Classification of coal gangue and identification of coal type based on first-derivative of mid-infrared spectrum

Abstract

Efficiently sorting coal gangue and identifying coal types are vital operations in coal preparation, yet they are traditionally resource-consuming, labor-intensive, and potentially hazardous. This work puts forward an straightforward method employing mid-infrared spectroscopy with first derivative spectrum to address these issues. The proposed technique focuses on the delineation and enhancement of characteristic spectra to detect subtle differences among samples. The method utilizes just a few characteristic spectra of 3740–3700 cm⁻¹, 1790–1750 cm⁻¹, 1615–1583 cm⁻¹, 1580–1540 cm⁻¹, 1550–1440 cm⁻¹, 1270–1210 cm⁻¹ and 867–854 cm⁻¹ to achieve 100 % high-accuracy classification of coal gangue and identification of coal types with total 250 spectra, such as bituminite, anthracite, lignite, roof sandstone and gangue, without the need for secondary sample processing or the assistance of machine learning algorithms, simplifying the process considerably. Such a strategy not only significantly improves the efficiency of coal sorting but also endorses real-time on-site detection. It offers a theoretical foundation for advanced coal separation technology and its implementation in real-world mining operations.