METHODOLOGY FOR APPLYING BIG DATA TECHNOLOGIES AND PREDICTIVE MODELING TO ENHANCE OPERATIONAL EFFICIENCY OF COAL MINING COMPLEXES

Abstract

The integration of big data technologies into coal mining production processes represents a critical factor for enhancing operational efficiency amid the depletion of profitable deposits and tightening labor safety requirements. Global coal production reached 8.77 billion tons in 2024 with a production capacity of 8.9 billion tons, while more than 1,922 intelligent mining faces operate at coal mining enterprises in China. The study aims to analyze architectural solutions for analytical platforms and quantitatively assess the effectiveness of their implementation based on data from enterprises with varying levels of digitalization. The methodology includes comparative analysis of data processing technological solutions, statistical evaluation of operational indicators, and economic-mathematical modeling of investment attractiveness of digitalization projects. The empirical base covers the period 2022-2024 and includes operational data from enterprises with various architectural solutions ranging from fully on-premise to cloud-centric systems. It has been established that the implementation of predictive analytics systems ensures an increase in labor productivity from 22.4 to 88.7 tons per man-hour while simultaneously reducing longwall face personnel from 18-22 to 6-9 people. The accuracy of machine learning predictive models for forecasting energy consumption reaches 96% using LSTM architectures, providing a 13% reduction in peak loads. The economic effect of implementing predictive maintenance amounts to 5.5 million USD per enterprise annually through reduction of unplanned downtime and optimization of technical maintenance. The market for analytical solutions for coal mining demonstrates growth from 2.51 billion USD in 2024 to a projected 5.2 billion by 2035 with a compound annual growth rate of 6.9%. Intelligent management systems ensure production of 13,000 tons of coal per shift with a staff of 13 people at modern automated enterprises in China. The study revealed that injury rates are reduced by two-thirds with the implementation of IoT monitoring systems, while energy efficiency increases by 20% through AI optimization of equipment operating modes.

Keywords

Big Data, coal mining industry, predictive analytics, machine learning, IoT sensor networks, digital twins, production efficiency.

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