distinguished, more than the past. For example, Zadeh (2005) has pointed the role of fuzzy set theory in system theory (especially in control) will be increased during future years. The granulation of information theory (Zadeh, 1997) 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 (Yo &Yao, 2002). The process of constructing IGs is referred to as information granulation and emphasized the fact that a plethora of details doesn't necessity amount to knowledge. This was first pointed out in the pioneering work of Zadeh(1979). Granulation serves as an abstraction mechanism for reducing an entire conceptual burden. By changing the size of the IGS, we can hide, or reveal more or less details (Bargiela & Pedrycz, 2003). The second reason is about the behavior of data. In many practical data sets, such as mineral processing engineering, the normal group and abnormal group are considered separate populations. If we construct IGs by the similarity of numerical data, the amount of IGs in normal group will be remarkably smaller than the size of normal numerical data. In other words, if we consider IGs instead of numerical data, it might increase the proportion of abnormal data (Su et al, 2006). In this study, using two computational intelligence (CI) theories, neural networks, and fuzzy inference system, based on information granulation theory, an algorithm to analyses hydrocyclone data will be presented. In this model, self-organizing feature map, Neuro-Fuzzy Inference System is utilized to construct IGs. To determine suitable granulation level, the two criteria, "simplicity of rules "and "suitable error level", are supposed. Reset of paper has been organized as follow: section 2 covers a brief review on construction of information granules. Next section, describes proposed method. Finally, in section 4, the actual case comes from a hydrocyclone in laboratory scale. 

## 2. Construction Of Information Granules

Information granules are collections of entities that are arranged due to their similarity, functional adjacency, or indiscernibility relation. The process of forming information granules is referred to as IG. There are many approaches to construction of IG, for example SOM, Fuzzy C-Means (FCM), and RST. The granulation level depends on the requirements of the project. The smaller IGs come from more detailed processing. On the other hand, because of complex innate feature of information in real world and to deal with vagueness, adopting of fuzzy and rough analysis or the combination form of them is necessary. In this study, the main aim is to develop a hierarchical extraction of IGs using three main steps: 1-Random selection of initial crisp granules: this step can be set as "Close World" Assumption .But in many applications, the assumption of complete information is not feasible (CWA), and only cannot be used. In such cases, an Open World Assumption 