Using artificial neural networks of the type extreme learning machine for the modelling and prediction of the temperature in the head the column. Case of a C6H11-CH3 distillation column

Abstract

This study establishes a reliable model based on artificial neural networks of the ELM (extreme learning machine) type. The use of this model must reflect the true behavior of the process under its normal operating conditions and enable distinguishing a normal mode from an abnormal one. The installation in this study is a continuous distillation column for methylcyclohexane from a toluene/methylcyclohexane mixture, which mass composition has been defined as 23% methylcyclohexane. The ANN-ELM model was applied to a database of 1000 samples. All the relevant inputs of the model are defined by the inputs of the continuous distillation column during its normal operation, namely: the heating power, the preheating power, the reflux rate, the feed rate, the pressure drops and the preheating temperature. On the other hand, the model’s output is defined by the output of the continuous distillation column during its normal operation, namely the temperature at the head of the column. The ELM-type neural architecture obtained during the learning phase was tested on a 30% of the database. The results showed very good forecast accuracy using the ELM model. The low RMSE value (RMSE = 0.0168) was recorded during the test phase when the number of neurons in the hidden layer becomes 30. The prediction of the temperature at the head of the column by the ANN-ELM model has achieved its most accurate performance when the sigmoid activation function was adopted with a number of neurons in the hidden layer of 30. Moreover, the correlation coefficients were very close to unity during the test phase (R = 0.9345). The ANN-ELM prediction model is best suited for modeling the normal mode with variable operating points of the automated continuous distillation column and can be used online for the detection and diagnosis of malfunctions in this type of operation installation.