Inferential Latent Variable Models for Combustion Processes

Abstract

This thesis investigates the application of latent variable methods to three combustion processes. Multivariate analysis of flame images and process data is performed to predict important quality parameters and monitor flame stability. The motivation behind this work is to decrease operational costs and greenhouse gases in these energy intensive processes. The three combustion processes studied are a lime kiln, a basic oxygen furnace and a coal-fired boiler. In lime kiln operation, the main goal is to stabilize final product temperature in order to reduce fouling and energy costs. Due to long process dynamics, prediction of product temperature is required at least one hour in advance for potential use in a control scheme. Several methods for extracting features from flame images were investigated for the prediction of the temperature. The best method is then combined with process data in a PLS model that also incorporates dynamic information. The analysis revealed that prediction one hour into the future is successful using latent variable methods. In the basic oxygen furnace analysis, the main goal is to predict end-point carbon of the batch process. Termination of the batch as soon as the desired carbon is attained reduces oxygen consumption and thus operational cost. Traditional image analysis is used to identify a constant field of view in the flame images. Multivariate image feature extraction methods were then used in combination with process data to successfully predict the final carbon content of the heat. The coal-fired boiler analysis focuses on monitoring of flame stability at different production and air to fuel levels of the boiler. Prediction of energy efficiency and off-gas chemistry from flame images is also investigated. An unexpected result was the ability to use the installed cameras for localized fouling monitoring. This thesis showed that the use of multivariate analysis of flame images and process data in combustion process is very promising.