Abstract
In a conventional coal-fired boiler combustion of coal causes buildup of soot, ash, and slag in the heat transfer surfaces, which reduces the heat transfer as well as operating efficiency. To achieve a high operating efficiency and improving heat transfer in boiler pressure parts, sootblowing is an important phenomenon in boiler operation. Boiler operators are typically provided with little or no information about the fouling status of heating surfaces or with much guidance regarding how to optimize sootblowing operations even if there are some indications like metal temperature, sprays. This raises the requirement of sootblowing optimization strategy to determine which portions of the boiler to clean and on what schedule. The objective of this study is to develop a sootblowing optimization system that uses thermodynamic model and artificial neural networks model to predict the effectiveness of heating surfaces. In addition, the system utilizes an optimization algorithm to refine the search for the optimal sequence of sootblower frequency and achieve boiler performance targets.
Published Version
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