Authors:
Thiago Nicoli de Abreu
1
;
2
;
Andrea G. Campos Bianchi
3
and
Saul Emanuel Delabrida Silva
3
Affiliations:
1
Programa de Pós-Graduação em Instrumentação, Controle e Automação de Processos de Mineração (PROFICAM), Universidade Federal de Ouro Preto (UFOP) and Instituto Tecnológico Vale (ITV), Ouro Preto, Brazil
;
2
Vale S.A., Vitória, Brazil
;
3
Departamento de Computação (DECOM), Universidade Federal de Ouro Preto (UFOP), Ouro Preto, Brazil
Keyword(s):
Modeling, Machine Learning, Data Mining, Database, Roller Press, Pelletizing.
Abstract:
In recent years, the technology of the roller press has become very useful in the pelletizing processes to comminute the pellet feed and increase the specific surface of the iron ore. It is known that the surface gain is directly related to the productivity and quality gains in the pelletizing process. In view of its importance, the increase in efficiency of the press becomes increasingly necessary, mainly due to its direct impact on the production chain. The large number of variables involved in its operation demonstrate that conventional methods and the knowledge of this process can be improved. For this, the work identifies the variables with the highest production in the specific surface gain, develops a classification model to determine rules of optimal operation settings and presents a model for the prediction of the specific surface variable, seeking gains in determining performance of this asset.