Application of artificial intelligence and red-tailed hawk optimization for boosting biohydrogen production from microalgae
Enhancing biohydrogen production from microalgae is crucial in addressing environmental and energy challenges. It provides a sustainable, clean energy source while reducing greenhouse gas emissions. Moreover, it advances microalgae-based biotechnology, enabling innovative biofuel production and ecol...
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Elsevier
2024-11-01
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| Series: | International Journal of Thermofluids |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666202724003173 |
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| author | Hegazy Rezk Ali Alahmer Abdul Ghani Olabi Enas Taha Sayed |
| author_facet | Hegazy Rezk Ali Alahmer Abdul Ghani Olabi Enas Taha Sayed |
| author_sort | Hegazy Rezk |
| collection | DOAJ |
| description | Enhancing biohydrogen production from microalgae is crucial in addressing environmental and energy challenges. It provides a sustainable, clean energy source while reducing greenhouse gas emissions. Moreover, it advances microalgae-based biotechnology, enabling innovative biofuel production and ecological revitalization. The main target of this study is to develop a robust ANFIS model to simulate the biohydrogen production process from microalgae within photobioreactors. The study focuses on enhancing hydrogen yield by optimizing three critical process parameters: sulfur concentration (%), run time (hours), and wet biomass concentration (g/L). Initially, an adaptive neuro-fuzzy inference system (ANFIS) model for biohydrogen production process is constructed based on empirical data. Subsequently, the red-tailed hawk algorithm (RTH) is used to determine the optimal values for the process parameters, corresponding to maximum hydrogen yield. The performance of ANFIS model in predicting hydrogen yield is assessed using root mean square error (RMSE) and coefficient-of-determination (R2) values. The obtained RMSE values for training and testing data are 2.8477 × 10−05 and 1.2807, respectively, while the corresponding R2 values are 1.0 and 0.9911 for training and testing. The introduction of fuzzy logic into the model significantly improves its predictive accuracy, as evidenced by the drop in RMSE from 10.79 with ANOVA to 0.7159 with ANFIS, representing a substantial 93.4 % decrease. The remarkable precision of the ANFIS model, indicated by its low RMSE and high R2 values, underscores the success of the modeling stage. The combination between ANFIS with the RTH technique yields impressive results, leading to a hydrogen yield enhancement of 6.87 % and 26.65 % when compared to both measured data and ANOVA. |
| format | Article |
| id | doaj-art-8856a8032f5a42c7b7aae36a6771bee8 |
| institution | Kabale University |
| issn | 2666-2027 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Thermofluids |
| spelling | doaj-art-8856a8032f5a42c7b7aae36a6771bee82024-12-13T11:04:10ZengElsevierInternational Journal of Thermofluids2666-20272024-11-0124100876Application of artificial intelligence and red-tailed hawk optimization for boosting biohydrogen production from microalgaeHegazy Rezk0Ali Alahmer1Abdul Ghani Olabi2Enas Taha Sayed3Department of Electrical Engineering, College of Engineering in Wadi Alddawasir, Prince Sattam bin Abdulaziz University, Saudi ArabiaDepartment of Mechanical Engineering, Tuskegee University, Tuskegee, AL 36088, USASustainable Energy & Power Systems Research Centre, RISE, University of Sharjah, P.O. Box 27272, Sharjah, , UAE; Corresponding author.Chemical Engineering Department, Faculty of Engineering, Minia University, Elminia, EgyptEnhancing biohydrogen production from microalgae is crucial in addressing environmental and energy challenges. It provides a sustainable, clean energy source while reducing greenhouse gas emissions. Moreover, it advances microalgae-based biotechnology, enabling innovative biofuel production and ecological revitalization. The main target of this study is to develop a robust ANFIS model to simulate the biohydrogen production process from microalgae within photobioreactors. The study focuses on enhancing hydrogen yield by optimizing three critical process parameters: sulfur concentration (%), run time (hours), and wet biomass concentration (g/L). Initially, an adaptive neuro-fuzzy inference system (ANFIS) model for biohydrogen production process is constructed based on empirical data. Subsequently, the red-tailed hawk algorithm (RTH) is used to determine the optimal values for the process parameters, corresponding to maximum hydrogen yield. The performance of ANFIS model in predicting hydrogen yield is assessed using root mean square error (RMSE) and coefficient-of-determination (R2) values. The obtained RMSE values for training and testing data are 2.8477 × 10−05 and 1.2807, respectively, while the corresponding R2 values are 1.0 and 0.9911 for training and testing. The introduction of fuzzy logic into the model significantly improves its predictive accuracy, as evidenced by the drop in RMSE from 10.79 with ANOVA to 0.7159 with ANFIS, representing a substantial 93.4 % decrease. The remarkable precision of the ANFIS model, indicated by its low RMSE and high R2 values, underscores the success of the modeling stage. The combination between ANFIS with the RTH technique yields impressive results, leading to a hydrogen yield enhancement of 6.87 % and 26.65 % when compared to both measured data and ANOVA.http://www.sciencedirect.com/science/article/pii/S2666202724003173BiohydrogenANFIS modelingOptimizationMicroalgae |
| spellingShingle | Hegazy Rezk Ali Alahmer Abdul Ghani Olabi Enas Taha Sayed Application of artificial intelligence and red-tailed hawk optimization for boosting biohydrogen production from microalgae International Journal of Thermofluids Biohydrogen ANFIS modeling Optimization Microalgae |
| title | Application of artificial intelligence and red-tailed hawk optimization for boosting biohydrogen production from microalgae |
| title_full | Application of artificial intelligence and red-tailed hawk optimization for boosting biohydrogen production from microalgae |
| title_fullStr | Application of artificial intelligence and red-tailed hawk optimization for boosting biohydrogen production from microalgae |
| title_full_unstemmed | Application of artificial intelligence and red-tailed hawk optimization for boosting biohydrogen production from microalgae |
| title_short | Application of artificial intelligence and red-tailed hawk optimization for boosting biohydrogen production from microalgae |
| title_sort | application of artificial intelligence and red tailed hawk optimization for boosting biohydrogen production from microalgae |
| topic | Biohydrogen ANFIS modeling Optimization Microalgae |
| url | http://www.sciencedirect.com/science/article/pii/S2666202724003173 |
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