Production of H2 via sorption enhanced auto-thermal reforming for small scale Applications-A process modeling and machine learning study |
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| Affiliation: | 1. Department of Chemical Engineering, University of Engineering and Technology, Lahore, Pakistan;2. Corporate Sustainability and Digital Chemical Management Department, Interloop Limited, Pakistan;1. Purification Equipment Research Institute of CSIC, Handan 056027, Hebei province, People''s Republic of China;2. Institute of Nuclear and New Energy Technology, MOST-USDA Joint Research Centre for Biofuels, Beijing Engineering Research Center for Biofuels, Tsinghua University, Beijing, People''s Republic of China;1. School of Automation, Wuhan University of Technology, Wuhan, 122 Luoshi Road, 430000, China;2. Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China;3. Dept. of Mechanical & Aerospace Engineering, North Carolina State University, Raleigh, NC, USA;1. School of Chemistry and Chemical Engineering, Southwest University, Chongqing 400715, China;2. School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China;1. State Key Laboratory of Nonlinear Mechanics, Institute of Mechanics, Chinese Academy of Sciences, Beijing, 100190, China;2. School of Engineering Science, University of Chinese Academy of Sciences, Beijing, 100049, China;3. The University of Hong Kong, Hong Kong, 999077, China;4. School of Earth and Space Sciences, Peking University, Beijing, 100871, China |
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| Abstract: | Small scale production of H2 via sorption enhanced auto-thermal reforming (SEATR) of methane is simulated using Ni based catalyst and CaO sorbent for the capturing of CO2. One dimensional heterogeneous reactor model was developed using gPROMS model builder to study the performance of SEATR reactor. At low pressure mode, the process was evaluated for varying temperature, pressure, gas flux and steam to carbon ratio. Chemical equilibrium with application (CEA), an equilibrium based software was employed so as to compare both equilibrium and simulation results. Under a range of temperature (500–1000 K), pressure (1–10 bar), S/C ratio (1–6), and O/C ratio (0.2–0.6) close to equilibrium conditions, model outputs satisfactory results with regard to CH4 conversion, CO2 capturing, H2 yield and purity. At 750 K, 2.9 bar, Gs of 0.4 kg/m2 s, S/C of 3 and O/C of 0.45, H2 purity and CH4 conversion achieved was 97% and 94% respectively in comparison with 66% and 77% from conventional auto-thermal reforming. In Bayesian Regularization (BR), Mean square error(MSE) and R value is minimum for neural network algorithm comparison. It accounts for 1.2e?10 and 0.999 respectively. BR produces minimum error with increase in Epochs and gradients values highlighting maximum performance with optimize computation time for process modeling data-integration studies and generalization. |
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| Keywords: | Mathematical modeling Thermodynamic equilibrium Hydrogen Sorption enhanced reforming Auto-thermal reforming |
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