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Prediction Method for Torrefied Rice Husk Based on Gray-scale Analysis

机译:基于灰阶分析的米糠预测方法

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Torrefaction pretreatment has recently gained attention for the potential improvement in biomass properties. Otherwise, visible image-processing technology for analyzing properties of torrefied biomass was evaluated for possible use in the future online process control. In this study, torrefied rice husk from different torrefaction temperatures (180–330 °C) was obtained. After torrefaction, the biochar was characterized to determine the effects of torrefaction temperature on the properties, including the proximate analysis, solid yield (SY), and higher heating values. In addition, the color values, including red-green-blue (RGB) values, and grayscale (GS) of torrefied rice husk, were measured. The results show that the fixed carbon and ash increased from 17.39 to 35.13 and 7.06 to 38.41%, respectively, while volatile matters decreased from 71.47% to a minimum of 22.89% with the increase of torrefaction temperature from 105 to 330 °C. The SY remained higher than 46% even at the most severe torrefaction condition because of the high ash content and high remaining lignin. Moreover, the higher heating values of torrefied rice husk were increased from 14.80 to 17.82 MJ/kg when increased the pretreatment temperature. RGB values were decreased with the increase of torrefaction temperature. The GS analysis results show that the color of torrefied rice husk changed from yellow to brown at light torrefaction and black at severe torrefaction. GS of torrefied rice husk shows a good correlation (R = 0.9998) with torrefaction temperature. Prediction equations with higher fitting degree between GS and proximate analysis (R2 > 0.9900), high heat values (R2 = 0.9999), and SY (R2 = 0.9979), which are developed to reflect the changing characteristics of torrefied rice husk. The results show that the prediction method based on GS is a promising technology to measure the properties of torrefied rice husk.
机译:烘焙预处理最近因生物质特性的潜在改善而受到关注。否则,对用于分析烘焙生物质特性的可见图像处理技术进行了评估,以用于将来的在线过程控制。在这项研究中,获得了来自不同烘焙温度(180-330°C)的烘焙过的稻壳。焙干后,对生物炭进行表征,以确定焙干温度对性能的影响,包括最近的分析,固体产率(SY)和较高的发热量。此外,还测量了烘焙过的稻壳的颜色值,包括红-绿-蓝(RGB)值和灰度(GS)。结果表明,随着焙烧温度从105°C升高至330°C,固定碳和灰分分别从17.39%升高至35.13%和7.06%升高至38.41%,而挥发性物质从71.47%降低至最低22.89%。由于高灰分含量和高残留木质素,即使在最苛刻的烘烤条件下,SY仍保持高于46%。此外,当提高预处理温度时,烘焙过的稻壳的较高发热量从14.80升高至17.82 MJ / kg。 RGB值随烘焙温度的升高而降低。 GS分析结果表明,焙干的稻壳颜色在浅焙烧下从黄色变为棕色,在强焙烧下从黑色变为黑色。烘焙过的稻壳的GS与烘焙温度具有良好的相关性(R = 0.9998)。在GS和接近分析(R2> 0.9900),高发热量(R2 = 0.9999)和SY(R2 = 0.9979)之间具有较高拟合度的预测方程被开发出来,以反映出米糠壳的变化特性。结果表明,基于GS的预测方法是一种很好的技术,可以用于测量焙烤过的稻壳的特性。

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