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Modelling of thermal processes during extrusion based densification of agricultural biomass residues
- Mikulandrić, Robert, Vermeulen, Brecht, Nicolai, Bart, Saeys, Wouter
- Applied energy 2016 v.184 pp. 1316-1331
- agricultural machinery and equipment, biomass, bulk density, energy efficiency, environmental impact, equations, extrusion, feedstocks, friction, heat transfer, model validation, prediction, simulation models, temperature
- Biomass residues are increasing their share as a feedstock for renewable heat and power systems. Agricultural biomass is available in large quantities but to be utilised in energy systems the bulk density of the material should be increased. A large number of process parameters influence biomass compression and thus affect machinery efficiency and particular energy consumption. Increased concerns related to energy efficiency and environmental impacts of agricultural machinery have led to an increased interest in simulation models which can be used for process optimisation. In this study the influence of temperature on the biomass compression process performance has been analysed. For this purpose, mathematical models describing the thermal processes in the biomass material and the surrounding compression chamber have been elaborated. The heat transfer in the biological material has been described with time dependent Navier–Stokes equations for non-isothermal flow, while time dependent Navier–Stokes equations for heat transfer in solids have been utilised to describe heat transfer in metal structures of the chamber. The prediction performance of the model has been verified by comparing the simulated temperature evolution in the biomass and chamber walls to the corresponding values measured from a biomass compression machine through dedicated tests. The model was found to be able to predict the measured values with an average R² of 0.82. The influence of friction heat in the compression chamber has been simulated and heat losses during the process have been estimated. The developed simulation model has been used to make suggestions for process improvement.