Research On Fault Detection Method For Batch Processes Based On Multi-phases Division

With the advantages of the flexible production mode, various types of processing, short production cycle, the batch process gradually replaced the traditional continuous process production mode and has become the main mode of production in aspects of biological pharmaceutical industry, chemical industry, electronics manufacturing and so on. And the proportion in the domestic industry structure also continued to increase. However, in the batch production process, there are many interference factors to make negative impacts including the product quality cannot be effectively guaranteed and the security risks in the production process keep increasing. Based on this background, research on batch process fault detection technology has aroused widespread concern and attention in related fields of professional institutions and scholars, and they has gained abundant research results.This paper based on multi period batch process is regards as the research background. As the existing fault detection methods exist phases divided inaccurate, poorly performed on non-gaussian and nonlinear characteristics significant data, algorithm design work were carried out, and have achieved good detection results.The main research work is as follows.(1) Analysis to summarize the specific characteristics and data features of the batch process. The method of principal component analysis(PCA) application principle in fault detection is explained in detail. Additionally, based on Multi-way principal component analysis(MPCA), the specific fault detection method in batch process is analyzed further.(2) For batch processes multi-period problems, the traditional k-means algorithm for batch processes time division need given the definite number of multi-period and the random initialization of clustering center is uncertain which caused multi-period division is not accurate divided into and clustering results. So this paper puts forward the improved k-means algorithm, the initial clustering center is identified through the largest minimum distance method and the application of clustering validity function to determine the best number in order to realize multi-period division of multi-period batch processes adaptive partitioning.(3) The fault detection methods based on MPCA does not apply to non-gaussian and nonlinear characteristics of significant batch process data. In-depth study based on independent principal component method(ICA) and K nearest neighbor(KNN) fault detection method of batch processes, based on this, this paper advances a new ICA-KNN method of batch process for fault detection, and effectively solved the problem--- the amount of ICA algorithm statistical control limits is difficult to determine.(4) Respectively improved multi-period division MPCA method and ICA-KNN fault detection method for penicillin fermentation process and semiconductor batch generation process simulation, comparison of the results with other methods show that the above two methods improved the accuracy of fault detection.