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A data‐based optimal setting method for the coking flue gas denitration process
Author(s) -
Li Yaning,
Wang Xuelei,
Liu Zhenjie,
Bai Xiwei,
Tan Jie
Publication year - 2019
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.23226
Subject(s) - flue gas , process (computing) , computer science , process optimization , set (abstract data type) , mathematical optimization , engineering , process engineering , mathematics , waste management , environmental engineering , programming language , operating system
Abstract This study focuses on developing an optimal setting method for the first integrated coking flue gas desulphurization and denitration device in China. Maintaining the denitration process in a state of optimal economic efficiency has become an issue in production optimization control. This paper proposes a data‐based two‐stage nonparametric optimization method to optimize the operation of the denitration process. A principal component regression (PCR)‐based multiple case fusion case‐based reasoning (CBR) method is proposed to obtain the initial optimization set points. To overcome the steady‐state modelling difficulties associated with the process, a local modelling method for the coking flue gas denitration process is developed using an improved just‐in‐time learning (JITL) algorithm. Taking the preset values obtained above as the initial value of an active set algorithm, the optimization problem can be solved in a timely and precise manner. The intelligent setting software has been developed for running industrial applications, and the results demonstrate the effectiveness of the proposed optimization approach.