Modelling a Cement Precalciner by Machine Learning Methods

EasyChair Preprint 6653, version 2

Abstract

This work is a feasibility study of modelling the calcination process in a cement precalciner by employing machine learning algorithms. Calcination which is the thermal decomposition of calcium carbonate into calcium oxide and carbon dioxide plays a major role in characterizing the clinker quality, energy demand and CO2 emissions in a cement production facility. Due to the complex nature of the calcination process, it has been always a challenge to reasonably model the precalciner system. Six machine learning algorithms were tested to predict the apparent degree of calcination, CO2 molar fraction and water molar fraction in the precalciner outlet stream. Fifteen input variables were used to train the algorithms where their values were obtained through a large number of simulated dataset by applying mass and energy balance to the precalciner system. Artificial neural network (ANN) showed a better predictability for all three outputs than other regression methods.

Keyphrases: cement manufacturing process, machine learning, precalciner

@booklet{EasyChair:6653,
  author    = {Amila Chandra Kahawalage and M.H. Wathsala N. Jinadasa},
  title     = {Modelling a Cement Precalciner by Machine Learning Methods},
  howpublished = {EasyChair Preprint 6653},
  year      = {EasyChair, 2021}}