Adaptive Kernel Learning Online Support Vector Regression for Predicting Sulfur Oxides Emissions in Coal-Based Power Plants
DOI:
https://doi.org/10.23055/ijietap.2026.33.1.11135Abstract
With the increasing global population and rapid urbanization, power plants are required to meet the growing energy demand. As a result, there has been a notable increase in environmental pollutants, particularly from coal-based plants, including carbon dioxide (CO₂), sulfur oxides (SOₓ), and nitrogen oxides (NOₓ), which pose threats to human health and the environment. Existing monitoring techniques face challenges in accurately predicting emissions due to the changing dynamics of boiler operations. Consequently, inaccuracies and insufficient control measures may occur. To address the dynamic characteristics of sensor data, a novel, accurate, and online support vector regression approach is proposed. It presents an online system for predicting SOₓ emissions from coal-fired plants, addressing dynamic changes in boiler systems. The method optimizes kernel width σ, penalty C, and error ε using the sine cosine algorithm. Based on a real-life dataset from over 400 sensors, the proposed system outperformed existing methods in predicting SOₓ emissions.
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