Improving the environmental impact of palm kernel shell through maximizing its production of hydrogen and syngas using advanced artificial intelligence
Fossil fuel depletion and the environmental concerns have been under discussion for energy production for many years and finding new and renewable energy sources became a must. Biomass is considered as a net zero CO2 energy source. Gasification of biomass for H2 and syngas production is an attractiv...
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my.utp.eprints.221092019-02-28T08:00:15Z Improving the environmental impact of palm kernel shell through maximizing its production of hydrogen and syngas using advanced artificial intelligence Rezk, H. Nassef, A.M. Inayat, A. Sayed, E.T. Shahbaz, M. Olabi, A.G. Fossil fuel depletion and the environmental concerns have been under discussion for energy production for many years and finding new and renewable energy sources became a must. Biomass is considered as a net zero CO2 energy source. Gasification of biomass for H2 and syngas production is an attractive process. The main target of this research is to improve the production of hydrogen and syngas from palm kernel shell (PKS) steam gasification through defining the optimal operating parameters� using a modern optimization algorithm. To predict the gaseous outputs, two PKS models were built using fuzzy logic based on the experimental data sets. A radial movement optimizer (RMO) was applied to determine the system's optimal operating parameters. During the optimization process, the decision variables were represented by four different operating parameters. These parameters include; temperature, particle size, CaO/biomass ratio and coal bottom ash (CBA) with their operating ranges of (650�750 °C), (0.5�1 mm), (0.5�2) and wt (0.02�0.10), respectively. The individual and interactive effects of different combinations were investigated on the production of H2 and syngas yield. The optimized results were compared with experimental data and results obtained from Response Surface Methodology (RSM) reported in literature. The obtained optimal values of the operating parameters through RMO were found 722 °C, 0.92 mm, 1.72 and 0.06 wt for the temperature, particle size, CaO/biomass ratio and coal bottom ash, respectively. The results showed that syngas production was significantly improved as it reached 65.44 vol which was better than that obtained in earlier studies. © 2018 Elsevier B.V. 2019 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058856762&doi=10.1016%2fj.scitotenv.2018.12.284&partnerID=40&md5=74a09e29731a79fee8abfc1e2471a301 Rezk, H. and Nassef, A.M. and Inayat, A. and Sayed, E.T. and Shahbaz, M. and Olabi, A.G. (2019) Improving the environmental impact of palm kernel shell through maximizing its production of hydrogen and syngas using advanced artificial intelligence. Science of the Total Environment, 658 . pp. 1150-1160. http://eprints.utp.edu.my/22109/ |
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Fossil fuel depletion and the environmental concerns have been under discussion for energy production for many years and finding new and renewable energy sources became a must. Biomass is considered as a net zero CO2 energy source. Gasification of biomass for H2 and syngas production is an attractive process. The main target of this research is to improve the production of hydrogen and syngas from palm kernel shell (PKS) steam gasification through defining the optimal operating parameters� using a modern optimization algorithm. To predict the gaseous outputs, two PKS models were built using fuzzy logic based on the experimental data sets. A radial movement optimizer (RMO) was applied to determine the system's optimal operating parameters. During the optimization process, the decision variables were represented by four different operating parameters. These parameters include; temperature, particle size, CaO/biomass ratio and coal bottom ash (CBA) with their operating ranges of (650�750 °C), (0.5�1 mm), (0.5�2) and wt (0.02�0.10), respectively. The individual and interactive effects of different combinations were investigated on the production of H2 and syngas yield. The optimized results were compared with experimental data and results obtained from Response Surface Methodology (RSM) reported in literature. The obtained optimal values of the operating parameters through RMO were found 722 °C, 0.92 mm, 1.72 and 0.06 wt for the temperature, particle size, CaO/biomass ratio and coal bottom ash, respectively. The results showed that syngas production was significantly improved as it reached 65.44 vol which was better than that obtained in earlier studies. © 2018 Elsevier B.V. |
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Rezk, H. Nassef, A.M. Inayat, A. Sayed, E.T. Shahbaz, M. Olabi, A.G. |
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Rezk, H. Nassef, A.M. Inayat, A. Sayed, E.T. Shahbaz, M. Olabi, A.G. Improving the environmental impact of palm kernel shell through maximizing its production of hydrogen and syngas using advanced artificial intelligence |
author_facet |
Rezk, H. Nassef, A.M. Inayat, A. Sayed, E.T. Shahbaz, M. Olabi, A.G. |
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Rezk, H. |
title |
Improving the environmental impact of palm kernel shell through maximizing its production of hydrogen and syngas using advanced artificial intelligence |
title_short |
Improving the environmental impact of palm kernel shell through maximizing its production of hydrogen and syngas using advanced artificial intelligence |
title_full |
Improving the environmental impact of palm kernel shell through maximizing its production of hydrogen and syngas using advanced artificial intelligence |
title_fullStr |
Improving the environmental impact of palm kernel shell through maximizing its production of hydrogen and syngas using advanced artificial intelligence |
title_full_unstemmed |
Improving the environmental impact of palm kernel shell through maximizing its production of hydrogen and syngas using advanced artificial intelligence |
title_sort |
improving the environmental impact of palm kernel shell through maximizing its production of hydrogen and syngas using advanced artificial intelligence |
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2019 |
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https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058856762&doi=10.1016%2fj.scitotenv.2018.12.284&partnerID=40&md5=74a09e29731a79fee8abfc1e2471a301 http://eprints.utp.edu.my/22109/ |
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