Cloud service for protection and monitoring of computer networks

Ospanova, Ademi; Zharkimbekova, Aizhan; Kusepova, L.Т.; Тokkuliyeva, А.К.; Мanmuryn, М.

doi:10.51301/vest.su.2021.i3.29

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Preprint1

Article1

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

The Perspective of Using Neural Networks and Machine Learning Algorithms for Modelling and Forecasting the Quality Parameters of Coking Coal - a Case Study

Dyczko

2024

Preprint

View full text Add to dashboard Cite

The Perspective of Using Neural Networks and Machine Learning Algorithms for Modelling and Forecasting the Quality Parameters of Coking Coal - a Case Study

Dyczko

2024

Preprint

View full text Add to dashboard Cite

The Perspective of Using Neural Networks and Machine Learning Algorithms for Modelling and Forecasting the Quality Parameters of Coking Coal—A Case Study

Dyczko

2024

Geosciences

View full text Add to dashboard Cite

The quality of coking coal is vital in steelmaking, impacting final product quality and process efficiency. Conventional forecasting methods often rely on empirical models and expert judgment, which may lack accuracy and scalability. Previous research has explored various methods for forecasting coking coal quality parameters, yet these conventional methods frequently fall short in terms of accuracy and adaptability to different mining conditions. Existing forecasting techniques for coking coal quality are limited in their precision and scalability, necessitating the development of more accurate and efficient methods. This study aims to enhance the accuracy and efficiency of forecasting coking coal quality parameters by employing neural networks and artificial intelligence algorithms, specifically in the context of Knurow and Szczyglowice mines. The research involves gathering historical data on various coking coal quality parameters, including a proximate and ultimate analysis, to train and test neural network models using the Group Method of Data Handling (GMDH). Real-world data from Knurow and Szczyglowice mines’ coal production facilities form the basis of this case study. The integration of neural networks and artificial intelligence techniques significantly improves the accuracy of predicting key quality parameters such as ash content, sulfur content, volatile matter, and calorific value. This study also examines the impact of these quality indicators on operational costs and highlights the importance of final indicators like the Coke Reactivity Index (CRI) and Coke Strength after Reaction (CSR) in expanding industrial reserve concepts. Model performance is evaluated using metrics such as mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The findings demonstrate the effectiveness of these advanced techniques in enhancing predictive modeling in the mining industry, optimizing production processes, and improving overall operational efficiency. Additionally, this research offers insights into the practical implementation of advanced analytics tools for predictive maintenance and decision-making support within the mining sector.

show abstract

Cloud service for protection and monitoring of computer networks

Cited by 2 publications

References 3 publications

The Perspective of Using Neural Networks and Machine Learning Algorithms for Modelling and Forecasting the Quality Parameters of Coking Coal - a Case Study

The Perspective of Using Neural Networks and Machine Learning Algorithms for Modelling and Forecasting the Quality Parameters of Coking Coal - a Case Study

The Perspective of Using Neural Networks and Machine Learning Algorithms for Modelling and Forecasting the Quality Parameters of Coking Coal—A Case Study

Contact Info

Product

Resources

About