Methodology for inferring kinetic parameters of diesel oil HDS reactions based on scarce experimental data

Abstract Nowadays environmental regulations of fossil fuels emissions impose stricter limits for contaminants such as sulfur, nitrogen and aromatics from middle distillate petroleum fractions. The most important process used in oil refineries to reach the required specifications is catalytic hydrogenation. A key issue to optimize these units is the availability of reliable kinetic models for this complex, tri-phase reaction. A detailed, phenomenological model of the reactor would demand an exceeding experimental effort for consistently estimating all the necessary kinetic and transport parameters. Thus, a simplified approach is generally used for routine assessment of new catalysts and/or new streams to be processed. Due to the difficulty of characterization of these streams, which are very complex mixtures of numerous species, most models are based on pseudo-components. This approach, however, does not allow for model generalization with respect to feed composition. This paper presents and discusses a new methodology for dealing with this problem. Conventional neural network (NN) training algorithms are used for inducing NNs to predict kinetic parameters of simplified models for the catalytic hydrodesulfurization (HDS) reaction, using macro properties of the feed as input. As in practice there are rarely enough experimental data to subsidize empirical learning algorithms, the paper proposes and describes an ad hoc methodology for artificially enlarging the initial scarce experimental data. Results from inferring kinetic parameters of the catalytic removal of sulfur using NNs, based on macro-properties of oil middle distillates, are presented and discussed.