RESEARCH ON THE COMBUSTION PROCESS USING TIME SERIES

Box G.E.P, Jenkins G.M.: Analiza szeregów czasowych Prognozowanie i sterowanie, Warszawa 1983.
  Google Scholar

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.
DOI: https://doi.org/10.1016/j.atmosenv.2008.07.020   Google Scholar

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.
DOI: https://doi.org/10.1016/j.jclepro.2017.06.167   Google Scholar

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.
  Google Scholar

Komada P.: Analiza procesu termicznej przeróbki biomasy. Monografie – Polska Akademia Nauk. Komitet Inżynierii Środowiska, Warszawa 2019.
  Google Scholar

Korbicz J., Kościelny J.M., Kowalczuk Z., Cholewa W.: Diagnostyka procesów, Modele, Metody sztucznej inteligencji, Zastosowania. Wydawnictwo Naukowo-Techniczne, Warszawa 2002.
  Google Scholar

Kordylewski W. i inni.: Spalanie i paliwa. Politechnika Wrocławska, Wrocław 2008.
  Google Scholar

Kotyra A., Wojcik W., Gromaszek K., Smolarz A., Jagiełło, K.: Assessment of biomass-coal co-combustion on the basis of flame image. Przegląd Elektrotechniczny 88(11b), 2012, 241–243.
  Google Scholar

Kotyra A., Wójcik W., Gromaszek K., Bazil G.: Application of flame image series analysis in estimation of biomass and coal combustion operating point. Przegląd Elektrotechniczny 8(92)2016, 129–132.
DOI: https://doi.org/10.15199/48.2016.08.35   Google Scholar

Lu G., Yan Y., Colechin M.: A digital imaging based multifunctional flame monitoring system. IEEE Transactions on instrumentation and measurement, 53(4), 2004, 1152–1158.
DOI: https://doi.org/10.1109/TIM.2004.830571   Google Scholar

Mahla S.K., Parmar K.S., Singh J., Dhir A., Sandhu S.S., Chauhan B.S.: Trend and time series analysis by ARIMA model to predict the emissions and performance characteristics of biogas fueled compression ignition engine. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 1–12.
DOI: https://doi.org/10.1080/15567036.2019.1670286   Google Scholar

Ong C.S., Huang J.J., Tzeng G.H.: Model identification of ARIMA family using genetic algorithms. Applied Mathematics and Computation 164(3), 2005, 885–912.
DOI: https://doi.org/10.1016/j.amc.2004.06.044   Google Scholar

Sanchez A.B., Ordonez C., Lasheras F.S., Juez F.J.D., Roca-Pardinas J.: Forecasting SO2 Pollution Incidents by means of Elman Artificial Neural Networks and ARIMA Models, Abstract and Applied Analysis 2013, Article ID 238259.
DOI: https://doi.org/10.1155/2013/238259   Google Scholar

Savchuk Т. О., Kozachuk A., Gromaszek K., Sugurova L.: Forecasting the state of technogenic emergency situation on the railway transport using data mining technologies. Przegląd Elektrotechniczny 1, 2014, 50–54.
  Google Scholar

Sawicki D., Kotyra A., Perdesh K.: Ekstrakcja cech obrazów płomienia współspalania węgla i biomasy z wykorzystaniem wizyjnego systemu diagnostycznego. Przegląd Elektrotechniczny 92(8), 2016, 133–136.
DOI: https://doi.org/10.15199/48.2016.08.36   Google Scholar

Sawicki D., Kotyra A., Akhmetova A., Baglan I., Suleymenov A.: Using Optical Methods for Process State Classification of Co-firing of Coal and Biomass. Annual Set The Environment Protection 2(18), 2016, 404–415.
  Google Scholar

Sawicki D., Kotyra A.: A quality factor of co-firing pulverized coal and biomass. Przegląd Elektrotechniczny 92(11), 2016, 140–143.
DOI: https://doi.org/10.15199/48.2016.11.35   Google Scholar

Smolarz A., Wójcik W., Gromaszek K., Komada P., Lytvynenko V.I., Mussabekov N., Toigozhinova A.: Artificial intelligence methods in diagnostics of coal-biomass blends cocombustion in pulverised coal burners. Environmental Engineering V, 2017, 311–317.
DOI: https://doi.org/10.1201/9781315281971-44   Google Scholar

Wójcik W., Gromaszek K., Shegebayeva Z., Suleimenov B., Burlibay A.: Optimal control for combustion process. Przegląd Elektrotechniczny 90(4), 2014, 157–160.
  Google Scholar

Wójcik W., Gromaszek K., Smailova S.: Using optical signals for pulverised coal combustion process optimal control to increase economic efficiency of the boiler. Actual Problems of Economics 4, 2013, 307–311.
  Google Scholar

Wojcik W., Kotyra A., Komada P., Golec T.: Fiber optic system detecting the type of burned fuel in power boilers. Proc. of SPIE 5125, 2003.
DOI: https://doi.org/10.1117/12.497731   Google Scholar

Wójcik W.: Application of fibre-optic flame monitoring systems to diagnostics of combustion process in power boilers. Bulletin of the Polish Academy of Sciences – Technical Sciences 56(2), 2008, 177–195.
  Google Scholar

Wójcik W.: Światłowodowy układ do monitorowania procesu spalania, PAK 53(11), 2007, 24–28.
  Google Scholar

Zhou H., Li Y., Tang Q., Lu G., Yan Y.: Combining flame monitoring techniques and support vector machine for the online identification of coal blends. Journal of Zhejiang University – Science A 18(9), 2017, 677–689.
DOI: https://doi.org/10.1631/jzus.A1600454   Google Scholar

Zyska T., Wójcik W., Imanbek B., Zhirnova O.: Diagnostyka stanu czujnika termoelektrycznego w procesie zgazowania biomasy. Rocznik Ochrona Środowiska 18(2)/2016, 652–666.
  Google Scholar