Develops a kinetic model for ethylene-to-butene conversion in gas-phase conditions, indicating process optimization implications.
This study develops an integrated kinetic model for the ethylene-to-butene (ETB) process to address ethylene oversupply from shale gas and rising C 4 olefin demand. A heterogeneous 1 wt % Ni/SiO 2 –Al 2 O 3 catalyst (Ni–Al-Q3; SiO 2 /Al 2 O 3 = 476, Ni/Al = 0.27) was prepared and tested in a fixed-bed reactor at 200–380 °C, 2–20 bar, and WHSV 360–1440 mL (g cat h) −1 . NH 3 -TPD characterized the catalyst acidity. The observed kinetic trends were as follows: ethylene conversion increased with temperature and pressure, while C 4 selectivity decreased. A lumped kinetic model comprising seven reaction pathways, including dimerization, oligomerization, cracking, aromatization, and hydrogenation, was established. Because conversion and selectivity evolved markedly with time on stream, a new data set measured at three times on stream (0.85, 1.3, and 4.0 h) over four temperatures and five space velocities was used for parameter estimation, and a two-site Voorhies catalyst deactivation function was integrated into the model. Kinetic parameters for the proposed reaction network were fitted using design-of-experiments-based kinetic data, with global optimization via a genetic algorithm and a reparameterized Arrhenius formulation. The model accurately reproduced the dominant carbon flow (ethylene conversion and butene yield) across the calibrated conditions. The model is calibrated and valid at 230–270 °C, 2 bar, and 0.85–4.0 h on stream; the 220 and 260 °C runs and the 5–20 bar runs are used for validation, with pressure treated as prediction-only. The identifiable apparent activation energies span 19.1–88.3 kJ mol –1; three low-yield pathways (R3, R4, and R7) are pinned at the 1 kJ mol –1 lower bound and retained only for stoichiometric closure, and are not interpreted as physical values. This work presents an integrated kinetic model for Ni-aluminosilicate catalyzed ETB under gas-phase conditions and provides a quantitative basis for reactor scale-up and process optimization within the stated operating window.
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Kim et al. (2026) studied this question.
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