Pollutant monitoring in tail gas of sulfur recovery unit with statistical and soft computing models

Abstract

In this article, data-driven models are developed for real time monitoring of sulfur dioxide and hydrogen sulfide in the tail gas stream of sulfur recovery unit (SRU). Statistical [partial least square (PLS), ridge regression (RR) and Gaussian process regression (GPR)] and soft computing models are constructed from plant data. The plant data were divided into training and validation sets using Kennard-Stone algorithm. All models are developed from the training data set. PLS model is designed using SIMPLS algorithm. Three different ridge parameter selection techniques are used to design the RR model. GPR model is designed using four hyper parameter selection methods. The soft computing models include fuzzy and neuro-fuzzy models. Prediction accuracy of all models is assessed by simulation with validation dataset. Simulation results show that the GPR model designed with marginal log likelihood maximization method has good prediction accuracy and outperforms the performance of all other models. The developed GPR model is also found to yield better prediction accuracy than some other models of the SRU proposed in the literature.