Mathematical simulation analysis and optimization of cylinder reactor for CO2 catalytic hydrogenation to jet fuel based on machine learning
With Fe-based catalyst,CO2 can be successfully converted into high value-added jet fuel via hydrogenation,showing great potential of industrial application.There is a lack of accurate and appropriate reactor model for CO2 hydrogenation to jet fuel,therefore it is urgent to construct reaction model and provide reference for the industrialization of related processes.The relationship between experimental conditions and mole fraction of key components CO and CO2 was explored through machine learning,and the prediction model of the key components CO and CO2 was constructed.Based on the Anderson-Schulz-Flory distribution,the carbon chain growth model was established,and the product distribution model was further constructed.Moreover,based on the calculation results of material balance,heat balance and pressure drop,homogeneous one-dimensional model of cylindrical fixed-bed reactors was established.The results of CO2 hydrogenation to jet fuel were obtained by simulating operation conditions,and the operation conditions were optimized.The simulation results show that CO2 conversion rate decreases and space time yield of jet fuel lumped component C11H24 increases with the increase of inlet temperature and space velocity.CO2 conversion rate increases with the increase of operating pressure.The operating pressure of maximum space time yield of C11H24 is 2.0 MPa.CO2 conversion rate and space time yield of C11H24 increase with temperature of saturated boiling water increasing.The optimum reaction conditions are inlet temperature of 275℃,inlet pressure of 2.0 MPa,space velocity of 4000 h-1 and saturated boiling water temperature of 294℃.Currently,the CO2 conversion rate is 21.95%,the space time yield of C11H24 is 12.14 g/(L·h),and the pressure drop is 0.20 MPa.
CO2 hydrogenationjet fuelmachine learningcylindrical fixed-bed reactorreactor simulation
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