Prediction of industrial debutanizer column compositions using data-driven ANFIS- and ANN-based approaches

The work in this paper is based on an industrial debutanizer column in a petroleum refinery located in Malaysia, which produces LPG (liquefied petroleum gas) as the top stream and light naphtha as the bottom stream. The controlled outputs, which are the critical product quality to be measured, are the concentrations of the top and the bottom streams. It is required to maintain the product quality for profitability, and recent trends in soft-computing have seen a plethora of methods developed for this purpose. However, due to practical constraints experienced by the plant that limit extensive plant step tests, data available for the development of the soft sensor for this industrial debutanizer column is scarce. Hence, in this paper, a neuro-fuzzy technique (ANFIS) is investigated and compared against the widely used ANN model for the special case of limited training data samples of the industrial debutanizer column. It is observed that in comparison to ANN, ANFIS has better generalization performance and can work well with limited training data samples. From the results, it was observed that the prediction performance of the ANFIS model generally improved the RMSE by approximately 10 times in comparison to that of ANN.