Intelligent Sootblower

This project focuses on optimizing sootblower operation in a coal-fired power plant boiler using Artificial Intelligence and Machine Learning. In simple terms, a sootblower is a system that sprays high-pressure steam to clean ash and soot deposits from boiler heating surfaces.

Problem

Previously the sootblower mechanism using time-based method and cover all sootblower area. If the sootblower operates too frequently, it wastes steam and reduces plant efficiency. On the other hand, if cleaning is delayed, soot accumulation reduces heat transfer performance. To solve this problem, I developed an “Intelligent Sootblower” system that helps operators identify which boiler areas actually require cleaning.

Approach

The method uses historical operational data collected from the DCS and PI System, such as temperature, pressure, and other process parameters. The data is processed using Python and machine learning models to predict the fouling level of each boiler area, and the results are displayed through a monitoring dashboard. As a result, sootblower operation became more targeted and steam consumption was reduced by approximately 35%.