Abstract—Reliable and accurate measurement of product compositions is one of the main difficulties in distillation column control. In this paper a soft sensor based on generalized regression neural network (GRNN) is proposed to estimate the product composition of a multicomponent distillation column on the basis of simulated time series data. The results are compared with artificial neural network (ANN) based soft sensor. From the detailed dynamic simulation results, it is found that the proposed GRNN based estimator works better than ANN based soft sensor. The performance of estimator is evaluated in the presence of noise in the input.

Index Terms—Distillation column, generalized regression, neural network, soft sensor.

Sanjay R. Patil is with the Department of Instrumentation Engineering, Government College of Engineering, Chandrapur, Maharashtra, India.
V. N. Ghate is with the Department of Electrical Engineering, Government College of Engineering, Chandrapur, Maharashtra, India.

Cite:Sanjay R. Patil, V. N. Ghate, "Generalized Regression Neural Network Based on Soft Sensor for Multicomponent Distillation Column," International Journal of Computer and Communication Engineering vol. 4, no. 6, pp. 371-378, 2015.