1090 XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3
Heinicke, F. 2013. Beitrag zur Modellierung der Zerkleinerung
in Gutbettwalzenmühlen. Dissertation. Rheinisch-
Westfälische Technische Hochschule Aachen.
Heinz, R., Hofmann, F., Petersen, H. and Polke, R.
1999. Produktkenndaten zur Charakterisierung
des Verdichtungsverhaltens für die Auslegung
von Walzenpressen. Chemie Ingenieur Technik 61
(5):424–425.
Herrmann, W. 1973. Das Verdichten von Pulvern zwischen
zwei Walzen. Weinheim/Bergstr.: Verlag Chemie.
Herting, M.G. 2007. Einfluss der Partikelgröße auf die
Walzenkompaktierung und Tablettierung. Dissertation.
Heinrich Heine University Düsseldorf.
Jenike, A.W. 1961. Gravity flow of bulk solids. Bulleting No.
108. Salt Lake City: University of Utah.
Johanson, J.R. 1965. A Rolling Theory for Granular Solids.
Journal of Applied Mechanics 32(4):842–848.
Katashinskii, V.P. 1966. Analytical determination of spe-
cific pressure during the rolling of metal powders.
Powder Metall Met Ceram 5:765–772.
Klymowsky, R., Patzelt, N., Knecht, J., and Burchardt, E.
2002. Selection and Sizing of High Pressure Grinding
Rolls. In SME Mining Reference Handbook. Edited by
R.L. Lowrie.
Knobloch, O. and Thormann, P. 1988.
Zerkleinerungstechnologie mit der Gutbettwalzen-
mühle. Eine Analyse der Einflußfaktoren. Baustoff-
industrie 3:73–76.
Kühnel, L.: Druckverteilung im idealen Gutbett.
Dissertation. Technische Universität Bergakademie
Freiberg, 2018
the data can be used to create dashboards and insights into
the state of the process and machine.
SUMMARY AND CONCLUSION
IIoT is a major developing topic within heavy-duty
machine applications. Roller presses use various sensors to
control the operation of the machine and over the last years
several digital helpers have been developed to optimize
the machine performance. Utilizing a robust database of
machine sensor observations allows for cooperative bench-
marking across production facilities worldwide and plays a
key role in advancing the development of roller press opti-
mization. Within this paper four examples of digital helpers
have been presented and show clear creative solutions that
offer a better understating of the machine’s operation.
Nevertheless, there still lies optimization potential
through combining the discussed measurement data with
up- and downstream equipment and material data. This
will give insight into not only the variable effects on the
machine, but also their impact on the surrounding equip-
ment. Detailed research into the “in process” material
properties (fresh and processed feed) in high volume granu-
lometric flow would provide insights that would benefit the
further creation of more digital helpers in the future.
REFERENCES
Anderson, K.T. 1990. Experimental measurements of pres-
sure distribution in cement roller mill. 9th International
Conference on Experimental Mechanics. Technical
University of Denmark.
Hanstein, T. 2002. Beitrag zur Erhöhung der Standzeit
der Arbeitsorgane von Gutbett-Walzenmühlen.
Dissertation. TU Bergakademie Freiberg.
Figure 11. Example of stud wear detection over time
Heinicke, F. 2013. Beitrag zur Modellierung der Zerkleinerung
in Gutbettwalzenmühlen. Dissertation. Rheinisch-
Westfälische Technische Hochschule Aachen.
Heinz, R., Hofmann, F., Petersen, H. and Polke, R.
1999. Produktkenndaten zur Charakterisierung
des Verdichtungsverhaltens für die Auslegung
von Walzenpressen. Chemie Ingenieur Technik 61
(5):424–425.
Herrmann, W. 1973. Das Verdichten von Pulvern zwischen
zwei Walzen. Weinheim/Bergstr.: Verlag Chemie.
Herting, M.G. 2007. Einfluss der Partikelgröße auf die
Walzenkompaktierung und Tablettierung. Dissertation.
Heinrich Heine University Düsseldorf.
Jenike, A.W. 1961. Gravity flow of bulk solids. Bulleting No.
108. Salt Lake City: University of Utah.
Johanson, J.R. 1965. A Rolling Theory for Granular Solids.
Journal of Applied Mechanics 32(4):842–848.
Katashinskii, V.P. 1966. Analytical determination of spe-
cific pressure during the rolling of metal powders.
Powder Metall Met Ceram 5:765–772.
Klymowsky, R., Patzelt, N., Knecht, J., and Burchardt, E.
2002. Selection and Sizing of High Pressure Grinding
Rolls. In SME Mining Reference Handbook. Edited by
R.L. Lowrie.
Knobloch, O. and Thormann, P. 1988.
Zerkleinerungstechnologie mit der Gutbettwalzen-
mühle. Eine Analyse der Einflußfaktoren. Baustoff-
industrie 3:73–76.
Kühnel, L.: Druckverteilung im idealen Gutbett.
Dissertation. Technische Universität Bergakademie
Freiberg, 2018
the data can be used to create dashboards and insights into
the state of the process and machine.
SUMMARY AND CONCLUSION
IIoT is a major developing topic within heavy-duty
machine applications. Roller presses use various sensors to
control the operation of the machine and over the last years
several digital helpers have been developed to optimize
the machine performance. Utilizing a robust database of
machine sensor observations allows for cooperative bench-
marking across production facilities worldwide and plays a
key role in advancing the development of roller press opti-
mization. Within this paper four examples of digital helpers
have been presented and show clear creative solutions that
offer a better understating of the machine’s operation.
Nevertheless, there still lies optimization potential
through combining the discussed measurement data with
up- and downstream equipment and material data. This
will give insight into not only the variable effects on the
machine, but also their impact on the surrounding equip-
ment. Detailed research into the “in process” material
properties (fresh and processed feed) in high volume granu-
lometric flow would provide insights that would benefit the
further creation of more digital helpers in the future.
REFERENCES
Anderson, K.T. 1990. Experimental measurements of pres-
sure distribution in cement roller mill. 9th International
Conference on Experimental Mechanics. Technical
University of Denmark.
Hanstein, T. 2002. Beitrag zur Erhöhung der Standzeit
der Arbeitsorgane von Gutbett-Walzenmühlen.
Dissertation. TU Bergakademie Freiberg.
Figure 11. Example of stud wear detection over time