Modelling a Cement Precalciner by Machine Learning Methods
Amila Chandra Kahawalage, Wathsala Jinadasa · Linköping electronic conference proceedings · 2022
This work is a feasibility study of modelling the calcination process in a cement precalciner by employing machine learning algorithms. Calcination plays a significant role in characterising the clinker quality, energy demand and CO2 emissions in a cement production facility. Due to the complex nature of the calcination process, it has always been a challenge to reasonably model the precalciner system. This study is an attempt of finding a feasible alternative to answering this challenge. In this study, six machine learning algorithms were tested to analyse three output variables, which are, 1). the apparent degree of calcination, 2). CO2 molar fraction (dry basis) and 3).water molar fraction in the precalciner outlet stream. Fifteen input variables were used to train the algorithms, of which the values were obtained through a large number of simulated datasets by applying mass and energy balance to the precalciner system. A number of machine learning algorithms showed better predictability and Artificial neural network (ANN) showed the best performance for all three output variables.