Model оf Multifactorial Assessment оf Natural Gas Quality
Journal Title: Вентиляція, освітлення та теплогазопостачання - Year 2019, Vol 30, Issue
Abstract
In accordance with the provisions of the Energy Strategy of Ukraine for the period until 2035, despite the significant development of green energy, natural gas remains the main energy source in the country. In connection with the accession to the common European space of regulation of natural gas trade in the country, all payments for fuel consumption should be made in units of energy. Thus, the main question is the quality of natural gas. One of the peculiarities of the gas supply system is the considerable degree of uncertainty in the change of a large number of disturbing factors and constantly changing parameters of its functioning. A mathematical model based on fuzzy logic theory is expedient for gas quality assessment. According to the results of consideration of the factors characterizing the physico-chemical properties of the natural gas extracted, the quality of its preparation for transportation and the technical operation conditions of the gas distribution system in a settlement, a fuzzy set was obtained to determine the quality of the fuel. According to the "center of mass" method for defuzzification, the fuzzy set corresponds to a fuzzy set that is in the range from one to five points. As an example, for natural gas with limit values in accordance with the Gas Transmission System Code, the quality is estimated at 3.1 points. The fuzzy inference model, together with the dephasification procedure, provides the ability to observe changes in the baseline – the quality of natural gas – depending on changes in quantitative and qualitative factors on the path from the gas field to the consumer. Using the proposed mathematical model in a complex for all levels and sublevels, it is possible to obtain a predicted estimate of the impact of the above factors on fuel quality. In this case, the value obtained is taken from the results of a virtual experiment, which is based on an expert knowledge base.
Authors and Affiliations
К. Predun, Yu. Franchuk, O. Obodianska
Keywords
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