Coal mill modeling by machine learning based on onsite measurements

This paper presents a novel coal mill modeling technique using genetic algorithms (GAs) based on routine operation data measured onsite at a National Power (NP) power station in the UK. The work focuses on the modeling of an E-type vertical spindle coal mill. The model performances for two different mills are evaluated covering a whole range of operating conditions. The simulation results show a satisfactory agreement between the model responses and measured data. The appropriate data can be obtained without recourse to extensive mill tests, and the model can be constructed without difficulty in computation. Thus, the work is of general applicability.