A proposed novel combined milling and combustion performance model for fuel selection

This paper presents for the first time the development and evaluation of novel combined milling performance metric and a burnout prediction tool. Pistachio shells, walnut shell, rice husks, and palm kernel shells and wood pellets were milled in a vertical spindle mill with pneumatic classification and then pyrolyzed in a drop tube furnace in three particle sizes (53–75 μm, 212–300 μm, 650–850 μm) to produce chars. The Von Rittinger constant was used to rank the milling performance, which allows for the impact of mill choking to be considered, providing a more realistic assessment of milling performance. The novel burnout prediction model (simulating the combustion of the chars produced) is based on composite burnout profiles for different char types and is the first burnout prediction model which uses char morphology data to quantitatively predict burnout. It provides a rapid burnout comparison tool for power generators by quantifying the carbon loss during an iterative process, where the char material is progressively ‘burning’ from the outside inwards. Finally, by combining the milling and burnout metricises, it is possible to predict milling requirements for a desired burnout performance. These tools will enable power generators to make informed holistic decisions about new fuels and understand how composition and particle size influences both milling and subsequent burnout performance. • New milling performance metric for any fuels milled in a vertical spindle mill. • Ranks milling behaviour, pneumatic classification & mill choking in combined metric. • Novel burnout prediction tool for a drop tube furnace developed & evaluated. • For the first time a combined milling & a burnout prediction tool presented. • Enables predication of milling requirements for a desired burnout performance.