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D. Markauskas and H. Kruggel-Emden
Overall, all three models can better represent simulations with an instant decline
of the fraction retained, whereas the deviations increase for a less instant decline of
the fraction retained due to larger liquid amounts, frequencies and lower amplitudes.
If the models are adjusted with one set of parameters to all simulation results (see
Fig. 23, left bars), the ranking of the models from the lowest to the largest deviation
is model b, model c and model a, for each configuration. The results for the models b
and c are similar whether one set of parameters is used for all simulations (Fig. 23, left
bars) or one for each simulation (Fig. 23, right bars). In contrast, the deviations for
model a are much larger when only one set of parameters is used for all simulations. It
can be concluded, that the functional forms of all three process models can represent
the progression of the fraction retained per size class for one individual configuration
well. However, in batch screening with several particle layers on the screen, it is
essential to consider the subprocesses stratification and passage like is done in model
b and model c to represent the results for a wider range of configurations.
5 Conclusions
To conclude, the findings made provide insights in the relevant subprocesses stratification, passage and transport as well as particularities of screening like operational
parameters and particle characteristics including their influence on time or spatial
dependent outcomes like the fraction retained. To obtain the aforementioned findings
the modeling of screening processes with the discrete element method was improved
and extended by the possibility to address sieving/screening under the influence of
a certain amount of liquid. In addition, a general straightforward procedure to determine DEM simulation parameters reliably was developed. Furthermore, appropriate
validations against experiments have been carried out in order to underline the correctness of the DEM simulations and to apply the respective DEM submodels for
further investigations even beyond sieving/screening. With the information and data
obtained from steady state and dynamic DEM simulations, process models were
benchmarked and successfully extended for sieving/screening under moist conditions. Consequently, the derived process models can be applied as prototypes in
dynamic process simulation frameworks of combined solids processes.
Acknowledgements The authors gratefully acknowledge the support by DFG within project SPP
1679 through grant number KR3446/7-1, KR3446/7-2 and KR3446/7-3.
References
1. Stieß, M. Mechanische Verfahrenstechnik - Partikeltechnologie 1, Springer, Berlin (2009)
2. Liu, K.: Some factors affecting sieving performance and efficiency. Powder Technol. 193(2),
208–213 (2009)
D. Markauskas and H. Kruggel-Emden
Overall, all three models can better represent simulations with an instant decline
of the fraction retained, whereas the deviations increase for a less instant decline of
the fraction retained due to larger liquid amounts, frequencies and lower amplitudes.
If the models are adjusted with one set of parameters to all simulation results (see
Fig. 23, left bars), the ranking of the models from the lowest to the largest deviation
is model b, model c and model a, for each configuration. The results for the models b
and c are similar whether one set of parameters is used for all simulations (Fig. 23, left
bars) or one for each simulation (Fig. 23, right bars). In contrast, the deviations for
model a are much larger when only one set of parameters is used for all simulations. It
can be concluded, that the functional forms of all three process models can represent
the progression of the fraction retained per size class for one individual configuration
well. However, in batch screening with several particle layers on the screen, it is
essential to consider the subprocesses stratification and passage like is done in model
b and model c to represent the results for a wider range of configurations.
5 Conclusions
To conclude, the findings made provide insights in the relevant subprocesses stratification, passage and transport as well as particularities of screening like operational
parameters and particle characteristics including their influence on time or spatial
dependent outcomes like the fraction retained. To obtain the aforementioned findings
the modeling of screening processes with the discrete element method was improved
and extended by the possibility to address sieving/screening under the influence of
a certain amount of liquid. In addition, a general straightforward procedure to determine DEM simulation parameters reliably was developed. Furthermore, appropriate
validations against experiments have been carried out in order to underline the correctness of the DEM simulations and to apply the respective DEM submodels for
further investigations even beyond sieving/screening. With the information and data
obtained from steady state and dynamic DEM simulations, process models were
benchmarked and successfully extended for sieving/screening under moist conditions. Consequently, the derived process models can be applied as prototypes in
dynamic process simulation frameworks of combined solids processes.
Acknowledgements The authors gratefully acknowledge the support by DFG within project SPP
1679 through grant number KR3446/7-1, KR3446/7-2 and KR3446/7-3.
References
1. Stieß, M. Mechanische Verfahrenstechnik - Partikeltechnologie 1, Springer, Berlin (2009)
2. Liu, K.: Some factors affecting sieving performance and efficiency. Powder Technol. 193(2),
208–213 (2009)