REAL-TIME PREDICTION OF HIGHER HEATING VALUE OF COAL IN COAL-FIRED POWER PLANTS USING OPERATING PARAMETERS

Abstract

This study introduces a novel approach to estimate the higher heating value of coal using real-time operational data from coal-fired power plants, addressing a significant gap in conventional methodologies. Traditionally, coal quality assessments involve extensive laboratory testing, which is impractical for real-time applications. This research develops a practical alternative by leveraging operational parameters such as main steam pressure, temperature, load, condensate flow, and coal flow as indicators of coal’s calorific value. The model developed in this study bypasses the time-consuming processes associated with traditional methods, enabling real-time estimation of coal’s higher heating value. Empirical validation shows the model’s high predictive accuracy, evidenced by an R2 value of 0.9666, indicating that it accounts for approximately 96.66 % of the variance in higher heating value. These results are supported by low mean square error and root mean square error values, underscoring superior performance compared to conventional methods. The effective use of operational data not only addresses the challenge of real-time higher heating value estimation but also optimizes the combustion process and enhances power plant efficiency. The practical application of these findings is pivotal for real-time coal quality control and plant performance management, providing a crucial tool for optimizing energy management. In conclusion, this research successfully develops and validates a data-driven approach for the real-time prediction of coal’s calorific value. This approach holds potential for widespread application, thereby improving energy management and operational efficiency in an industry that remains a major global energy provider