Neural-based Monitoring of a Debutanizer Distillation Column

In this paper a neural approach to distillation columns modelling is described. In particular a Debutanizer colums is considered and a real-time estimate of the butane percentage (C4) in the bottom draw (C5) is obtained by a NARMAX model implemented with a Multi-Layer Perceptron. The analyser of the C4 in C5 percentage used at present, provides a measure after a great and unknown delay, and is therefore not suitable for closed loop control purposes. A neural-based model, acting as a virtual sensor, can therefore represent a suitable strategy in getting a real-time estimation of the C4 in C5 concentration. Neural networks are used both to evaluate the delay of the analyser and to provide the desired real-time estimate of the C4 flow in the bottom draw of the debutanizer, overcoming the analyser’s delay. To obtain more accurate results the model is built so that the measured output is used as an input of the model together with the predicted one, suitably delayed. The neural NARMAX model has been determined by using an appropriate set of measurements performed on a plant operating in Sicily (Italy) and is now working on the plant. A comparison between the estimated output and the analyser's measures confirms the validity of the proposed approach.