Studies On Granularity Data Mining And Its Application In Process Industry

With the large-scale, complication and modernization of process industry such as petroleum and chemical engineering, a large number of data about material, product, equipment, process, operation and so on, are generated from manufacture and research of them. Extracting deeply knowledge, optimal operation condition and manageable pattern from large data of production and management, namely, process industrial data mining, is one of the most important technologies to realize online monitoring, fault diagnosis, safety estimation, product management, marketing analysis and prediction and so on of process industry. In addition, it can provide more effective decision support for industrial safety operation and efficient manufacture.The first important task of process industrial data mining is to select and build effective and suitable data mining algorithms to process industrial data. The granularity data mining can research system from different level versions, mine process operating model and relative variables in different granularity space according to practical applications. Moreover, it can discover the relationships and rules among process variables and find the local or global optimization among different granularity space to solve the process diagnosis and operating optimization effectively.The cracking furnace system is the key equipment in ethylene manufacturing process, which has a lot of typical characteristic of general continues petrochemical process. In this paper the process industrial granularity data mining is mainly based on the ethylene cracking furnace system.According to the high dimensions and uncertainty of process industrial data, the fuzzy set and rough set of granularity data mining are studied for process data. To overcome the roughness that it can not completely discern knowledge granularity, the nature relationship between information granularity principle and roughness ofknowledge is studied, and the algorithms of granularity computing and optimal attribute reduct based on granularity entropy are proposed. To pursue fast and efficient granularity data mining algorithm for process industry, rough data mining model based on information granularity matrix algorithm is proposed according to fuzzy information granularity matrix principle. And on the basis of information granularity matrix algorithm, the information compression granularity matrix algorithm and incremental rule acquisition are proposed. The proposed rough data mining model and data mining algorithm are understood easily and operating conveniently, can decrease the store space and improve the efficiency of process industrial data mining.According to process data with noise, multi-frequency and dynamics characteristic, the paper makes several researches as follows: Adopting wavelet transformation based on data moving window to extract feature and filter noise, and then studies granularity data mining on nonlinear principal component analysis (NLPCA) based on inp(?) training neural network (ITNN), moreover improves the learning algorithm of ITN(?) Using the multi-granularity space analysis of wavelet transformation, adapt multi-scale nonlinear PCA (MSNLPCA) granularity data mining method is proposed extract feature and abnormal states monitoring for process industrial time-serial da(?) Using the proposed information granularity matrix algorithm to acquire fuzzy diagnos(?) rules, fuzzy discretization method of continued data is studied on normal distribution o(?) process data and fuzzy-rough information state space model about real-time proces data is built. Based on above researches multi-granularity process monitoring an diagnosis model integrating MSNLPCA-Rough set is proposed.According to the strong coupling and high interrelation of process data, tw granularity data mining methods are studied, namely, dynamical fuzzy clustering-ranking and kernel clustering algorithm. Using PCA to decide the number of fuzzy clustering and ranking the variables in each cluster based on interrelation index, fuzzy clustering-ranking algorithm is used in process alarms optimal management to improve operating efficiency and avoid blindness of dealing with alarms. Dynamic kernel clustering algorithm is used to recognize optimal operating pattern and select the better cracking crude oil to improve the operating ability of ethylene cracking furnace...