APPLYING NEURAL NETWORK IN HYDROTREATING PROCESS

摘要

Neural Network technology is an approach for describing process data behavior, using mathematical algorithms and statistical techniques. The use of neural network for modeling process is increasing in several kinds of chemical industries. This paper makes comments about successful critical factors, advantages and disadvantages of this methodology. Moreover, it presents some applications in Hydrotreating process of the petroleum refining industry. In feedstock Hydrotreating, the knowledge about characteristics of process regarding product property estimation, hydrogen chemical consumption and removal of contaminants (sulfur, nitrogen, aromatics), is very important to process optimization, product quality control and environment protection. The Neural Network technique has been used to model the behavior of the hydrogen chemical consumption, generation of light gas, the conversions of the hydrogenation of aromatic hydrocarbons (HDA), hydrodesulfurization (HDS) and hydrodenitrogenation (HDN) reactions and product physical properties. Operation conditions and some relevant feedstock properties were selected as input variables. In addition, Neural Networks have been built to predict the cetane number and stability of feedstock and hydrogenated products. The models were developed with experimental data, which were obtained in hydrogenation pilot plants from PETROBRAS. This paper presents a comparison between pilot plant data and estimated data.