Accurate Coal Classification Using PAIPSO-ELM with Near-Infrared Reflectance Spectroscopy

Abstract

China has vast proven coal reserves, encompassing a wide variety of types. However, traditional coal classification methods have limitations, often leading to inaccurate classification and inefficient utilization of coal resources. To address this issue, this paper introduces the Extreme Learning Machine (ELM) as a novel coal classification method, based on the near-infrared reflectance spectroscopy (NIRS) of coal. Initially, we collected NIRS data from coal samples using the SVC-HR-1024 spectrometer. Given the high dimensionality and strong linear correlations in NIRS data, we conducted preprocessing to enhance the usefulness of the data. In experiments, the ELM model demonstrated good classification performance. However, due to the random generation of input layer weights and hidden layer biases in the ELM model, its performance can be unstable, preventing the model from fully realizing its potential. To overcome this shortcoming, we employed the Particle Swarm Optimization (PSO) algorithm to optimize the parameters of the ELM model. Simulation results showed that the PSO-ELM model achieved a 9.68% improvement in classification accuracy compared to the original ELM model. Furthermore, we optimized the PSO algorithm by introducing exponentially decaying inertia factors and position-variant particles to further reduce the risk of the algorithm falling into local optima. The improved Position-Adaptive Inertia PSO-ELM (PAIPSO-ELM) model achieved an additional 2% increase in classification accuracy over the PSO-ELM model, without a significant increase in training time. In summary, this paper proposes a coal spectral classification method based on the PAIPSO-ELM model, effectively overcoming the limitations of traditional classification methods while meeting industrial demands for classification accuracy and speed.