Robust Statistical Process Monitoring for Biological Nutrient Removal Plants

Abstract

This paper presents an approach by combining robust fuzzy principal component analysis (RFPCA) technique with the multiscale principal component analysis (MSPCA) methodology. Thus the two typical issues of industrial data, outliers and changing process conditions are solved by resulting MS-RFPCA methodology. The RFPCA is proved to be effective in mitigating the impact of noise, and MSPCA has become necessary due to the nature of complex systems in which operations occur at different scales. The efficiency of the proposed technique is illustrated on a simulated benchmark of biological nitrogen removal process. © Springer International Publishing Switzerland 2014.