Adaptive Network Fuzzy Inference System and Particle Swarm Optimization of Biohydrogen Production Process
Green hydrogen is considered to be one of the best candidates for fossil fuels in the near future. Bio-hydrogen production from the dark fermentation of organic materials, including organic wastes, is one of the most cost-effective and promising methods for hydrogen production. One of the main chall...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Fermentation |
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| Online Access: | https://www.mdpi.com/2311-5637/8/10/483 |
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| author | Tareq Salameh Enas Taha Sayed A. G. Olabi Ismail I. Hdaib Yazeed Allan Malek Alkasrawi Mohammad Ali Abdelkareem |
| author_facet | Tareq Salameh Enas Taha Sayed A. G. Olabi Ismail I. Hdaib Yazeed Allan Malek Alkasrawi Mohammad Ali Abdelkareem |
| author_sort | Tareq Salameh |
| collection | DOAJ |
| description | Green hydrogen is considered to be one of the best candidates for fossil fuels in the near future. Bio-hydrogen production from the dark fermentation of organic materials, including organic wastes, is one of the most cost-effective and promising methods for hydrogen production. One of the main challenges posed by this method is the low production rate. Therefore, optimizing the operating parameters, such as the initial pH value, operating temperature, N/C ratio, and organic concentration (xylose), plays a significant role in determining the hydrogen production rate. The experimental optimization of such parameters is complex, expensive, and lengthy. The present research used an experimental data asset, adaptive network fuzzy inference system (ANFIS) modeling, and particle swarm optimization to model and optimize hydrogen production. The coupling between ANFIS and PSO demonstrated a robust effect, which was evident through the improvement in the hydrogen production based on the four input parameters. The results were compared with the experimental and RSM optimization models. The proposed method demonstrated an increase in the biohydrogen production of 100 mL/L compared to the experimental results and a 200 mL/L increase compared to the results obtained using ANOVA. |
| first_indexed | 2024-03-09T03:40:31Z |
| format | Article |
| id | doaj.art-83d40ab46abb4f008ad9ed7da5cdbddb |
| institution | Directory Open Access Journal |
| issn | 2311-5637 |
| language | English |
| last_indexed | 2024-03-09T03:40:31Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fermentation |
| spelling | doaj.art-83d40ab46abb4f008ad9ed7da5cdbddb2023-12-03T14:41:29ZengMDPI AGFermentation2311-56372022-09-0181048310.3390/fermentation8100483Adaptive Network Fuzzy Inference System and Particle Swarm Optimization of Biohydrogen Production ProcessTareq Salameh0Enas Taha Sayed1A. G. Olabi2Ismail I. Hdaib3Yazeed Allan4Malek Alkasrawi5Mohammad Ali Abdelkareem6Sustainable and Renewable Energy Engineering Department, University of Sharjah, Sharjah P.O. Box 27272, United Arab EmiratesCenter for Advanced Materials Research, University of Sharjah, Sharjah P.O. Box 27272, United Arab EmiratesSustainable and Renewable Energy Engineering Department, University of Sharjah, Sharjah P.O. Box 27272, United Arab EmiratesDepartment of Renewable Energy Engineering, Faculty of Engineering, Isra University, Amman 11622, JordanDepartment of Nutrition, Harvard T H Chan School of Public Health, Boston, MA 02120, USAIndustrial Assessment Center, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USASustainable and Renewable Energy Engineering Department, University of Sharjah, Sharjah P.O. Box 27272, United Arab EmiratesGreen hydrogen is considered to be one of the best candidates for fossil fuels in the near future. Bio-hydrogen production from the dark fermentation of organic materials, including organic wastes, is one of the most cost-effective and promising methods for hydrogen production. One of the main challenges posed by this method is the low production rate. Therefore, optimizing the operating parameters, such as the initial pH value, operating temperature, N/C ratio, and organic concentration (xylose), plays a significant role in determining the hydrogen production rate. The experimental optimization of such parameters is complex, expensive, and lengthy. The present research used an experimental data asset, adaptive network fuzzy inference system (ANFIS) modeling, and particle swarm optimization to model and optimize hydrogen production. The coupling between ANFIS and PSO demonstrated a robust effect, which was evident through the improvement in the hydrogen production based on the four input parameters. The results were compared with the experimental and RSM optimization models. The proposed method demonstrated an increase in the biohydrogen production of 100 mL/L compared to the experimental results and a 200 mL/L increase compared to the results obtained using ANOVA.https://www.mdpi.com/2311-5637/8/10/483artificial intelligenceANFISPSOmodelingoptimizationbiohydrogen |
| spellingShingle | Tareq Salameh Enas Taha Sayed A. G. Olabi Ismail I. Hdaib Yazeed Allan Malek Alkasrawi Mohammad Ali Abdelkareem Adaptive Network Fuzzy Inference System and Particle Swarm Optimization of Biohydrogen Production Process Fermentation artificial intelligence ANFIS PSO modeling optimization biohydrogen |
| title | Adaptive Network Fuzzy Inference System and Particle Swarm Optimization of Biohydrogen Production Process |
| title_full | Adaptive Network Fuzzy Inference System and Particle Swarm Optimization of Biohydrogen Production Process |
| title_fullStr | Adaptive Network Fuzzy Inference System and Particle Swarm Optimization of Biohydrogen Production Process |
| title_full_unstemmed | Adaptive Network Fuzzy Inference System and Particle Swarm Optimization of Biohydrogen Production Process |
| title_short | Adaptive Network Fuzzy Inference System and Particle Swarm Optimization of Biohydrogen Production Process |
| title_sort | adaptive network fuzzy inference system and particle swarm optimization of biohydrogen production process |
| topic | artificial intelligence ANFIS PSO modeling optimization biohydrogen |
| url | https://www.mdpi.com/2311-5637/8/10/483 |
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