Department of Thermal Science and Energy Engineering, University of Science and Technology of China, Jinzhai Road, Hefei 230026, China.
Department of Thermal Science and Energy Engineering, University of Science and Technology of China, Jinzhai Road, Hefei 230026, China.
Sci Total Environ. 2021 Jan 10;751:142293. doi: 10.1016/j.scitotenv.2020.142293. Epub 2020 Sep 12.
The harm done to the environment by coal gangue was very serious, and it is urgent to adopt effective methods to dispose of coal gangue in order to prevent further environmental damage. Co-pyrolysis experiments of coal gangue (CG) and peanut shell (PS) were carried out using thermogravimetry-Fourier transform infrared spectroscopy (TG-FTIR) under nitrogen atmosphere. The heavy metal was detected using the inductively coupled plasma-optical emission spectroscopy (ICP-OES). CG and PS were mixed according to the mass ratio of 1:0, 3:1, 1:1, 1:3 and 0:1. The samples were heated to 1000 °C at the heating rate of 10 °C/min, 20 °C/min and 30 °C/min. The comprehensive pyrolysis index (CPI) of CG, C3P1, C1P1, C1P3 and PS is 0.17 × 10, 9.75 × 10, 35.47 × 10, 100.94 × 10 and 192.72 × 10% ·min·°C. The kinetic parameters were calculated by model-free methods (Flynn-Wall-Ozawa and Kissinger-Akahira-Sunose). The gas products generated at different temperatures during the pyrolysis experiment were detected by Fourier transform infrared spectrometer. The heating rate, temperature and mixing ratio are the input parameters of artificial neural network (ANN), and the remaining mass percentage of sample during the pyrolysis is the output parameter. The ANN model was established and used to predict thermogravimetric experimental data. The ANN18 model is the best model for predicting the co-pyrolysis of CG and PS.
煤矸石对环境的危害非常严重，因此迫切需要采取有效方法来处理煤矸石，以防止进一步的环境破坏。采用热重-傅里叶变换红外光谱联用（TG-FTIR）技术，在氮气气氛下对煤矸石（CG）和花生壳（PS）进行共热解实验，采用电感耦合等离子体-原子发射光谱（ICP-OES）检测重金属。按照质量比为 1:0、3:1、1:1、1:3 和 0:1 将 CG 和 PS 混合。将样品以 10°C/min、20°C/min 和 30°C/min 的升温速率加热至 1000°C。CG、C3P1、C1P1、C1P3 和 PS 的综合热解指数（CPI）分别为 0.17×10、9.75×10、35.47×10、100.94×10 和 192.72×10%·min·°C。采用无模型法（Flynn-Wall-Ozawa 和 Kissinger-Akahira-Sunose）计算动力学参数。采用傅里叶变换红外光谱仪检测不同温度下热解实验产生的气体产物。升温速率、温度和混合比是人工神经网络（ANN）的输入参数，热解过程中样品的剩余质量百分比是输出参数。建立了 ANN 模型，并用于预测热重实验数据。ANN18 模型是预测 CG 和 PS 共热解的最佳模型。
