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The development of novel and low-energy-intensive processes for efficacious petrochemical commodities such as ethylene is currently challenging. This feasibility study was aimed at designing a wall-cooled multi-tubular oxydehydrogenation reactor for managing intensified ethane conversion to ethylene. The utilization of ethane + air feed in this system was investigated within two selective catalysts (Ni–Nb–O and MoVTeNbO). A rigorous mathematical model with reasonable assumptions was developed, which was then validated against experimental and calculated data of the MoVTeNbO catalyst. A large databank (1440 runs for each catalyst) was then generated with this model in extended operating conditions. The databanks were analyzed in specific cases and also overall to explore the connection between the input (feed composition and pressure, as well as coolant flow rate and temperature) and output (conversions, yields, selectivities, output and maximum temperatures, and pressure drop) variables of the system. Feed composition and coolant temperature, followed by feed flow rate and pressure, were the most effective variables in the presence of the two catalysts. Benefitting from statistical analyses and a genetic-algorithm-assisted multi-objective optimization procedure, this study found the practical and best operation of the system loaded with any of the catalysts. While processing more feed and converting a larger quantity of ethane to ethylene, the Ni–Nb–O catalyst was overshadowed by more air demand and carbon oxides release. MoVTeNbO catalyst produced lower quantities of ethylene and carbon oxides at higher temperatures. Future studies on the economic and environmental aspects of the process would facilitate catalyst screening for the best operation. • Designed a wall-cooled multi-tubular ODH reactor for ethylene production. • Modeled and validated Ni–Nb–O and MoVTeNbO catalysts for ethane ODH. • Generated a databank to assess key operating parameters. • Identified feed and coolant temperature as dominant process variables. • Applied a multi-objective genetic algorithm optimization for the best reactor operation.
Bakhtyari et al. (Mon,) studied this question.