As an important energy resource, the quality of coal directly affects combustion efficiency, environmental emissions, and product quality. Traditional detection methods rely on manual sampling and laboratory testing, which have issues such as uneven sampling, cumbersome operations, and long cycles, making it difficult to meet real-time and accurate requirements. This paper proposes optimization solutions including multimodal information fusion, standardization and automation, and intelligent monitoring systems. These technologies significantly improve the accuracy and efficiency of coal quality detection and have been validated in both laboratory and industrial applications, demonstrating their great potential in real-time monitoring and management of coal quality. The optimized methods not only more accurately reflect the actual ash and sulfur content in coal products but also provide strong technical support for the development of the coal industry, achieving intelligent and efficient coal quality monitoring.

coal quality detection  /  advanced information technology  /  sensing technology  /  data processing and analysis