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T13:06:24Z |
| publishDate | 2023 |
| publisher | MDPI |
| record_format | dspace |
| spelling | UMPir376192023-07-13T03:03:16Z http://umpir.ump.edu.my/id/eprint/37619/ Using machine learning to predict the performance of a cross-flow ultrafiltration membrane in xylose reductase separation Salehi, Reza Krishnan, Santhana Mohd Nasrullah, Zulkifli Chaiprapat, Sumate T Technology (General) TA Engineering (General). Civil engineering (General) This study provides a new perspective for xylose reductase enzyme separation from the reaction mixtures—obtained in the production of xylitol—by means of machine learning technique for large-scale production. Two types of machine learning models, including an adaptive neuro-fuzzy inference system based on grid partitioning of the input space and a boosted regression tree were developed, validated, and tested. The models’ inputs were cross-flow velocity, transmembrane pressure, and filtration time, whereas the membrane permeability (called membrane flux) and xylitol concentration were considered as the outputs. According to the results, the boosted regression tree model demonstrated the highest predictive performance in forecasting the membrane flux and the amount of xylitol produced with a coefficient of determination of 0.994 and 0.967, respectively, against 0.985 and 0.946 for the grid partitioning-based adaptive neuro-fuzzy inference system, 0.865 and 0.820 for the best nonlinear regression picked from among 143 different equations, and 0.815 and 0.752 for the linear regression. The boosted regression tree modeling approach demonstrated a superior capability of predictive accuracy of the critical separation performances in the enzymatic-based cross-flow ultrafiltration membrane for xylitol synthesis. MDPI 2023-03 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/37619/1/Using%20machine%20learning%20to%20predict%20the%20performance%20of%20a%20cross-flow%20ultrafiltration.pdf Salehi, Reza and Krishnan, Santhana and Mohd Nasrullah, Zulkifli and Chaiprapat, Sumate (2023) Using machine learning to predict the performance of a cross-flow ultrafiltration membrane in xylose reductase separation. Sustainability (Switzerland), 15 (5). pp. 1-27. ISSN 2071-1050. (Published) https://doi.org/10.3390/su15054245 https://doi.org/10.3390/su15054245 |
| spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) Salehi, Reza Krishnan, Santhana Mohd Nasrullah, Zulkifli Chaiprapat, Sumate Using machine learning to predict the performance of a cross-flow ultrafiltration membrane in xylose reductase separation |
| title | Using machine learning to predict the performance of a cross-flow ultrafiltration membrane in xylose reductase separation |
| title_full | Using machine learning to predict the performance of a cross-flow ultrafiltration membrane in xylose reductase separation |
| title_fullStr | Using machine learning to predict the performance of a cross-flow ultrafiltration membrane in xylose reductase separation |
| title_full_unstemmed | Using machine learning to predict the performance of a cross-flow ultrafiltration membrane in xylose reductase separation |
| title_short | Using machine learning to predict the performance of a cross-flow ultrafiltration membrane in xylose reductase separation |
| title_sort | using machine learning to predict the performance of a cross flow ultrafiltration membrane in xylose reductase separation |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/37619/1/Using%20machine%20learning%20to%20predict%20the%20performance%20of%20a%20cross-flow%20ultrafiltration.pdf |
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