Identification and Control of Distillation Process using Partial Least Squares based Artificial Neural Network

Abstract

Partial least squares technique has been in use for identification of the dynamics & control for multivariable distillation process. Discrete input-output time series data ) ( Y X were generated by exciting non-linear process models with pseudo random binary signals. Signal to noise ratio was set to 10 by adding white noise to the data. The ARX models as well FIR models in combination with least squares technique were used to build up dynamic inner relations among the scores of the time series data ) ( Y X , which logically built up the framework for PLS based process controllers. In this work, process dynamics was also identified in latent subspace using neural networks. The inverse dynamics of the latent variable based NN process acted as inverse neural controller (DINN). Distillation process without any decoupler could be controlled by a series of NN-SISO controllers General Terms Process Identification & control, Statistical Process Control