Simultaneous high hydrogen content-synthesis gas production and in-situ CO2 removal via sorption-enhanced reaction process: modeling, sensitivity analysis and multi-objective optimization using NSGA-II algorithm
The main focus of this study is improvement of the steam-methane reforming (SMR) process by in-situ CO2 removal to produce high hydrogen content synthesis gas. Sorption-enhanced (SE) concept is applied to improve process performance. In the proposed structure, the solid phase CO2 adsorbents and pre-...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Iranian Association of Chemical Engineering (IAChE)
2016-11-01
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| Series: | Iranian Journal of Chemical Engineering |
| Subjects: | |
| Online Access: | https://www.ijche.com/article_38870_56ff2ab5c1eccc8801672b089a3ed8c1.pdf |
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| Summary: | The main focus of this study is improvement of the steam-methane reforming (SMR) process by in-situ CO2 removal to produce high hydrogen content synthesis gas. Sorption-enhanced (SE) concept is applied to improve process performance. In the proposed structure, the solid phase CO2 adsorbents and pre-reformed gas stream are introduced to a gas-flowing solids-fixed bed reactor (GFSFBR). One dimensional mathematical model is developed to evaluate the effect of adsorbents on the efficiency of SMR at steady-state condition. To prove the accuracy of the considered model, simulation results are compared against available industrial plant data. Modeling results represent that application of SE method in SMR enhances syngas production and reduces CO2 content. The reported data indicate that by overcoming thermodynamic limitations and controlling coke formation, CH4 conversion and H2 yield improve about 23% and 29%, respectively. For more investigation, sensitivity analyses of some related parameters of the pre-reformed gas are performed to predict optimum conditions. Finally, the proposed GFSFBR for the SMR process leads to higher hydrogen production and H2/CO ratio. As the last part, non-dominated sorting genetic algorithm-II is applied to perform multi-objective optimization of the SE-SMR. |
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| ISSN: | 1735-5397 2008-2355 |