Inference of distillation column products quality using Bayesian networks

Abstract

Different neural networks algorithms have already been employed on the inference of distillation column products quality. This paper applies Bayesian neural networks on the inference of diesel 85% ASTM distillation, and compares the results with traditional multilayer perceptrons. Also, several pre-processing and variables selection methods have been implemented and tested.