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Type-2 fuzzy logic systems in applications: managing data in selective catalytic reduction for air pollution prevention

Niewiadomski Adam, Kacprowicz Marcin

Journal of Artificial Intelligence and Soft Computing Research

2021

Vol. 11, No. 2

85--97

The article presents our research on applications of fuzzy logic to reduce air pollution by DeNOx filters. The research aim is to manage data on Selective Catalytic Reduction (SCR) process responsible for reducing the emission of nitrogen oxide (NO) and nitrogen dioxide (NO2). Dedicated traditional Fuzzy Logic Systems (FLS) and Type-2 Fuzzy Logic Systems (T2FLS) are proposed with the use of new methods for learning fuzzy rules and with new types of fuzzy implications (the so-called ”engineering implications”). The obtained results are consistent with the results provided by experts. The main advantage of this paper is that type-2 fuzzy logic systems with ”engineering implications” and new methods of learning fuzzy rules give results closer to expert expectations than those based on traditional fuzzy logic systems. According to the literature review, no T2FLS were applied to manage DeNOx filter prior to the research presented here.

Dedicated type-2 fuzzy logic systems: a novel approach to DeNOx filtration systems

Niewiadomski A., Kacprowicz M.

Journal of Applied Computer Science

2018

Vol. 26, nr 2

117--130

The paper presents some novel research on applications of Type-2 Fuzzy Logic Systems to support the Selective Catalytic Reduction process (SCR). The aim of the research is to design and test higher order fuzzy logic systems and their genuine modifications to manage data in DeNOx systems responsible for controlling emission of nitrogen oxides (NO, NO2). Since in real applications, it is still performed under the supervision of a human expert, the scope is to replace, at least partially, his/her participation with dedicated type-2 fuzzy logic systems. As the result, it is shown that the proposed systems with new means of learning fuzzy IF-THEN rules allow us to compute parameters much closer to those determined by experts, even in a comparison to some earlier approaches based on traditional fuzzy logic.