Szczegóły publikacji
Opis bibliograficzny
The combustion of methane from hard coal seams in gas engines as a technology leading to reducing greenhouse gas emissions – electricity prediction using ANN / Marek BOROWSKI, Piotr ŻYCZKOWSKI, Jianwei Cheng, Rafał ŁUCZAK, Klaudia ZWOLIŃSKA // Energies [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 1996-1073. — 2020 — vol. 13 iss. 17 art. no. 4429, s. 1–20. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 18–20, Abstr. — Publikacja dostępna online od: 2020-08-27
Autorzy (5)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 130232 |
|---|---|
| Data dodania do BaDAP | 2020-09-24 |
| Tekst źródłowy | URL |
| DOI | 10.3390/en13174429 |
| Rok publikacji | 2020 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Energies |
Abstract
Greenhouse gases such as carbon dioxide and methane cause global warming and consequently climate change. Great efforts are being made to reduce greenhouse gas emissions with the objective of addressing this problem, hence the popularity of technologies conductive to reducing greenhouse gas emissions. CO(2)emissions can be reduced by improving the thermal efficiency of combustion engines, for example, by using cogeneration systems. Coal mine methane (CMM) emerges due to mining activities as methane released from the coal and surrounding rock strata. The amount of methane produced is primarily influenced by the productivity of the coal mine and the gassiness of the coal seam. The gassiness of the formation around the coal seam and geological conditions are also important. Methane can be extracted to the surface using methane drainage installations and along with ventilation air. The large amounts of methane captured by methane drainage installations can be used for energy production. This article presents a quarterly summary of the hourly values of methane capture, its concentration in the methane-air mixture, and electricity production in the cogeneration system for electricity and heat production. On this basis, neural network models have been proposed in order to predict electricity production based on known values of methane capture, its concentration, pressure, and parameters determining the time and day of the week. A prediction model has been established on the basis of a multilayer perceptron network (MLP).
