气候变化领域集成服务门户(CCPortal): Real-time tracking of renewable carbon content with AI-aided approaches during co-processing of biofeedstocks

CCPortal

DOI	10.1016/j.apenergy.2024.122815
	Real-time tracking of renewable carbon content with AI-aided approaches during co-processing of biofeedstocks
	Cao, Liang; Su, Jianping; Saddler, Jack; Cao, Yankai; Wang, Yixiu; Lee, Gary; Siang, Lim C.; Pinchuk, Robert; Li, Jin; Gopaluni, R. Bhushan
发表日期	2024
ISSN	0306-2619
EISSN	1872-9118
起始页码	360
卷号	360
英文摘要	Decarbonization of the oil refining industry is essential for reducing carbon emissions and mitigating climate change. Co-processing bio feed at existing oil refineries is a promising strategy for achieving this goal. However, accurately quantifying the renewable carbon content of co -processed fuels can be challenging due to the complex process involved. Currently, it can only be achieved through expensive offline 14C measurements. To address this issue, with high-quality and large-scale commercial data, our study proposes a novel approach that utilizes data -driven methods to build inferential sensors, which can estimate the real -time renewable content of biofuel products. We have collected over 1,000,000 co-processing data points from refineries under different bio feed co-processing ratios and operational conditions-the largest dataset of its kind to our knowledge We use interpretable deep neural networks to select model inputs, then apply robust linear regression and bootstrapping techniques to estimate renewable content and confidence interval. Our method has been validated with four previous 14C measurements during co-processing at the fluid catalytic cracker. This novel methods provides a practical solution for the industry and policymakers to quantify renewable carbon content and accelerate the transition to a more sustainable energy system.
英文关键词	Renewable engergy; Interpretable machine learning; Inferential sensor; Co-processing; Bio-fuel; Renewable carbon tracking
语种	英语
WOS研究方向	Energy & Fuels ; Engineering
WOS类目	Energy & Fuels ; Engineering, Chemical
WOS记录号	WOS:001183737300001
来源期刊	APPLIED ENERGY
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/295083
作者单位	University of British Columbia; University of British Columbia
推荐引用方式 GB/T 7714	Cao, Liang,Su, Jianping,Saddler, Jack,et al. Real-time tracking of renewable carbon content with AI-aided approaches during co-processing of biofeedstocks[J],2024,360.
APA	Cao, Liang.,Su, Jianping.,Saddler, Jack.,Cao, Yankai.,Wang, Yixiu.,...&Gopaluni, R. Bhushan.(2024).Real-time tracking of renewable carbon content with AI-aided approaches during co-processing of biofeedstocks.APPLIED ENERGY,360.
MLA	Cao, Liang,et al."Real-time tracking of renewable carbon content with AI-aided approaches during co-processing of biofeedstocks".APPLIED ENERGY 360(2024).