A study to predict pyrolytic behaviors of refuse-derived fuel (RDF): Artificial neural network application
A study to predict pyrolytic behaviors of refuse-derived fuel (RDF): Artificial neural network application
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DOI：
10.1016/j.jaap.2016.10.013
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发表时间：
2016-11-01
影响因子：
6
通讯作者：
Haykiri-Acma, Hanzade
中科院分区：
文献类型：
--
作者：
Cepeliogullar, Ozge;Mutlu, Ilhan;Haykiri-Acma, Hanzade
本文通过实验和模拟研究，揭示了垃圾衍生燃料（RDF）这种高度非均质燃料在高温区的热行为。在第一部分中，RDF在热分析仪中从室温到900 ℃以不同的升温速率热解，并通过使用TG-FTIR-MS监测逸出气体分析。然后，将获得的数据用于开发人工神经网络（ANN）模型，该模型可以预测RDF在新的升温速率下的热行为，而无需进行任何实验。选择温度和加热速率作为输入参数，而选择温度依赖性重量损失作为输出参数。详细研究了神经元数目、训练次数、传递函数类型等参数对网络性能的影响，优化了网络拓扑结构。优化研究表明，使用tansig-logsig非线性函数组合训练25次的7-6个神经元的人工神经网络性能最好。通过引入一个新的实验数据集来测试优化的ANN的预测性能。实验值和预测值之间的良好协议表明，人工神经网络可以是一个有前途的工具，即使是非均质燃料，如RDF的热解行为估计。(C)2016爱思唯尔B. V.保留所有权利。
The present study demonstrates the thermal behaviors of refuse-derived fuel (RDF), a highly heterogeneous fuel, at high temperature region by bringing experimental and modelling studies together. In the first part, RDF was pyrolyzed in thermal analyzer from room temperature to 900 degrees C at varying heating rates as well as the evolved gas analysis was monitored by using TG-FTIR-MS. Afterwards, obtained data was used to develop an artificial neural network (ANN) model that can predict thermal behaviors of RDF at a new heating rate without performing any experiments. The temperature and heating rate were selected as input parameters while temperature dependent weight loss was selected as output parameter. The effects of parameters such as neuron number, training number, and the transfer function type on the network performance were investigated in detail to optimize network topology. Optimization studies showed that the best performance was achieved with ANN that had 7-6 neurons trained 25 times with tansig-logsig non-linear function combination. Prediction performance of the optimized ANN was tested by introducing a new experimental dataset. The good agreement between experimental and predicted values revealed that ANN can be a promising tool in pyrolytic behaviors estimation of even heterogeneous fuels such as RDF. (C) 2016 Elsevier B.V. All rights reserved.