Power plant condition monitoring by means of coal powder granulometry classification

Rossetti, Damiano, Squartini, Stefano, Collura, Stefano and Zhang, Yu (2018) Power plant condition monitoring by means of coal powder granulometry classification. Measurement, 123 . pp. 39-47. ISSN 0263-2241

Paper_Measurment_Journal_Rossetti.pdf - Whole Document

Item Type:Article
Item Status:Live Archive


In this work, a condition monitoring approach suitable for coal fired power plant is proposed. This approach is based on classification techniques and it is applied for the monitoring of the Particle Size Distribution (PSD) of coal powder. For coal fired power plant, the PSD of coal can affect the combustion performance, therefore it is a meaningful parameter of the operating condition of the plant. Three tests have been carried out aimed to study the effect of the class numbers, the dataset size, and the reduction of the number of false positives on the effectiveness of the approach. For each designed test, three standard classification algorithms, i.e. Artificial Neural Network, Extreme Learning Machine and Support Vector Machine, have been employed and compared. Experimental data taken from 13 measuring point on 13 burners of two different industrial power plants have been used. Obtained results showed that, using two classes give the most accurate results, using only the 90% of the available data can still provide comparable classification results, and the level of false positive can be effectively reduced.

Keywords:Condition monitoring, Particle size distribution, Classification, Machine learning, bmjdoi
Subjects:J Technologies > J140 Mineral Processing
G Mathematical and Computer Sciences > G760 Machine Learning
Divisions:College of Science > School of Engineering
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ID Code:31390
Deposited On:25 Mar 2018 16:34

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