# Application Of Rough Set Theory To Analysis Of Hydrocyclone Operation

H.Owladeghaffari, M.Ejtemaei&M.Irannajad Department of Mining&Metulrgical Engineering, Amirkabir University of Technology, Tehran, Iran Outline: This paper describes application of rough set theory, on the analysis of hydrocyclone operation. In this manner, using Self Organizing Map (SOM) as preprocessing step, best crisp granules of data are obtained. Then, using a combining of SOM and rough set theory (RST)-called SORST-, the dominant rules on the information table, obtained from laboratory tests, are extracted. Based on these rules, an approximate estimation on decision attribute is fulfilled. Finally, a brief comparison of this method with the SOM-NFIS system (briefly SONFIS) is highlighted. 

## A Brief Introuduction:

In the design of mineral processing cycle, one of the most important issues is the selection of hydrocyclone in different parts of the site. However, prediction of hydrocyclone performance using direct or indirect modeling faces with some difficulties. Apart from analytical, numerical, or experimental modeling, modeling based on intelligent systems can be supposed as an excellent situation, which is ensued by data engineering, machine learning, and stochastic learning theorems. With advancing and extension of intelligent knowledge discovery (Data mining), in different applied sciences, selection of best features, accounting of the uncetainty in the monitored data, are the main challenges of the most sciences. 

Because of being the uncertainty feature of the monitored data, accounting of uncertainty approaches such probability, fuzzy set and rough set theories to knowledge acquisition, extraction of rules and prediction of unknown cases, have been distinguished, more than the past. The granulation of information theory [1] covers the mentioned approaches in two formats: crisp information granulation and fuzzy information granulation. There are two reasons why we propose this concept to tackle uncertainty in the monitored mineral processing data. The first one is human instinct. As human beings, we have developed a granular view of the world. When describing a problem, we tend to shy away from numbers and use aggregates to ponder the question instead. This is especially true when a problem involves incomplete, uncertain, or vague information. It may be sometimes difficult to differentiate distinct elements, and so one is force consider "information granules (IG) which one collection of entities arranged together due to their similarity, functional adjacency, and indistinguishability. 

In this study, using three computational intelligence (CI) theories, neural networks, fuzzy inference system and rough set based on information granulation theory, two algorithms to analyses of hydrocyclone data will be presented. In our models, self-organizing feature map, Neuro-Fuzzy Inference System and rough set are utilized to construct IGs. Details of our instruments can be followed in [2], [3], [4]. 