Hydroprocessing reactions require several days to reach steady-state, leading to long experimentation times for collecting sufficient data for kinetic modeling purposes. The information contained in the transient data during the evolution toward the steady-state is, at present, not used for kinetic modeling since the stabilization behavior is not well understood. The present work aims at accelerating kinetic model construction by employing these transient data, provided that the stabilization can be adequately accounted for. A comparison between the model obtained against the steady-state data and the one after accounting for the transient information was carried out. It was demonstrated that by accounting for the stabilization, combined with an experimental design algorithm, a more robust and faster manner was obtained to identify kinetic parameters, which saves time and cost. An application was presented in hydrodenitrogenation, but the proposed methodology can be extended to any hydroprocessing reaction.
Establishing the steady state in hydrotreating process requires several days, leading to long experimentation times in order to obtain sufficient steady-state data for kinetic modelling. However, during the evolution towards this steady state, effluent analyses are already carried out at regular time intervals to determine whether the steady state has been reached and to ensure that the reaction is under control. In this paper, the stabilization time was assessed by using experimental data during these transient conditions. The stabilization evolution is supposed to follow a first-order response. A characteristic time for stabilization τ was defined. A linear model with interaction for τ prediction was developed. It was found that a higher LHSV leads to a quicker stabilization. The extent of the impact of LHSV on τ depends on the feed resin content, i.e., the polar components with high molecule weight. A direct relationship between reactor pressure and stabilization time was found. Temperature is not a dominant factor. Stabilization of spent catalyst depends on the previous operating conditions.Moreover, online transient data can be used in order to predict, from the first two experimental points and τ calculated by the model, the future steady-state value. By testing against new data with other feedstocks, the model has been found to provide a good prediction of the stabilization evolution and the steady-state hydrotreating performance. If this value is far from the target, operators can change the operating condition without waiting for stabilization.
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