A Multi-Model Prediction Method for Coal Mine Gas Concentration with Hierarchical Structure

Abstract

The low concentration of gas in the gas blending process is influenced by a number of factors and is characterized by some time variation and non-linearity. Therefore, the gas concentration needs to be predicted. This paper proposes a multi-model forecasting method with a hierarchical structure. First, because the measured gas concentration time-series data contain a lot of noise, the time-series data are decomposed into several independent eigenmode functions by using empirical mode decomposition, adaptively denoising by low-pass filtering, and then using phase space reconstruction technology to obtain a new time-series sample. Then, the training samples are grouped by conditional fuzzy clustering to determine the number of sub-modules. Finally, the maximum membership method is used to select sub-models and sub-sub-models, and then a multi-model time-series prediction model is established. The model can not only select different sub-models to process data in different regions but also can process each data jointly by multiple sub-models in different sub-models. Experiments were carried out on low-concentration measured data extracted from mines. The experimental results show that the proposed prediction model can capture the nonlinear characteristics of gas concentration time series and is superior to other existing prediction models in accuracy.