# Analysis Of Hydrocyclone Performance Based On Information Granulation Theory

## Hamed Owladeghaffari ¹, Majid Ejtemaei* ² **And Mehdi Irannajad ³**

²Dept.mining & metallurgical engineering,Teharn,Iran majidejtemaei@gmail.com 
¹ Dept.mining & metallurgical engineering,Teharn,Iran h.o.ghaffari@gmail.com
³ Dept.mining & metallurgical engineering,Teharn,Iran irannajad@aut.ac.ir 

## Key Words: Information Granulation Theory, Som, Nfis, Hydrocyclone.

ABSTRACT.  This paper describes application of information granulation theory, on the analysis of hydrocyclone perforamance. In this manner, using a combining of Self Organizing Map (SOM) and Neuro-Fuzzy Inference System (NFIS), crisp and fuzzy granules are obtained(briefly called SONFIS). Balancing of crisp granules and sub fuzzy granules, within non fuzzy information (initial granulation), is rendered in an open-close iteration. Using two criteria, "simplicity of rules "and "adaptive threoshold error level", stability of algorithm is guaranteed. Validation of the proposed method, on the data set of the hydrocyclone is rendered. 

## 1. **Introduction**

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 has an own 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. In recent years employment of these methods, whether in data analysis or control process in mineral engineering, has extended. As instance, Karr et all (2000), have applied genetic algorithm to optimize the design and performance of hydrocyclones. The used genetic algorithm pursued two objects: tune a fuzzy logic and deriving a genetic algorithm seeking to optimize of hydrocyclone circuit performance by incorporating of the fuzzy model. The main disadvantages of such model, is that did not take in to account the superfluous and abnormal data set (because of inseparable feature of uncertainty and vagueness in pilot plant or laboratory monitoring (or testing)). Employing of similar models of fuzzy logic (fuzzy inference system) and neural network, in cone crusher control (Moshghbar et al 1995) and control of flotation column (Carvalho et al 2002 & Viehia et al 2005) have been pointed. With advancing and extension of intelligent knowledge discovery (Data mining), in different applied sciences, selection of best features, accounting of the vagueness and roughness of 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 