Work

On Increasing the Efficiency of a Cement Clinker Kiln Using Machine Learning

Year: 2024

Type: article

Abstract: The production of cement clinker faces many management challenges, particularly in terms of consistently high product quality, efficient energy usage, and stable furnace operation. In this study, a machine learning model based on gradient boosting was developed for the efficient operation modes of the kiln (required quality and low energy consumption). The influence of process parameters on the efficiency of the clinker kiln was investigated. As a result, it was shown that stable kiln feeding improves the quality of the final product. High feeding variation leads to an increase in the dispersion of the entire setup and attempts to maintain it in a stable state by changing the volume of burned gas. When there is high feeder operation variation, the lime saturation factor has a significant impact on the outcome. The obtained results can be used to create a digital assistant for the kiln operator.

Source: Journal of Engineering Thermophysics

DOI: 10.1134/s1810232824040015

Authors: E. B. Butakov, S. S. Abdurakipov, V. Y. Neznamov, S. V. Alekseenko

Institutions: Russian Academy of Sciences, Siberian Branch of the Russian Academy of Sciences, Institute of Thermophysics, National Research Nuclear University MEPhI

Language: English

Topic: Fault Detection and Control Systems

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Responsible consumption and production

Open Access status: green

OpenAlex ID: W4406533088

FWCI: 0.475

Cites: 8

Cited by: 3

Related to: 10