Abstract
This paper presents a study on the data-driven modelling and optimisation of a crude oil hydrotreating process using bootstrap aggregated neural networks. Hydrotreating (HDT) is a chemical process that can be widely used in crude oil refineries to remove undesirable impurities like sulphur, nitrogen, oxygen, metal and aromatic compounds. In order to enhance the operation efficiency of HDT process for crude oil refining, process optimisation should be carried out. To overcome the difficulties in building detailed mechanistic models, Bootstrap aggregated neural network models are developed from process operation data. In this paper, a crude oil HDT process simulated using Aspen HYSYS is used as a case study. It is shown that bootstrap aggregated neural network gives more accurate and reliable predictions than single neural networks. The neural network model based optimisation results are validated on HYSYS simulation and are shown to be effective.
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