Predicting gas emissions in a cement kiln plant using hard and soft modeling strategies

Abstract

In this work, two alternative methodologies for modeling and predicting gas emissions of NO, NO2 and SO2 are presented. The first method involves hard modeling strategies with Parsimonious Multivariate Least Squares (PMLS) assuming simple polynomial functions as base model. The second is a soft modeling approach using Extreme Learning Machine (ELM). In this work we found that both methods have similar capabilities for data description, providing an in depth analysis of the system under study. Results also reveal further insights in predicting gas emissions and enlighten on which of the factors can be useful for prediction, and consequently for system characterization and emission abatement.