A Review of Application of Experimental Design Techniques Related to Dark Fermentative Hydrogen Production
The current review purpose is to present a general overview of different experimental design methods that are applied to investigate the effect of key factors on dark fermentation and are efficient in predicting the experimental data for biological hydrogen production. The methods of two levels full...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Materials and Energy Research Center (MERC)
2020-04-01
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| Series: | Journal of Renewable Energy and Environment |
| Subjects: | |
| Online Access: | https://www.jree.ir/article_106767_ea4ad4d6d3f350c5715fc66ca4e686b7.pdf |
| _version_ | 1797790460015542272 |
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| author | Fatemeh Boshagh Khosrow Rostami |
| author_facet | Fatemeh Boshagh Khosrow Rostami |
| author_sort | Fatemeh Boshagh |
| collection | DOAJ |
| description | The current review purpose is to present a general overview of different experimental design methods that are applied to investigate the effect of key factors on dark fermentation and are efficient in predicting the experimental data for biological hydrogen production. The methods of two levels full and fractional factorials, Plackett–Burman, and Taguchi were employed for screening the most important factors in dark fermentation. The techniques of central composite, Box–Behnken, Taguchi, and one factor at a time for optimization of the dark fermentation were extensively used. Papers on the three levels full and fractional factorials, artificial neural network coupled with genetic algorithm, simplex, and D-optimal for the optimization of the dark fermentation are limited, and no paper on the Dohlert design has been reported to date. The artificial neural network coupled with genetic algorithm is a more suitable method than the RSM technique for the optimization of dark fermentation. Literature shows that the optimization of critical factors plays a significant role in dark fermentation and is useful to improve the hydrogen production rate and hydrogen yield. |
| first_indexed | 2024-03-13T02:04:55Z |
| format | Article |
| id | doaj.art-1b6c7c0744274a098ffe7ab3bfc741f3 |
| institution | Directory Open Access Journal |
| issn | 2423-5547 2423-7469 |
| language | English |
| last_indexed | 2024-03-13T02:04:55Z |
| publishDate | 2020-04-01 |
| publisher | Materials and Energy Research Center (MERC) |
| record_format | Article |
| series | Journal of Renewable Energy and Environment |
| spelling | doaj.art-1b6c7c0744274a098ffe7ab3bfc741f32023-07-01T09:03:04ZengMaterials and Energy Research Center (MERC)Journal of Renewable Energy and Environment2423-55472423-74692020-04-0172274210.30501/jree.2020.106767106767A Review of Application of Experimental Design Techniques Related to Dark Fermentative Hydrogen ProductionFatemeh Boshagh0Khosrow Rostami1Department of Chemical Technologies, Iranian Research Organization for Science and Technology (IROST), P. O. Box: 3353-5111, Tehran, Iran.Department of Biotechnology, Iranian Research Organization for Science and Technology (IROST), P. O. Box: 3353-5111, Tehran, Iran.The current review purpose is to present a general overview of different experimental design methods that are applied to investigate the effect of key factors on dark fermentation and are efficient in predicting the experimental data for biological hydrogen production. The methods of two levels full and fractional factorials, Plackett–Burman, and Taguchi were employed for screening the most important factors in dark fermentation. The techniques of central composite, Box–Behnken, Taguchi, and one factor at a time for optimization of the dark fermentation were extensively used. Papers on the three levels full and fractional factorials, artificial neural network coupled with genetic algorithm, simplex, and D-optimal for the optimization of the dark fermentation are limited, and no paper on the Dohlert design has been reported to date. The artificial neural network coupled with genetic algorithm is a more suitable method than the RSM technique for the optimization of dark fermentation. Literature shows that the optimization of critical factors plays a significant role in dark fermentation and is useful to improve the hydrogen production rate and hydrogen yield.https://www.jree.ir/article_106767_ea4ad4d6d3f350c5715fc66ca4e686b7.pdfbiohydrogen productiondark fermentationexperimental designoptimization |
| spellingShingle | Fatemeh Boshagh Khosrow Rostami A Review of Application of Experimental Design Techniques Related to Dark Fermentative Hydrogen Production Journal of Renewable Energy and Environment biohydrogen production dark fermentation experimental design optimization |
| title | A Review of Application of Experimental Design Techniques Related to Dark Fermentative Hydrogen Production |
| title_full | A Review of Application of Experimental Design Techniques Related to Dark Fermentative Hydrogen Production |
| title_fullStr | A Review of Application of Experimental Design Techniques Related to Dark Fermentative Hydrogen Production |
| title_full_unstemmed | A Review of Application of Experimental Design Techniques Related to Dark Fermentative Hydrogen Production |
| title_short | A Review of Application of Experimental Design Techniques Related to Dark Fermentative Hydrogen Production |
| title_sort | review of application of experimental design techniques related to dark fermentative hydrogen production |
| topic | biohydrogen production dark fermentation experimental design optimization |
| url | https://www.jree.ir/article_106767_ea4ad4d6d3f350c5715fc66ca4e686b7.pdf |
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