388
R. Schiemann et al.
Conclusion and Outlook
A growing demand for increased automation and operational optimization of process
plants is observed in the minerals processing industry.
To meet this demand for metallurgical roasting plants, a Roaster Optimizer has
been developed, which is a digital system applying advanced automation methods in
combination with digitized process know-how as well as decades of experience with
plant design and operation. The resulting combination is a powerful tool continuously
controlling the plant in a close to optimal way, taking care of stable and profitable
operation while respecting process limits and protecting downstream equipment for
reduced maintenance. One of the backbones is constituted by detailed thermodynamic process models on a plant level as well as equipment-specific special models.
This combination provides insight and operational awareness on multiple levels of
plant operation, which can be leveraged by the underlying optimization routines.
Several Pretium advanced control systems such as digital systems are currently
rolled out for various processes including zinc and pyrite roasting, alumina calcination as well as iron ore pelletizing plants. Analyses show a high potential for energy
consumption optimization in all of these technologies.
References
1. Runkel M, Haus S, Kauvosaari S, Hammerschmidt J (2019) Digitalization to maximize performance of metallurgical roasting plants. Paper presented at EMC 2019, Düsseldorf, 23–26 June
2019
2. Haus S, Mehl S, Lagerstedt A (2015) Optimizing iron ore agglomeration plant performance—
Outotec’s solutions for plant monitoring and sustainable operation. https://www.outotec.com/
globalassets/products/sintering–pelletizing/optimus_whitepaper_2018.pdf. Accessed 2 Sept
2019
3. Schiemann R, Haus S, Orth A (2018) Dynamic training simulators for the process industry: an
application to power plants. Heat Process 4:57–66
4. Schlautmann M et al (2011) Dynamic process models for on-line control of steelmaking
processes, exemplified for the VOD process. Stahl und Eisen 2011(10)
5. Kemmerich M, Storch H (2015) Process heat recovery and digitalization in sulphuric acid plants.
Paper presented at 3rd international symposium on innovation and technology in the phosphate
industry, Marrakech, Morocco, 18–20 May 2015
R. Schiemann et al.
Conclusion and Outlook
A growing demand for increased automation and operational optimization of process
plants is observed in the minerals processing industry.
To meet this demand for metallurgical roasting plants, a Roaster Optimizer has
been developed, which is a digital system applying advanced automation methods in
combination with digitized process know-how as well as decades of experience with
plant design and operation. The resulting combination is a powerful tool continuously
controlling the plant in a close to optimal way, taking care of stable and profitable
operation while respecting process limits and protecting downstream equipment for
reduced maintenance. One of the backbones is constituted by detailed thermodynamic process models on a plant level as well as equipment-specific special models.
This combination provides insight and operational awareness on multiple levels of
plant operation, which can be leveraged by the underlying optimization routines.
Several Pretium advanced control systems such as digital systems are currently
rolled out for various processes including zinc and pyrite roasting, alumina calcination as well as iron ore pelletizing plants. Analyses show a high potential for energy
consumption optimization in all of these technologies.
References
1. Runkel M, Haus S, Kauvosaari S, Hammerschmidt J (2019) Digitalization to maximize performance of metallurgical roasting plants. Paper presented at EMC 2019, Düsseldorf, 23–26 June
2019
2. Haus S, Mehl S, Lagerstedt A (2015) Optimizing iron ore agglomeration plant performance—
Outotec’s solutions for plant monitoring and sustainable operation. https://www.outotec.com/
globalassets/products/sintering–pelletizing/optimus_whitepaper_2018.pdf. Accessed 2 Sept
2019
3. Schiemann R, Haus S, Orth A (2018) Dynamic training simulators for the process industry: an
application to power plants. Heat Process 4:57–66
4. Schlautmann M et al (2011) Dynamic process models for on-line control of steelmaking
processes, exemplified for the VOD process. Stahl und Eisen 2011(10)
5. Kemmerich M, Storch H (2015) Process heat recovery and digitalization in sulphuric acid plants.
Paper presented at 3rd international symposium on innovation and technology in the phosphate
industry, Marrakech, Morocco, 18–20 May 2015