Forecasting Air Pollutant Emissions Using Deep Sparse Transformer Networks: A Case Study of the Ekibastuz Coal-Fired Power Plant

Yurii Andrashko, Oleksandr Kuchanskyi, Andrii Biloshchytskyi (), Alexandr Neftissov and Svitlana Biloshchytska ()
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Yurii Andrashko: Department of System Analysis and Optimization Theory, Uzhhorod National University, 88000 Uzhhorod, Ukraine
Oleksandr Kuchanskyi: Department of Computational and Data Science, Astana IT University, Astana 010000, Kazakhstan
Andrii Biloshchytskyi: Department of Administration, Astana IT University, Astana 010000, Kazakhstan
Alexandr Neftissov: Research and Innovation Center “Industry 4.0”, Astana IT University, Astana 010000, Kazakhstan
Svitlana Biloshchytska: Department of Computational and Data Science, Astana IT University, Astana 010000, Kazakhstan

Sustainability, 2025, vol. 17, issue 11, 1-17

Abstract: It is important to predict air pollutant emissions from coal-fired power plants using real-time technological parameters to improve environmental efficiency. Since the relationship between emissions and parameters is nonlinear, machine learning models are needed to forecast emissions under various boiler operating modes. This study develops and tests Deep Sparse Transformer Networks for predicting pollutant time series, accounting for long-term dependencies. Data were collected from a 4000 MW coal-fired power plant in Ekibastuz, Kazakhstan, covering 67,527 records for 14 indicators at 10 min intervals. Fractal R/S analysis confirmed long-term memory in SO 2 , PM 2.5 , and NO x series, guiding window length selection. The results show that the model achieves slightly better accuracy for SO 2 (R 2 0.95–0.38), while NO x and PM 2.5 have similar dynamics (R 2 0.93–0.26). However, accuracy drops notably after 12 points, making the model best suited for short-term forecasts. These findings support environmental monitoring services and help optimize plant parameters, contributing to lower emissions and advancing carbon neutrality goals.

Keywords: coal-fired power station; machine learning; emission forecasting; long-term dependence; R/S analysis; industrial management; carbon neutrality (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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