INFORMATION SYSTEM FOR METALLURGICAL PROCESS ANALYSIS AND OPTIMIZATION

Bagdaulet Kenzhaliyev, Serik Aibagarov

Journal of problem in computer science and information technologies, 2025

Full text Open access: Gold Metadata (CSL-JSON)
DOI 10.26577/jpcsit20253310
Data confidence 70 medium
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Abstract

The metallurgical industry faces increasing challenges in reconciling production efficiency with environmental compliance while managing heterogeneous data streams across complex processing operations. Traditional approaches to metallurgical process analysis rely on manual calculations and isolated software tools, limiting operational efficiency and introducing potential errors in critical decision-making processes. This paper presents the design and implementation of a comprehensive web-based information system specifically developed for integrated metallurgical process analysis and optimization. The system architecture employs a modular design incorporating three specialized computational modules: pyrometallurgical calculations for ore-to-metal conversions, hydrometallurgical process modeling for extraction operations, and auxiliary process calculators for specialized applications. The platform integrates Django-based backend processing with responsive frontend interfaces, supporting multi-user access, comprehensive data validation, and seamless integration with existing plant information systems. Implementation includes predictive analytics capabilities utilizing machine learning algorithm for forward process prediction and optimization. System validation demonstrates robust performance with processing times ranging from 0.6 to 3.4 seconds across different computational modules and operational success rates exceeding 98.7% for all core functions. The platform supports multiple data input formats including manual entry and Excel file processing, with comprehensive export capabilities (JSON, CSV, Excel) enabling integration with downstream analysis tools. Performance evaluation indicates the system successfully addresses key industrial requirements for accuracy, reliability, and scalability in metallurgical process analysis applications. The developed architecture provides a practical framework for implementing digital transformation initiatives in metallurgical operations while maintaining computational precision required for critical industrial applications