Application of artificial neural networks to co-combustion of hazelnut husk-lignite coal blends.

The artificial neural network (ANN) theory is applied to thermal data obtained by non-isothermal thermogravimetric analysis (TGA) from room temperature to 1000°C at different heating rates in air to study co-combustion of hazelnut husk (HH)-lignite coal (LC) blends of various composition. The heating rate, blend ratio and temperature were used in the ANN analysis to predict the TG curves of the blends as parameters that affect the thermal behavior during combustion. The ANN model provides a good prediction of the TG curves for co-combustion with a coefficient of determination for the developed model of 0.9995. The agreement between the experimental data and the predicted values substantiated the accuracy of the ANN calculation.

[1]  S. Ceylan,et al.  Second-generation sustainability: Application of the distributed activation energy model to the pyrolysis of locally sourced biomass–coal blends for use in co-firing scenarios , 2015 .

[2]  Yuanhang Wei,et al.  Kinetics based on two-stage scheme for co-combustion of herbaceous biomass and bituminous coal , 2015 .

[3]  J. Yanik,et al.  Combustion behavior of different kinds of torrefied biomass and their blends with lignite. , 2015, Bioresource technology.

[4]  Huahua Xiao,et al.  Application of genetic algorithm to pyrolysis of typical polymers , 2015 .

[5]  Dražen Lončar,et al.  Artificial neural network modelling approach for a biomass gasification process in fixed bed gasifiers. , 2014 .

[6]  Rajeev K Sukumaran,et al.  Prediction of sugar yields during hydrolysis of lignocellulosic biomass using artificial neural network modeling. , 2015, Bioresource technology.

[7]  Xiaoqian Ma,et al.  Thermogravimetric analysis of co-combustion between microalgae and textile dyeing sludge. , 2015, Bioresource technology.

[8]  M. V. Gil,et al.  Grindability and combustion behavior of coal and torrefied biomass blends. , 2015, Bioresource technology.

[9]  F. Şahin,et al.  Effects of engine parameters on ionization current and modeling of excess air coefficient by artificial neural network , 2015 .

[10]  F. Fantozzi,et al.  Thermogravimetric analysis of the behavior of sub-bituminous coal and cellulosic ethanol residue during co-combustion. , 2015, Bioresource technology.

[11]  Shiwen Fang,et al.  Thermogravimetric analysis of the co-combustion of paper mill sludge and municipal solid waste , 2015 .

[12]  Sedat Keleş,et al.  A perspective for potential and technology of bioenergy in Turkey: Present case and future view , 2015 .

[13]  A. Pütün,et al.  Thermal and kinetic behaviors of biomass and plastic wastes in co-pyrolysis. , 2013 .

[14]  S. Ceylan,et al.  Pyrolysis kinetics of hazelnut husk using thermogravimetric analysis. , 2014, Bioresource technology.

[15]  Xigeng Song,et al.  Thermal characteristics and kinetics of refining and chemicals wastewater, lignite and their blends during combustion , 2015 .

[16]  Olivier Authier,et al.  Coal Chemical-Looping Combustion for Electricity Generation: Investigation for a 250 MWe Power Plant , 2013 .

[17]  S. Godbout,et al.  Combustion kinetic study of woody and herbaceous crops by thermal analysis coupled to mass spectrometry , 2015 .

[18]  Rasit Ata,et al.  Artificial neural networks applications in wind energy systems: a review , 2015 .

[19]  J. L. Goldfarb,et al.  Co-combustion of brewer's spent grains and Illinois No. 6 coal: Impact of blend ratio on pyrolysis and oxidation behavior , 2015 .

[20]  Stefano Di Gennaro,et al.  Modelling of electrical energy consumption in an electric arc furnace using artificial neural networks , 2016 .

[21]  S. Bhattacharya,et al.  Pyrolysis kinetics and reactivity of algae-coal blends , 2013 .

[22]  M. Carsky,et al.  Neural network modelling of coal pyrolysis , 2001 .

[23]  Zhang Yuanyuan,et al.  Investigation of combustion characteristics and kinetics of coal gangue with different feedstock properties by thermogravimetric analysis , 2015 .

[24]  P. Sarkar,et al.  Coal–biomass co-combustion: An overview , 2014 .