{"title":"A critical review of machine learning for lignocellulosic ethanol production via fermentation route","authors":"Ahmet Çoşgun, M. E. Günay, R. Yıldırım","doi":"10.18331/brj2023.10.2.5","DOIUrl":null,"url":null,"abstract":"In this work, machine learning (ML) applications in lignocellulosic bioethanol production were reviewed. First, the pretreatment-hydrolysis-fermentation route, the most commonly studied alternative, was summarized. Next, a bibliometric analysis was performed to identify the current trends in the field; it was found that ML applications in the field are not only increasing but also expanding their relative share in publications, with bioethanol seeming to be the most frequently researched topic while biochar and biogas are also receiving increased attention in recent years. Then, the implementation of ML for lignocellulosic bioethanol production via this route was reviewed in depth. It was observed that artificial neural network (ANN) is the most commonly used algorithm (appeared in almost 90% of articles), followed by response surface methodology (RSM) (in about 25% of articles) and random forest (RF) (in about 10% of articles). Bioethanol concentration is the most common output variable in the fermentation step, while fermentable sugar and glucose concentration are studied most in hydrolysis. The datasets are usually small, while the fitnesses of the models (R2) are usually high in the papers reviewed. Finally, a perspective for future studies, mostly considering improving data availability, was provided.","PeriodicalId":46938,"journal":{"name":"Biofuel Research Journal-BRJ","volume":null,"pages":null},"PeriodicalIF":14.4000,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biofuel Research Journal-BRJ","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18331/brj2023.10.2.5","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 5
Abstract
In this work, machine learning (ML) applications in lignocellulosic bioethanol production were reviewed. First, the pretreatment-hydrolysis-fermentation route, the most commonly studied alternative, was summarized. Next, a bibliometric analysis was performed to identify the current trends in the field; it was found that ML applications in the field are not only increasing but also expanding their relative share in publications, with bioethanol seeming to be the most frequently researched topic while biochar and biogas are also receiving increased attention in recent years. Then, the implementation of ML for lignocellulosic bioethanol production via this route was reviewed in depth. It was observed that artificial neural network (ANN) is the most commonly used algorithm (appeared in almost 90% of articles), followed by response surface methodology (RSM) (in about 25% of articles) and random forest (RF) (in about 10% of articles). Bioethanol concentration is the most common output variable in the fermentation step, while fermentable sugar and glucose concentration are studied most in hydrolysis. The datasets are usually small, while the fitnesses of the models (R2) are usually high in the papers reviewed. Finally, a perspective for future studies, mostly considering improving data availability, was provided.
期刊介绍:
Biofuel Research Journal (BRJ) is a leading, peer-reviewed academic journal that focuses on high-quality research in the field of biofuels, bioproducts, and biomass-derived materials and technologies. The journal's primary goal is to contribute to the advancement of knowledge and understanding in the areas of sustainable energy solutions, environmental protection, and the circular economy. BRJ accepts various types of articles, including original research papers, review papers, case studies, short communications, and hypotheses. The specific areas covered by the journal include Biofuels and Bioproducts, Biomass Valorization, Biomass-Derived Materials for Energy and Storage Systems, Techno-Economic and Environmental Assessments, Climate Change and Sustainability, and Biofuels and Bioproducts in Circular Economy, among others. BRJ actively encourages interdisciplinary collaborations among researchers, engineers, scientists, policymakers, and industry experts to facilitate the adoption of sustainable energy solutions and promote a greener future. The journal maintains rigorous standards of peer review and editorial integrity to ensure that only impactful and high-quality research is published. Currently, BRJ is indexed by several prominent databases such as Web of Science, CAS Databases, Directory of Open Access Journals, Scimago Journal Rank, Scopus, Google Scholar, Elektronische Zeitschriftenbibliothek EZB, et al.