A 6-lump kinetic model, including a catalyst decay function for hydrocracking of vacuum gas oil in a commercial plant, is proposed. The model considers vacuum gas oil (VGO) and unconverted oil, having boiling point higher than 380-°C (380+°C) as one lump. Other lumps are diesel (260-380-°C), kerosene (150-260-°C), heavy naphtha (90-150-°C), light naphtha (40-90-°C) and gases (40-°C) as products. Initially, a kinetic network with thirty coefficients is considered, but following an evaluation using measured data and order of magnitude analysis, mainly the route passes of converting middle distillates to naphtha lumps are omitted; thus the number of kinetic coefficients is reduced to eighteen. This result is consistent with the reported characteristics of amorphous catalyst, which has the tendency to produce more distillates than naphtha. By using catalyst decay function in the kinetic model and replacing days on stream with a noble term, called accumulated feed, the prediction of the final approach during 1.5 years is in good agreement with the actual commercial data. The average absolute deviation (AAD%) of the model is less than 5% for all main products. If the residue or unconverted VGO is considered, the error only increases to 6.94% which is still acceptable for a commercial model. The results also confirm that the hydrocracking of VGO to upgraded products is represented better by a second order reaction.
In this research, based on actual data gathered from an industrial scale vacuum gas oil (VGO) hydrocracker and artificial neural network (ANN) method, a model is proposed to simulate yields of products including light gases, liquefied petroleum gas (LPG), light naphtha, heavy naphtha, kerosene, diesel and unconverted oil (off-test). The input layer of the ANN model consists of the catalyst, feed and recycle flow rates, and bed temperatures, while the output neurons are yields of those products. The results showed that the AAD% (average absolute deviation) of the developed ANN model for training, testing, and validating data are 0.445%, 1.131% and 0.755%, respectively. Then, by considering all operational constraints, the results confirmed that the decision variables (i.e., recycle rate and bed temperatures) generated by the optimization approach can enhance the gross profit of the hydrocracking process to more than $0.81 million annually, which is significant for the economy of the target refinery.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.