Fuzzy modelling of carbon dioxide in a burning process

Ruusunen, Mika; Leiviskä, Kauko

doi:10.1016/s0967-0661(03)00142-4

Cited by 20 publications

(6 citation statements)

References 15 publications

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“…Imprecision of factor borders accompanying the combustion process has contributed to the use of fuzzy logic. This is confirmed by numerous publications, where examples of the work are [30,56]; especially important in the context of this article there is the study by [56]. Depicting a fuzzy system of monitoring carbon dioxide in the combustion process is a Takagi-Sugeno inference model [62].…”

Section: Introductionsupporting

confidence: 58%

“…For example, the OFN describing set temperature T Z1 = [56,56,56,56] informs the controller to "hold temperature of 56" For the number T Z2 = [56,58,60,72] the directing means, "I prefer temperature 72, but may be no less than 56." For the reverse OFN number there is the statement, "I prefer 56, but nothing will happen if it is 72."…”

Section: Output Variables Arementioning

confidence: 99%

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Fuzzy Numbers Applied to a Heat Furnace Control

Dobrosielski

Czerniak

Zarzycki

et al. 2017

Studies in Fuzziness and Soft Computing

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This chapter presents a trend phenomenon and application of the fuzzy controller for Ordered Fuzzy Numbers (OFNs). The authors propose to use a trend in a combustion process for a simplified model of a solid fuel fired furnace. Better control over the process translates into reduced CO 2 emission as well as optimal use of the furnace. When carrying out the fuzzy observation of the efficiency of the furnace, the authors apply the OFN notation by connecting the trend of furnace temperature changes with the order appropriate for this notation. Thanks to this approach it is possible to enhance information without the additional need to multiply the transmitted data. It is particularly effective in the multidimensional fuzzy observation when monitoring not only the condition of the temperature in the furnace but also the ambient temperature and the temperatures in several rooms of the heated building. The chapter is a continuation of a series of papers published by the authors on multidimensional fuzzy observation using OFN notation. A controller in the conventional fuzzy logic approach is also presented in the chapter. The controller was built using jFuzzyLogic software. The fact that there are more and more OFN applications seems to be a good predictor of the development of this generalization, an example of which is the problem analyzed in this chapter.

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Section: Introductionsupporting

confidence: 58%

Section: Output Variables Arementioning

confidence: 99%

Fuzzy Numbers Applied to a Heat Furnace Control

Dobrosielski

Czerniak

Zarzycki

et al. 2017

Studies in Fuzziness and Soft Computing

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show abstract

“…Ruusunen and Leivisk presented a fuzzy model-based approach for approximating carbon dioxide content in a wood combustion process. The model outputs are used to calculate real time combustion quality measures and lower caloric values of the wood fuel, including the approximation of the oxygen content [21].…”

Section: Combustion Processmentioning

confidence: 99%

Fuzzy Logic Applications In Chemical Processes

Emami¹

2010

J. Math. Computer Sci.

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Abstract:Fuzzy logic a new approach was intended to emulate human reasoning using calculations and operations with fuzzy groups and linguistic variables. Fuzzy variables describe qualitative expressions such as very slow, slow, fast, very fast, and so on. The application of fuzzy logic techniques has been increasing rapidly in the last few years. Fuzzy logic is used in target tracking, pattern recognition, robotics, power systems, controller design, chemical engineering, biomedical engineering, vehicular technology, economy management and decision making, aerospace applications, communications and networking, electronic engineering, and civil engineering. In many chemical engineering systems, the classification of product quality characteristics is performed by human experts, due to the absence of measuring devices. The development of mathematical models for such systems is a rather difficult task, since no equations based on first principles can be written. Chemical engineering has employed fuzzy logic in the detection of chemical agents as well as gas recognition. It has also been applied to processes control, batch distillation column, separation process, and kinetics. In this research, we investigate these applications in more detail.

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“…(Wahlroos, 1980) However, in a real combustion processes the straightforward determination only the maximum flame temperature can be impractical because of non-adiabatic processes and strongly changing process conditions. On the other hand, fusing the information collected from multiple temperature sensors can lead to success especially in small-scale combustion, as in model-based monitoring of carbon monoxide, CO 2 , (Ruusunen & Leiviskä, 2004).…”

Section: Theoretical Basismentioning

confidence: 99%