基于响应面方法的沥青含量、废棕榈油熟料粉和废稻秆灰可持续沥青路面建模与优化:一项试点研究

Nura Shehu Aliyu Yaro , Muslich Hartadi Sutanto , Noor Zainab Habib , Madzlan Napiah , Aliyu Usman , Ahmad Hussaini Jagaba , Abdulnaser M. Al-Sabaeei
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引用次数: 16

摘要

废物管理在世界各地变得越来越重要,将农业废物纳入路面行业是一种在改善混合料性能的同时实现可持续性的有前景的战略。在本研究中,我们优化并确定了用废棕榈油熟料粉(WPOCP)和废稻草灰(WRSA)改性的沥青混凝土混合料的最佳沥青粘结剂含量,以改善其工程性能。为了优化三个自变量(沥青结合料、WPOCP和WRSA含量)对混合料容重(BUW)、总混合料空隙率(VTM)、马歇尔稳定性和流量值的相互作用,使用了马歇尔混合料设计方法和响应面法(RSM)和Box-Behnken设计。制备了含有2%、4%、6%和8%(重量)沥青混合物的WPOCP样品,以及含有25%、50%、75%和100%(重量)填料的WRSA样品,其中沥青粘合剂含量为混合物的4%至6%(重量)。统计模型结果显示,所有响应都是显著的,BUW、VTM、马歇尔稳定性和流量的高相关系数(R2)分别为0.9840、0.9971、0.9920和0.9891。观察到输入变量的个体效应和三个变量之间的协同作用会影响所有的反应。数值优化产生的最佳WPOCP、WRSA和沥青含量值分别为8%、74%和5%。所有响应的平均误差均小于5%,表明预测值与实验数据吻合良好,生成的模型准确反映了实验结果。根据研究结果,可以得出结论,RSM是确定沥青混合料中最佳沥青结合料和改性剂含量的有效方法。它能够识别影响沥青混合料响应的最重要变量,并能够优化混合料以提高性能。此外,在沥青混合料中加入WPOCP和WRSA可以改善体积和马歇尔性能,从而在路面行业中形成一种更可持续的方法。
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Modeling and optimization of asphalt content, waste palm oil clinker powder and waste rice straw ash for sustainable asphalt paving employing response surface methodology: A pilot study

Waste management is becoming increasingly important around the world, and incorporating agro-waste into the pavement industry represents a promising strategy for achieving sustainability while improving mixture properties. In this study, we optimize and determine the optimum asphalt binder content of asphalt concrete mixtures modified with waste palm oil clinker powder (WPOCP) and waste rice straw ash (WRSA) to improve their engineering properties. To optimize the interactions between three independent variables (asphalt binder, WPOCP, and WRSA content) on mixture bulk unit weight (BUW), void in the total mix (VTM), Marshall stability, and flow values, the Marshall mix design approach and response surface methodology (RSM) with a Box-Behnken design were used. WPOCP samples containing 2%, 4%, 6%, and 8% by weight of asphalt mixtures were prepared, as were WRSA samples containing 25%, 50%, 75%, and 100% by weight of filler, with asphalt binder content ranging from 4 to 6% by weight of the mix. The statistical model results show that all responses were significant, with high coefficients of correlation (R2) of 0.9840, 0.9971, 0.9920, and 0.9891 for the BUW, VTM, Marshall stability, and flow, respectively. Individual effects of the input variables and synergistic interactions between the three variables were observed to influence all of the responses. Numerical optimization produced optimum WPOCP, WRSA, and asphalt content values of 8%, 74%, and 5%, respectively. The mean error for all responses was less than 5%, indicating that predicted values agree well with experimental data and that generated models accurately reflect experimental results. Based on the findings of the study, it can be concluded that RSM is an effective method for determining the optimal asphalt binder and modifier content in asphalt mixtures. It enables the identification of the most important variables influencing the response of the asphalt mixture and enables mixture optimization for improved performance. Furthermore, incorporating WPOCP and WRSA into asphalt mixtures was found to improve both volumetric and Marshall properties, resulting in a more sustainable approach in the pavement industry.

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