Smart Manufacturing Pathways for Industrial Decarbonization and Thermal Process Intensification

被引:4
|
作者
Price, Christopher R. [1 ]
Nimbalkar, Sachin U. [1 ]
Thirumaran, Kiran [1 ]
Cresko, Joe [2 ]
机构
[1] Oak Ridge Natl Lab, Mfg Sci Div, NTRC, One Bethel Valley Rd,Mailstop 6472, Oak Ridge, TN 37830 USA
[2] US DOE, Ind Efficiency & Decarbonizat Off, 1000 Independence Ave SW, Washington, DC 20585 USA
来源
关键词
thermal process intensification; energy efficiency; industrial decarbonization; smart manufacturing; industry; 4; 0; advanced manufacturing; emissions reductions; hydrogen production;
D O I
10.1520/SSMS20220027
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Rapid decarbonization is fast becoming the primary environmental and sustainability initiative for many economic sectors. Industry consumes more than 30 % of all primary energy in the United States and accounts for nearly 25 % of all greenhouse gas (GHG) emissions. More than 70 % of energy consumed by the industrial sector is related to thermal processes, which are also the largest contributors of carbon emissions, overwhelmingly due to the combustion of fossil fuels. Thermal process intensification (TPI) seeks to dramatically improve the energy performance of thermal systems through technology pillars focusing on alternative energy sources and processes, supplemental technologies, and waste heat management. The impacts of TPI have significant overlap with the goals of industrial decarbonization (ID) that seeks to phase out all GHG emissions from industrial activities. Emerging supplemental technologies such as smart manufacturing (SM) and the industrial internet of things (IoT) enable significant opportunities for the optimization of manufacturing processes. Combining strategies for TPI and ID with SM and IoT can open and enhance existing opportunities for saving time and en-ergy via approaches such as tighter control of temperature zones, better adjustment of thermal systems for variations in production levels and feedstock properties, and increased process throughput. Data collected by smart processes will also enable new advanced solutions such as digital twins and machine learning algorithms to further improve thermal system savings. This paper examines the individual pathways of TPI, ID, and SM and how the combination of all three can accelerate energy and GHG reductions.
引用
收藏
页码:41 / 53
页数:14
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