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

被引：87

作者：

Yildiz, Zeynep ^{[1
]}

Uzun, Harun ^{[1
]}

Ceylan, Selim ^{[1
]}

Topcu, Yildiray ^{[1
]}

机构：

[1] Ondokuz Mayis Univ, Dept Chem Engn, TR-55139 Samsun, Turkey

来源：

BIORESOURCE TECHNOLOGY | 2016年 / 200卷

关键词：

Co-combustion; Hazelnut husk; Lignite coal; TGA; Artificial neural network; THERMOGRAVIMETRIC ANALYSIS; COMBUSTION BEHAVIOR; PYROLYSIS KINETICS; TORREFIED BIOMASS; BITUMINOUS COAL; SLUDGE; WASTE;

D O I：

10.1016/j.biortech.2015.09.114

中图分类号：

S2 [农业工程];

学科分类号：

0828 ;

摘要：

The artificial neural network (ANN) theory is applied to thermal data obtained by non-isothermal thermogravimetric analysis (TGA) from room temperature to 1000 degrees 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. (C) 2015 Elsevier Ltd. All rights reserved.

引用

页码：42 / 47

页数：6

共 24 条

[1] RETRACTED: Artificial neural networks applications in wind energy systems: a review (Retracted article. See vol. 84, pg. 173, 2018) [J].

Ata, Rasit .

RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2015, 49 :534-562

[2] Coal Chemical-Looping Combustion for Electricity Generation: Investigation for a 250 MWe Power Plant [J].

Authier, Olivier ;

Le Moullec, Yann .

GHGT-11, 2013, 37 :588-597

[3] A perspective for potential and technology of bioenergy in Turkey: Present case and future view [J].

Bilgen, Selcuk ;

Keles, Sedat ;

Sarikaya, Ikbal ;

Kaygusuz, Kamil .

RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2015, 48 :228-239

[4] Thermogravimetric analysis of the behavior of sub-bituminous coal and cellulosic ethanol residue during co-combustion [J].

Buratti, C. ;

Barbanera, M. ;

Bartocci, P. ;

Fantozzi, F. .

BIORESOURCE TECHNOLOGY, 2015, 186 :154-162

[5] Neural network modelling of coal pyrolysis [J].

Carsky, M ;

Kuwornoo, DK .

FUEL, 2001, 80 (07) :1021-1027

[6] Co-combustion of brewer's spent grains and Illinois No. 6 coal: Impact of blend ratio on pyrolysis and oxidation behavior [J].

Celaya, Ana M. ;

Lade, Amanda T. ;

Goldfarb, Jillian L. .

FUEL PROCESSING TECHNOLOGY, 2015, 129 :39-51

[7] Thermal and kinetic behaviors of biomass and plastic wastes in co-pyrolysis [J].

Cepeliogullar, Ozge ;

Putun, Ayse E. .

ENERGY CONVERSION AND MANAGEMENT, 2013, 75 :263-270

[8] Pyrolysis kinetics of hazelnut husk using thermogravimetric analysis [J].

Ceylan, Selim ;

Topcu, Yildiray .

BIORESOURCE TECHNOLOGY, 2014, 156 :182-188

[9] Thermal characteristics and kinetics of refining and chemicals wastewater, lignite and their blends during combustion [J].

Chen, Jianbiao ;

Mu, Lin ;

Cai, Jingcheng ;

Yin, Hongchao ;

Song, Xigeng ;

Li, Aimin .

ENERGY CONVERSION AND MANAGEMENT, 2015, 100 :201-211

[10]

Gajic D., 2015, ENERGY

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