Upgrading carbonaceous materials: Coal, tar, pitch, and beyond

被引:40
|
作者
Zang, Xining [1 ,2 ,3 ]
Dong, Yuan [4 ,5 ]
Jian, Cuiying [6 ]
Ferralis, Nicola [7 ]
Grossman, Jeffrey C. [7 ]
机构
[1] Tsinghua Univ, Dept Mech Engn, Beijing 100084, Peoples R China
[2] Tsinghua Univ, Minist Educ, Key Lab Adv Mat Proc Technol, Beijing 100084, Peoples R China
[3] Tsinghua Univ, State Key Lab Tribol, Beijing 100084, Peoples R China
[4] Hangzhou Dianzi Univ, Sch Mech Engn, Hangzhou 310018, Peoples R China
[5] Shenzhen Artificial Intelligence Ind Assoc, Shenzhen 518031, Japan
[6] York Univ, Dept Mech Engn, 4700 Keele St, Toronto, ON M3J IP3, Canada
[7] MIT, Dept Mat Sci & Engn, Cambridge, MA 02139 USA
关键词
LASER-INDUCED GRAPHENE; DENSITY-FUNCTIONAL THEORY; ANTHRACITE COAL; ORGANIC-MATTER; REAXFF; COMBUSTION; GRAPHITE; REPRESENTATION; CONDUCTIVITY; SIMULATION;
D O I
10.1016/j.matt.2021.11.022
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Heavy carbonaceous materials (HCMs) such as coal are mostly used for nonrenewable power generation, while derivatives such as tar and pitch are often discarded by-products. Upgrading HCMs for a broad array of potential applications including their use in batteries, membranes, and catalysts could play an important role in the global demand for carbon neutralization. The diversity of HCMs is a technological asset that allows for the direct synthesis of highly customizable materials suited for specific applications. Herein, we will discuss state-of-the-art engineering techniques that can be employed to upscale HCMs and how the nature of the carbon source affects the final product. Further, we illustrate how machine learning (ML) methods can empower the screening of carbonaceous sources from this large family of materials with extremely diverse chemistry. We will also discuss data-driven methods to identify and prioritize the effects of individual processing parameters that could lead to a consistent as well as flexible manufacturing process.
引用
收藏
页码:430 / 447
页数:18
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