Determination of the lower calorific and ash values of the lignite coal by using artificial neural networks and multiple regression analysis

• Autorzy: Gulec Mahmut , Gulbandilar Eyyup

Identyfikatory

DOI

10.5277/ppmp18149

• Języki publikacji: EN

Abstrakty

EN

The calorific value of coal varies depending on type of coal and foreign matter content. The calorific value of coal from pits is determined by analyzing moisture, volatile matter, ash and sulfur content in laboratories. This analysis process imposes a burden on businesses both in terms of time and cost. However, calorific value, in particular, can be determined through simpler methods by using ash and moisture values. The aim of this study was to develop a model that reduces the time and labor costs of coal companies by determining the calorific value and ash content of coal with the back-propagation algorithm of artificial neural networks (ANN). The model design was developed based on the data that was obtained from the laboratory analyses of raw coals from the pits of Tuncbilek and Seyitomer mining areas in Turkey. The values of moisture, volatile matter, original ash and sulfur were determined as input variables, and the lower calorific values and ash content were selected as output variables. The lower calorific values (LCV) and Ash estimated by the developed model were compared with the LCV obtained in the laboratory tests and the results showed a correlation. In addition, two different ANN models and multiple regression analysis (MRA) were developed to obtain the single output of the LCV and ash parameters with similar features. As a result, the ANN model and MRA equation models proposed in this study was shown to successfully estimate the LCV and ash content of coals without performing laboratory analyses.

Słowa kluczowe

EN

lignite chemical analyses; artificial neural networks; Seyitomer lignite; Tuncbilek lignite

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Physicochemical Problems of Mineral Processing
• Rocznik: 2019
• Tom: Vol. 55, iss. 2
• Strony: 400--406
• Opis fizyczny: Bibliogr. 10 poz., rys., tab.

Twórcy

autor

Gulec Mahmut

• General Directorate of Turkish Coal, Garp Lignite Enterprise, Tavsanli, Kutahya, Turkey

autor

Gulbandilar Eyyup

• Eskisehir Osmangazi University, Faculty of Engineering and Architecture, Department of Computer Engineering, Meselik Campus, Eskisehir, Turkey

Bibliografia

• ALLAHKARAMI, E., IGDER, A., FAZLAVI, A., REZAI, B., 2017. Prediction of Co(II) and Ni(II) ions removal from wastewater using artificial neural network and multiple regression models. Physicochem. Probl. Miner. Process. 53(2), 1105−1118
• AMBROZIC, T., TURK, G., 2003. Prediction of subsidence due to underground mining by artificial neural networks. Computers and Geosciences. 29, 627–637
• CHELGANI, S.C., MESROGHLI, S., HOWER, J.C., 2010. Simultaneous prediction of coal rank parameters based on ultimate analysis using regression and artificial neural network. International Journal of Coal Geology. 83, 31–34.
• FENG, Q., ZHANG, J., ZHANG, X., WEN, S., 2015. Proximate analysis-based prediction of gross calorific value of coals: A comparison of support vector machine, alternating conditional expectation and artificial neural network. Fuel Processing Technology. 129, 120–129.
• GULBANDILAR, E., KOCAK, Y., 2016. Application of expert systems in prediction of flexural strength of cement mortars, Computers and Concrete. 18(1), 1-16.
• GULEC, M., 2014. Determination of the lower calorific value on the lignite coal by using artificial neural networks (in Turkish), Ms Thesis, Dumlupinar University, Institute of Science and Technology, Turkey.
• KURAL, O., 1998. Coal Properties, Technology and Environmental Associations (In Turkish), Istanbul, Turkey, pp. 59-69.
• YIN, C., ROSENDAHL, L., LUO, Z., 2003. Methods to improve prediction performance of ANN models. Simulation Modelling Practice and Theory. 11, 211–222.
• ZHAO, K., CHEN, S., 2011. Study on artificial neural network method for ground subsidence prediction of metal mine. Procedia Earth and Planetary Science. 2, 177 – 182.
• ZHOU, Q., HERRERA-HERBERT, J., HIDALGO, A., 2017. Predicting the Risk of Fault-Induced Water Inrush Using the Adaptive Neuro-Fuzzy Inference System. Minerals. 7, 55; doi:10.3390/min7040055.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).