Abstract
In the combustion process, one of the most important tasks is related to maintaining its stability. Numerous methods of monitoring, diagnostics, and analysis of the measurement data are used for this purpose. The information recorded in the combustion chamber constitute one-dimensional time series. In the case of non-stationary time series, which can be transformed into the stationary form, the autoregressive integrated moving average process can be employed. The paper presented the issue of forecasting the changes in flame luminosity. The investigations discussed in the work were carried out with the ARIMA model (p,d,q). The presented forecasts of changes in flame luminosity reflect the actual processes, which enables to employ them in diagnostics and control of the combustion process.
Subject
General Earth and Planetary Sciences,General Engineering,General Environmental Science
Reference25 articles.
1. Box G.E.P, Jenkins G.M.: Analiza szeregów czasowych Prognozowanie i sterowanie, Warszawa 1983.
2. Díaz-Robles L.A., Ortega J.C., Fu J.S. et al.: A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: the case of Temuco, Chile. Atmospheric Environment 42(35), 2008, 8331–8340.
3. Ding S., Dang Y.G., Li X.M., Wang J.J., Zhao K.: Forecasting Chinese CO2 emissions from fuel combustion using a novel grey multivariable model. Journal of Cleaner Production 162, 2017, 1527–1538.
4. Jiang S., Yang C., Guo J., Ding Z.: ARIMA forecasting of China’s coal consumption, price and investment by 2030. Energy Sources, Part B: Economics, Planning, and Policy 13(3), 2018, 190–195.
5. Komada P.: Analiza procesu termicznej przeróbki biomasy. Monografie – Polska Akademia Nauk. Komitet Inżynierii Środowiska, Warszawa 2019.
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献