Quality monitoring in petroleum refinery with regression neural network: Improving prediction accuracy with appropriate design of training set

Abstract

The objective of this research is twofold. First, design of training set from the available plant data which is followed by use of training set for developing data driven linear and non-linear soft sensor models for continuous quality monitoring in petroleum refinery. Three data sets from three different processes in the petroleum refinery were investigated. The three data sets belong to ethane-ethylene distillation, debutanization and sulphur recovery process. Five different training set design techniques were applied separately to the three process datasets. These include Kennard-Stone, Duplex, SPXY, KSPXY and SPXYE techniques. Different sets of training data and validation data are designed for the three processes using the five techniques. The resulting training set data are used to develop linear (Multiple Linear Regression) and non-linear (Regression Neural Network) models of the three processes. The resulting validation set data are used to test the generalization ability of the developed models. Subsequently, the function computation time for all five techniques on the three process datasets were determined. It was observed that the duplex technique resulted in the best representative training set. However, the training sets designed from Kennard-Stone and SPXYE techniques resulted in models with best prediction performance with unknown data. The regression neural network models developed from the training set obtained by using Kennard-Stone algorithm for the debutanizer column and sulphur recovery unit are also found to perform better than some other data driven models reported in the literature.