[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125651-en":3,"doc-seo-125651-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125651,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Using Machine Learning to Predict the Performance of a Cross-Flow Ultrafiltration Membrane in Xylose Reductase Separation","This study applies machine learning to separate xylose reductase enzyme from xylitol production reaction mixtures for large-scale manufacturing. Two modeling approaches are developed, validated, and tested: an adaptive neuro-fuzzy inference system with grid partitioning of the input space and a boosted regression tree. Inputs include cross-flow velocity, transmembrane pressure, and filtration time, while outputs are membrane flux and xylitol concentration. Results show the boosted regression tree achieves the highest predictive performance, supporting improved forecasting of separation performance critical to enzymatic cross-flow ultrafiltration for xylitol synthesis.","sustainability   \nArticle  \nUsing Machine Learning to Predict the Performance of a Cross-Flow Ultrafiltration Membrane in Xylose Reductase Separation  \nReza Salehi 1, Santhana Krishnan 1, Mohd Nasrullah 2 and Sumate Chaiprapat 1,3, *  \nCitation: Salehi, R.; Krishnan, S.; Nasrullah, M.; Chaiprapat, S. Using Machine Learning to Predict the Performance of a Cross-Flow Ultrafiltration Membrane in Xylose Reductase Separation. Sustainability 2023, 15, 4245. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/su15054245](10.3390/su15054245)  \nAcademic Editors: Andreas Kanavos, Lidietta Giorno and Emmanouil Papaioannou  \nReceived: 17 December 2022  \nRevised: 19 February 2023  \nAccepted: 22 February 2023  \nPublished: 27 February 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Civil and Environmental Engineering, Faculty of Engineering, Prince of Songkla University, Hat Yai Campus, Songkhla 90110, Thailand  \n2 Faculty of Civil Engineering Technology, University of Malaysia Pahang, Lebuhraya Tun Razak, Gambang 26300, Malaysia  \n3 PSU Energy Systems Research Institute, Research and Development Office, Prince of Songkla University, Songkhla 90110, Thailand  \n* Correspondence: [sumate.ch@psu.ac.th](sumate.ch@psu.ac.th)  \nAbstract: This study provides a new perspective for xylose reductase enzyme separation from thereaction 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.  \nKeywords: adaptive neuro-fuzzy inference system; boosted regression trees; cross-flow ultrafiltration; grid partitioning; (non)linear regression; xylitol; xylose reductase  \n1. Introduction  \nXylose reductase (XR) is a member of the aldose reductase or aldehyde reductase (ALR) family (EC [1.1.1.21](1.1.1.21)), which belongs to the aldo-keto reductase (AKR) superfamily of enzymes [1,2] . It catalyzes the reduction of xylose (found in hemicellulose hydrolysates from lignocellulosic biomass) to xylitol [3], which has enormous applications in the pharmaceutical, food, and beverage industries [4] as its global market size is expected to increase from USD 1 billion in 2022 [5] to USD 1.37 billion by 2025 [6] .  \nXR has been reported to be found in the cytoplasm of a wide variety of microorganisms, including bacteria, molds, algae, and yeasts [5,7,8] . However, as has appeared in the literature, only yeast species have been extensively studied. Some examples of yeast XRs include Candida shehatae [9], Candida tropicalis [10–12], Candida guilliermondii [13,14], Pichia fermentans [6], Chaetomi","cbCaiiOKDCNJFcb4","https://ap.wps.com/l/cbCaiiOKDCNJFcb4","pdf",5038491,1,27,"English","en",105,"# Abstract\n# Introduction\n## Xylose reductase function and sources\n## Downstream separation challenge and role of membranes\n## Cross-flow ultrafiltration and key operating parameters","[{\"question\":\"What is the main goal of this study?\",\"answer\":\"To predict performance of a cross-flow ultrafiltration membrane for separating xylose reductase from xylitol production reaction mixtures using machine learning for large-scale production.\"},{\"question\":\"Which machine learning models are compared in the study?\",\"answer\":\"An adaptive neuro-fuzzy inference system based on grid partitioning and a boosted regression tree are developed, validated, and tested.\"},{\"question\":\"What inputs and outputs are used for the predictive models?\",\"answer\":\"Inputs are cross-flow velocity, transmembrane pressure, and filtration time; outputs are membrane permeability (membrane flux) and xylitol concentration.\"}]","Using Machine Learning to Predict the Performance of a Cross-Flow Ultrafiltration Membrane in Xylose Reductase Separation | PDF",1785900444,68,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"using-machine-learning-to-predict-the-performance-of-a-cross-flow-ultrafiltration-membrane-in-xylose-reductase-separation","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/using-machine-learning-to-predict-the-performance-of-a-cross-flow-ultrafiltration-membrane-in-xylose-reductase-separation/125651/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of this study?","Question",{"text":75,"@type":76},"To predict performance of a cross-flow ultrafiltration membrane for separating xylose reductase from xylitol production reaction mixtures using machine learning for large-scale production.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are compared in the study?",{"text":80,"@type":76},"An adaptive neuro-fuzzy inference system based on grid partitioning and a boosted regression tree are developed, validated, and tested.",{"name":82,"@type":73,"acceptedAnswer":83},"What inputs and outputs are used for the predictive models?",{"text":84,"@type":76},"Inputs are cross-flow velocity, transmembrane pressure, and filtration time; 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