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Modelling the Gieseler fluidity of coking coals modified by multicomponent plastic wastes

机译:多组分塑料废料改性焦煤的Gieseler流动性建模

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摘要

A novel method for predicting the Gieseler maximum fluidity (F-max) of a coal + plastic mixture formed from a relative proportion of the plastics present in a multicomponent waste is proposed. A training set of five most-common thermoplastics in household wastes (HDPE, LDPE, PP, PS and PET), binary and ternary plastic mixtures was used to construct multivariable linear regression (MLR) models. Validation was conducted by means of an external set of mixed plastics and real unsorted plastic wastes. The results obtained from the numerical solution of the MLR models were found to be in satisfactory agreement with the experimental data obtained using a Gieseler plastometer. The F-max values fitted the models with determination coefficients of >0.96 and root mean square errors of prediction of 0.048 and 0.058. All the plastic mixtures tested represented a wide spectrum in concentration of the five polymers contained in municipal plastic wastes and a global plastic addition of 2 wt% to the coal was always used. The starting point for this study was to determine the effect of each single plastic on the reduction in fluidity of various coking coals and an industrial coking blend. Afterwards, the exponential functions of Fmax of the blends of coal and binary/ternary plastic mixtures were useful to analyze the changes in Gieseler Fmax with varying proportions of components. Based on the results, the coal responses were statistically treated and MLR models were developed. (C) 2015 Elsevier Ltd. All rights reserved.
机译:提出了一种预测煤+塑料混合物的吉塞勒最大流动性(F-max)的新方法,该混合物由多组分废物中存在的塑料的相对比例形成。训练了一套五种最常见的生活垃圾(HDPE,LDPE,PP,PS和PET),二元和三元塑料混合物的热塑性塑料,以构建多变量线性回归(MLR)模型。验证是通过外部一组混合塑料和未分类的真实塑料废料进行的。发现从MLR模型的数值解获得的结果与使用Gieseler塑性仪获得的实验数据令人满意地吻合。 F-max值使模型的确定系数大于0.96,并且预测的均方根误差为0.048和0.058。所有测试的塑料混合物在市政塑料废料中所含的五种聚合物的浓度范围很广,并且始终使用煤中2 wt%的全球塑料添加量。这项研究的出发点是确定每种单一塑料对各种炼焦煤和工业炼焦混合物流动性降低的影响。之后,煤和二元/三元塑料混合物的混合物的Fmax的指数函数可用于分析Gieseler Fmax在组分比例变化时的变化。根据结果​​,对煤响应进行了统计处理,并建立了MLR模型。 (C)2015 Elsevier Ltd.保留所有权利。

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