Optimization Method of Coal Sample in Ash Prediction Model Based on Near Infrared Spectroscopy

Abstract

According to the unique problem of sample data in ash prediction model based on near infrared spectroscopy, an optimization method was proposed. Principal component analysis method is used for eliminating abnormal samples in coal sample set and extracting feature information of coal spectrum. A double-level clustering method is presented which integrates self-organize map neural network and fuzzy C-means clustering algorithm. The method classifies original sample set into 5 subsets and filtered dispute points. At last, prediction sub-models of coal ash are built for each subset based on GA-BP neural network to analyze testing samples of each subsets separately. The experimental results showed that the optimization method based on principal component analysis and the double-level clustering method can check and remove abnormal and suspicious samples exactly, compress sample data effectively, and improve learning precision and calculating speed of sub-models dramatically. The optimization method was a new effective method for development and application of near infrared spectroscopy in coal quality analysis.

Keywords

• coal quality analysis, coal ash, optimization of coal sample, ash prediction model, near infrared spectroscopy, spectrum feature of coal, double-level clustering method