A critical review of machine learning for lignocellulosic ethanol production via fermentation route
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 fie...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Alpha Creation Enterprise
2023-06-01
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| Series: | Biofuel Research Journal |
| Subjects: | |
| Online Access: | https://www.biofueljournal.com/article_172024_15a66d1a6d40f2e9f81334c8f16d8f86.pdf |
| _version_ | 1797243611376517120 |
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| author | Ahmet Coşgun M. Erdem Günay Ramazan Yıldırım |
| author_facet | Ahmet Coşgun M. Erdem Günay Ramazan Yıldırım |
| author_sort | Ahmet Coşgun |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T18:57:52Z |
| format | Article |
| id | doaj.art-e772137597bb419d90def7e42320abe2 |
| institution | Directory Open Access Journal |
| issn | 2292-8782 |
| language | English |
| last_indexed | 2024-04-24T18:57:52Z |
| publishDate | 2023-06-01 |
| publisher | Alpha Creation Enterprise |
| record_format | Article |
| series | Biofuel Research Journal |
| spelling | doaj.art-e772137597bb419d90def7e42320abe22024-03-26T15:13:22ZengAlpha Creation EnterpriseBiofuel Research Journal2292-87822023-06-011021859187510.18331/BRJ2023.10.2.5172024A critical review of machine learning for lignocellulosic ethanol production via fermentation routeAhmet Coşgun0M. Erdem Günay1Ramazan Yıldırım2Department of Chemical Engineering, Boğaziçi University, 34342, Bebek-Istanbul, Turkey.Department of Energy Systems Engineering, Istanbul Bilgi University, 34060, Eyup-Istanbul, Turkey.Department of Chemical Engineering, Boğaziçi University, 34342, Bebek-Istanbul, Turkey.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.https://www.biofueljournal.com/article_172024_15a66d1a6d40f2e9f81334c8f16d8f86.pdfbiofuel productionbioethanol2nd generation feedstocklignocellulosic ethanolcellulosic ethanolmachine learning |
| spellingShingle | Ahmet Coşgun M. Erdem Günay Ramazan Yıldırım A critical review of machine learning for lignocellulosic ethanol production via fermentation route Biofuel Research Journal biofuel production bioethanol 2nd generation feedstock lignocellulosic ethanol cellulosic ethanol machine learning |
| title | A critical review of machine learning for lignocellulosic ethanol production via fermentation route |
| title_full | A critical review of machine learning for lignocellulosic ethanol production via fermentation route |
| title_fullStr | A critical review of machine learning for lignocellulosic ethanol production via fermentation route |
| title_full_unstemmed | A critical review of machine learning for lignocellulosic ethanol production via fermentation route |
| title_short | A critical review of machine learning for lignocellulosic ethanol production via fermentation route |
| title_sort | critical review of machine learning for lignocellulosic ethanol production via fermentation route |
| topic | biofuel production bioethanol 2nd generation feedstock lignocellulosic ethanol cellulosic ethanol machine learning |
| url | https://www.biofueljournal.com/article_172024_15a66d1a6d40f2e9f81334c8f16d8f86.pdf |
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