[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122237-en":3,"doc-seo-122237-105":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":4,"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},122237,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Application of machine learning techniques to predict biodiesel iodine value - Research report","Biodiesel is a promising alternative to fossil fuels, yet determining key fuel properties is often time-consuming and resource-intensive. This study investigates multiple machine learning models to predict iodine value (IV) from the fatty acid methyl esters (FAME) distribution. A dataset of 266 biodiesel examples from different feedstocks (1st to 3rd generation) is analyzed using leave-one-out methodology. Results show that double bonds and the FAME distribution are the most informative features, with XGBoost achieving an absolute mean error of 11.4 units. The work concludes that models require large, diverse training sets and that ANN/hold-out partitioning may overfit; leave-one-out best estimates performance.","Energy 292 (2024) 130638  \nContents lists available at ScienceDirect  \nEnergy  \njournal [homepage: www.elsevier.com/locate/energy](homepage: www.elsevier.com/locate/energy)  \n| Application of machine learning techniques to predict biodiesel iodine value\u003Cbr>G. Díez Valbuena a, A. García Tueroa, J. Díez b, E. Rodríguez a, A. Hern´andez Batteza, *\u003Cbr>a Department of Construction and Manufacturing Engineering, University of Oviedo, Pedro Puig Adam S/n, 33203, Gij´on, Spain b Artificial Intelligence Center, Universidad de Oviedo, Campus de Gij´on, Gij´on, 33203, Spain |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Handling Editor: Henrik Lund |  | Biodiesel is a good alternative to fossil fuels for conventional engines, but determining the properties of biodiesel can be a time-consuming and resource-intensive process. Therefore, the development of models capable of predicting these properties would be of great importance. In this work, different machine learning models were investigated for predicting the Iodine Value (IV) based on the distribution of fatty acid methyl esters (FAME). For this purpose, a database with 266 examples of biodiesel from different feedstocks (1st, 2nd and 3rd generation) was used along the leave-one-out methodology. The main results of the work are: the double bonds and the distribution of FAMEs are the best attributes for predicting IV and the XGBoost algorithm gives an absolute mean error of 11.4 units; the machine learning models for predicting biodiesel properties need to be trained on a large number and variety of biodiesel examples to better predict and generalize; the use of both ANNs and the hold-out approach of dividing the dataset into train/validate/test are not recommended due to the risk of overfitting and the algorithm’s dependence on which examples form each group given the currently available data. The leaveone-out method is most appropriate for estimating model performance. |\n| Keywords:\u003Cbr>Machine learning\u003Cbr>Prediction models Iodine value Biodiesel |  |  |\n\n1. Introduction  \nThe importance of clean energy generation is growing over the years. In 2015 the United Nations (UN) promoted an affordable, reliable, sustainable and modern energy transition for all and the decarbonization of fuels plays a crucial role in the process [1]. Around 99.8% of transport is currently powered by internal combustion engines (ICEs)  \n[2] and according to Senecal et al. [3], despite government policies, half of the vehicle fleet is still expected to be powered by ICEs by 2050. Therefore, one of the best options for reducing greenhouse gas emissions would be to find alternatives to fossil fuels that can be used in the current vehicle fleet. Considering the above facts, further research in the field of fuels such as biodiesel, it’s an appropriate approach [4].  \nBiodiesel can be produced by a variety of methods, but the most viable method is the transesterification of oils derived from a variety of feedstocks. In this process, lipids extracted from the feedstock react with methanol or ethanol in the presence of a catalyst (acid or basic) to convert triglycerides to fatty acid methyl or ethyl esters (FAME or FAEE). Depending on the origin of the feedstock, biodiesel can be classified as “Biodiesel of 1st generation” when derived from edible crops [5] and “Biodiesel of 2nd generation” when derived from non-edible  \nones [6,7]. These feedstocks have been widely discussed as they affect the food chain and the use of arable land [8]. As a result of this concern, biodiesel of 3rd generation [8,9] and 4th generation [10,11] can be obtained from microalgae resources. Microalgae are photosynthetic microorganisms that can grow in freshwater, seawater or wastewater, and therefore do not require arable land for their cultivation, and are great carbon sequestrators [12].  \nBiodiesel must meet the requirements outlined in the EN 14214:2012 + A2:2019 [13] and ASTM D6751 [14] standards. Both stan","cbCaimsgw2WvPJT2","https://ap.wps.com/l/cbCaimsgw2WvPJT2","pdf",5252766,1,11,"English","en",105,"# Introduction\n## Biodiesel feedstocks and classification\n## Standards and need for predictive modeling\n## Iodine value (IV) relevance","[{\"question\":\"Why is predicting biodiesel iodine value (IV) important?\",\"answer\":\"IV reflects the proportion of unsaturated constituents in biodiesel that influence oxidative stability and the cold filter plugging point.\"},{\"question\":\"Which inputs were found to be most predictive of IV?\",\"answer\":\"The distribution of fatty acid methyl esters (FAME) and the double-bond information are identified as the best attributes for predicting IV.\"},{\"question\":\"What modeling approach performed best and what accuracy was reported?\",\"answer\":\"XGBoost provided the best results, with an absolute mean error of 11.4 units under the leave-one-out evaluation.\"}]","Application of machine learning techniques to predict biodiesel iodine value - Research report | PDF",1785809566,28,{"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},"application-of-machine-learning-techniques-to-predict-biodiesel-iodine-value-research-report","",{"@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/application-of-machine-learning-techniques-to-predict-biodiesel-iodine-value-research-report/122237/",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","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is predicting biodiesel iodine value (IV) important?","Question",{"text":75,"@type":76},"IV reflects the proportion of unsaturated constituents in biodiesel that influence oxidative stability and the cold filter plugging point.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which inputs were found to be most predictive of IV?",{"text":80,"@type":76},"The distribution of fatty acid methyl esters (FAME) and the double-bond information are identified as the best attributes for predicting IV.",{"name":82,"@type":73,"acceptedAnswer":83},"What modeling approach performed best and what accuracy was reported?",{"text":84,"@type":76},"XGBoost provided the best results, with an absolute mean error of 11.4 units under the leave-one-out evaluation.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]