Connectionist models of a crude oil distillation column for real time optimisation

Abstract

This study presents the development of connectionist or artificial neural network (ANN) models of a crude oil distillation column that can be utilised for real time optimization (RTO). The column is an actual distillation tower in operation in a refinery in Malaysia. Connectionist models developed for RTO are different than for process control applications because they are steady state, multivariable models. Training data for the network models was generated using a reconciled steady state process model simulated in the Aspen Plus process simulator. All ANN models were coded and simulated in MATLAB. Two types of feedforward network models were developed and compared: multi-layer perceptron (MLP) with adaptive learning rates and radial basis function networks (RBFN). The RBFN models were found to yield better and more consistent predictions with shorter training times than the MLP models. Grouping suitable output variables in a network model were found to give better predictions, and allow the complex, multivariable model of the crude tower to be more manageable.