Real-time coal classification in thermal power plants

Abstract

Continuous variations in coal quality have a strong influence on operation of thermal power plants. However, in absence of real-time measurements of quality, the operators remain partially blind to the actual coal being consumed, leading to sub-optimal operation. A two-stage solution is proposed for real-time soft sensing of coal type. First, a novel semi-supervised cascaded clustering algorithm (SSCC) is utilized to extract coal classes from the historical sensor data of the plant and create a coal class library. The online stage includes a coal change detection algorithm to detect transition of coal and a SSCC-based coal classifier that enables real-time classification of coal using only the live sensor data. The algorithms are tested and verified with a set of synthetically generated industrial scale coal mill operation data. The real-time classification of coal can facilitate continuous optimum operation of plant vis-à-vis the emissions, efficiency and maintenance.