Machine Learning Enables Rapid Screening of Reactive Fly Ashes Based on Their Network Topology
Machine Learning Enables Rapid Screening of Reactive Fly Ashes Based on Their Network Topology
复制标题
机器学习能够根据网络拓扑快速筛选反应性飞灰
DOI：
10.1021/acssuschemeng.0c06978
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发表时间：
2021
影响因子：
8.4
通讯作者：
Bauchy, Mathieu
中科院分区：
文献类型：
--
作者：
Song, Yu;Yang, Kai;Chen, Jingyi;Wang, Kaixin;Sant, Gaurav;Bauchy, Mathieu
粉煤灰是煤燃烧产生的副产物，可以作为辅助胶凝材料代替普通波特兰水泥用于混凝土中。这为燃煤电厂运营商带来了收入，也降低了混凝土粘合剂部分的CO2强度（如果粉煤灰被认为没有碳足迹，则用粉煤灰取代每吨OPC可避免0.9吨CO2排放）。然而，在混凝土中使用粉煤灰，迄今为止，由于其作为SCM的性能的不确定性，仅限于小于20质量%的替代水平。虽然粉煤灰在混凝土中替代水泥的能力主要由其非晶相的反应性决定，但是表征粉煤灰的非晶相是复杂的并且成本过高，这迄今为止阻止了粉煤灰的任何高通量筛选以评估它们作为SCM的适用性。在这里，我们介绍了一种基于机器学习的方法，该方法仅基于快速，廉价的批量表征（X射线荧光：XRF），通过使用粉煤灰的非晶相的网络拓扑结构作为其反应性的结构代理，实现了对反应性粉煤灰的鲁棒筛选。在100多个粉煤灰数据集的基础上，我们训练了一个人工神经网络（ANN）模型，该模型提供了粉煤灰非晶相的质量分数及其网络拓扑结构的准确预测。这种新方法旨在最大限度地利用从常规生产中获得的飞灰，并确定回收目前储存在蓄水池中的飞灰的机会。
Fly ash, a byproduct of coal combustion, can be used as supplementary cementitious material (SCM) to replace ordinary portland cement (OPC) in concrete. This generates revenue for coal power plant operators and also reduces the CO2intensity of the binder fraction of a concrete (each ton of OPC replaced by fly ash results in 0.9 ton of avoided CO2emissions, if the fly ash is considered to have no carbon footprint). However, the use of fly ash in concrete has thus far been limited to replacement levels less than 20 mass % due to uncertainties in their performance as SCM. Although the ability of a fly ash to replace cement in concrete is largely determined by the reactivity of its amorphous phase, characterizing fly ashes’ amorphous phase is complex and cost prohibitive, which has thus far prevented any high-throughput screening of fly ashes to assess their suitability as SCMs. Here, we introduce a machine-learning-based methodology that enables robust screening of reactive fly ashes based solely on fast, inexpensive bulk characterization (X-ray fluorescence: XRF), by using the network topology of fly ashes’ amorphous phase as a structural proxy for their reactivity. On the basis of a data set of more than 100 fly ashes, we train an artificial neural network (ANN) model that offers accurate predictions of the mass fraction of fly ashes’ amorphous phase and the network topology thereof. This new method seeks to maximize the beneficial use of fly ashes obtained from routine production, as well as to identify opportunities for the reclamation of ashes that are presently stored in impoundments.
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影响因子：
3.5
作者：
Han Liu;Zipeng Fu;Kai Yang;Xinyi Xu;M. Bauchy
通讯作者：
Han Liu;Zipeng Fu;Kai Yang;Xinyi Xu;M. Bauchy
表征粉煤灰作为水泥替代剂的适用性的改进基础
DOI：
--
发表时间：
2017
期刊：
影响因子：
--
作者：
Tandré Oey;Jason Timmons;P. Stutzman;J. Bullard;Magdalena Balonis;M. Bauchy;G. Sant
通讯作者：
G. Sant
影响因子：
3.3
作者：
M. Bauchy
通讯作者：
M. Bauchy
通过 ICP-OES 测量工业废物的反应性：印度硅质生物质灰的案例研究
影响因子：
3.9
作者：
Uvegi, Hugo;Chaunsali, Piyush;Traynor, Brian;Olivetti, Elsa
通讯作者：
Olivetti, Elsa
煤飞灰中的玻璃：最新进展
DOI：
--
发表时间：
1987
期刊：
影响因子：
--
作者：
R. Hemmings;E. E. Berry
通讯作者：
E. E. Berry