Modeling and optimization of an industrial hydrocracker plant
The main objective of this study is modeling and optimization of an industrial Hydrocracker Unit (HU) using Artiﬁcial Neural Network (ANN) model. In this case some data from an industrial hydrocracker plant were collected. Two-thirds of the data points were used to train ANN model. Among the various networks and architectures, two multilayer feed forward networks with Back Propagation (BP) training algorithm were found as the best model for the plant. Inputs of both ANNs include fresh feed and recycle hydrogen ﬂow rate, temperature of reactors, mole percentage of H2 and HS, feed ﬂow rate and temperature of debutanizer, pressure of debutanizer receiver, top and bottom temperature of fractionator column and pressure of fractionator column. The ﬁrst network was employed to calculate the speciﬁc gravity of gas oil, kerosene, Light Naphtha (LN), Heavy Naphtha (HN), gas oil and kerosene ﬂash point and gas oil pour point. The secondnet work was used to calculate the volume percent of C2, LN, HN and kerosene, gas oil and fractionators column residual (off test). Unseen data points were used to check generalization capability of the best network. There were good overlap between network estimations and unseen data.