Decarbonization of Energy Systems and AI-Assisted Optimization in Life Cycle Assessment

Topic Information

Dear Colleagues,

For an effective decarbonization of energy-intensive technologies, it is necessary to develop accurate methods for the assessment of environmental impacts, thus supporting the process of making optimal decisions for the transition to sustainable processes and systems. Although the Life Cycle Assessment (LCA) method is recognized in the academic world as a useful tool for quantifying the environmental footprint of energy and industrial processes as well as of different systems, its implementation faces particular challenges such as high computation time, uncertainty of the data used, and complexity of the computational algorithms. This topic explores the use of artificial intelligence (AI) to optimize the LCA tool to increase the accuracy of the obtained data, reduce computation time, and provide decarbonization scenarios that can be analyzed in the promotion of sustainable strategies. This topic aims to develop LCA applications by integrating AI in areas such as the decarbonization of industries using CCUS technologies and the development of hydrogen production and storage processes based on renewable energies.

Prof. Dr. Cristian Dincă
Dr. Lu Liu
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Clean Technologies cleantechnol	4.1	6.1	2019	33.5 Days	CHF 1600	Submit
Energies energies	3.0	6.2	2008	16.8 Days	CHF 2600	Submit
Environments environments	3.5	5.7	2014	22.8 Days	CHF 1800	Submit
Resources resources	3.6	7.2	2012	26.1 Days	CHF 1600	Submit
Sustainability sustainability	3.3	6.8	2009	19.7 Days	CHF 2400	Submit
Processes processes	2.8	5.1	2013	14.9 Days	CHF 2400	Submit

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