Online Composition Prediction of a Debutanizer Column Using Artificial Neural Network

The current method for composition measurement of an industrial distillation column includes an offline method, which is slow, tedious and could lead to inaccurate results. Among advantages of using online composition designed are to overcome the long time delay introduced by laboratory sampling and provide better estimation, which is suitable for online monitoring purposes. This paper presents the use of an online dynamic neural network to simultaneously predict n-butane composition of the top and bottom products of an industrial debutanizer columns. Principal component and partial least square analysis are used to determine the important variables surrounding the column prior to implementing the neural network. It is due to the different types of data available for the plant, which requires proper screening in determining the right input variables to the dynamic model. Statistical analysis is used as a model adequacy test for the composition prediction of n-butane in the column. Simulation results demonstrated that the Artificial Neural Network (ANN) can reliably predict the online composition of n-butane of the column. It is further confirmed by the statistical analysis with low Root Mean Square Error (RMSE) value indicating better prediction.