Modelling of the performance of industrial HDS reactors using a hybrid neural network approach

A hybrid neural network model is presented for the simulation of the performance of industrial HDS reactors. This model can be used in estimating the catalyst deactivation rate and the impact of feed quality on catalyst activity. A deterministic mathematical code simulating the reactor performance for hydrodesulphurization and hydrogen consumption reactions was used. The deterministic code was coupled with a neural network used to correlate the evaluated kinetic parameters from the industrial data with feed quality and catalyst life time. The neural network is also used to predict the kinetic parameters needed for simulation from the feed quality and the catalyst time on stream. A part of the necessary kinetic parameters were obtained from kinetic experiments performed with the industrial catalyst and with representative feeds in a small scale reactor.