A modified fuzzy clustering framework for catalyst activity monitoring in the selective catalytic reduction system

Abstract

The catalyst activity monitoring in the selective catalytic reduction (SCR) system is of great importance for safety and economic operation in the power plant. To address the problem, a framework based on clustering considering time delay has been proposed. A compound parameter, , is put forward in this paper as a strategy to remove the influences from gas volume (power output), inlet NOx concentration, and outlet NOx concentration to the ammonia amount. A modified entropy‐based fuzzy clustering (EFC) method is proposed by a threshold varying model and then tested for its efficiency by four datasets from the University of California, Irvine (UCI) machine learning repository. With the maximum mutual information entropy coefficient (MIC) method for detecting time delay and the modified EFC method, process data from three working levels are handled for automatically obtained clustering centres. The proposed activity value, , is then calculated based on 1440 process data before and after the catalyst replacement shown in boxplot figures. The results of the framework are analyzed to be in accordance with the real working conditions, with values and fluctuation ranges starting to fall near first from the 721st sample in the 24th box.