Application of Rough Set Theory to Analysis of Hydrocyclone Operation

TL;DR

This paper presents a novel approach combining Self Organizing Map and Rough Set Theory to analyze hydrocyclone operation, extracting dominant rules for decision estimation and comparing it with existing methods.

Contribution

It introduces SORST, a combined SOM and rough set approach, for analyzing hydrocyclone data, which is a new methodology in this context.

Findings

Effective rule extraction from laboratory data

Improved decision estimation accuracy

Comparison shows advantages over SOM-NFIS system

Abstract

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.