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

Williams, Orla; Nichols, David; Güleç, Fatih; Perkins, Joseph; Lester, Edward

Abstract

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.

Citation

Williams, O., Nichols, D., Güleç, F., Perkins, J., & Lester, E. (2025). A proposed novel combined milling and combustion performance model for fuel selection. Journal of the Energy Institute, 120, Article 102046. https://doi.org/10.1016/j.joei.2025.102046

• Journal Article Type: Article
• Acceptance Date: Feb 21, 2025
• Online Publication Date: Mar 1, 2025
• Publication Date: 2025-06
• Deposit Date: Apr 9, 2025
• Publicly Available Date: Apr 23, 2025
• Journal: Journal of the Energy Institute
• Print ISSN: 1743-9671
• Electronic ISSN: 1746-0220
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 120
• Article Number: 102046
• DOI: https://doi.org/10.1016/j.joei.2025.102046
• Keywords: Milling performance rankingBurnout prediction, Combustion, Drop tube furnace, Von rittinger, Char morphology
• Public URL: https://nottingham-repository.worktribe.com/output/45849209
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S1743967125000741?via%3Dihub