Application of artificial neural networks to co-combustion of hazelnut husk–lignite coal blends. (January 2016)

• Record Type: Journal Article
• Authors: Yıldız, Zeynep; Uzun, Harun; Ceylan, Selim; Topcu, Yıldıray
• Is Part Of: Bioresource technology. Volume 200(2016)
• Page Start: 42
• Page End: 47
• Publication Date: 2016-01
• Subjects: Co-combustion -- Hazelnut husk -- Lignite coal -- TGA -- Artificial neural network; Biomass -- Periodicals; Biomass energy -- Periodicals; Bioremediation -- Periodicals; Agricultural wastes -- Periodicals; Factory and trade waste -- Periodicals; Organic wastes -- Periodicals; Bioénergie -- Périodiques; Déchets agricoles -- Périodiques; Déchets industriels -- Périodiques; Déchets organiques -- Périodiques; Déchets (Combustible) -- Périodiques; 662.88
• DOI: 10.1016/j.biortech.2015.09.114
• Languages: English
• ISSNs: 0960-8524

Abstract

Highlights: First study on application of ANN model to co-combustion process. The coefficient of determination for the developed model was 0.9995. ANN model predicted TG curves for co-combustion of blends effectively. Abstract: 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.