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dc.contributor.authorBae, Seong-Yun-
dc.contributor.authorYu, Eun-Uk-
dc.contributor.authorBelbase, Ankit-
dc.contributor.authorYun, Min-Seo-
dc.contributor.authorOh, Min-Kyeong-
dc.contributor.authorLee, Jae-Beom-
dc.contributor.authorLee, Ung-
dc.contributor.authorYoon, Hyung-Chul-
dc.contributor.authorMazari, Shaukat Ali-
dc.contributor.authorHan, Sang-Jun-
dc.contributor.authorMoon, Jong-Ho-
dc.date.accessioned2026-05-07T08:00:22Z-
dc.date.available2026-05-07T08:00:22Z-
dc.date.created2026-05-07-
dc.date.issued2026-04-
dc.identifier.issn0959-6526-
dc.identifier.urihttps://pubs.kist.re.kr/handle/201004/154636-
dc.description.abstractWith the growing global population, food demand is projected to rise significantly, leading to increased urea consumption. Therefore, sustainable urea synthesis pathways are essential to address both food security and climate change. In this study, gray, blue, and green urea production processes were systematically designed, optimized and comparatively analyzed. To identify the key process variables, both local sensitivity analysis (LSA) and global sensitivity analysis (GSA) were conducted. Subsequently, multi-objective optimization (MOO) was performed to improve the economic and environmental performance of each process. A comprehensive 4E (Energy, Exergy, Economic, Environmental) assessment was followed, including energy consumption (EnC), exergy destruction (ExD), levelized cost of urea (LCOU), and greenhouse gas emissions (GHGe). The energy–exergy–based MOO enabled reductions of 3.60 % in LCOU and 6.82 % in GHGe for the gray process. The EnC and ExD results showed a consistent trend: Gray < Blue < Green. In contrast to EnC, the reforming and urea synthesis sections were identified as the dominant contributors in terms of ExD. Meanwhile, the LCOU ($/t-Urea) followed the same trend as ExD: Gray (309.10) < Blue (325.99) < Green (498.46). In contrast, GHGe (tCO2-eq/t-Urea) exhibited the opposite trend: Gray (1.06) > Blue (0.90) > Green (0.66). Therefore, policy support is important to facilitate the transition toward sustainable urea production processes. For instance, under a carbon tax of $60/tCO2, the LCOU of the blue process becomes lower than that of the gray process. Furthermore, when the price of a renewable energy certificate (REC) exceeds $30/REC, the green process can achieve greater economic competitiveness over the gray and blue processes.-
dc.languageEnglish-
dc.publisherElsevier BV-
dc.titleFrom Energy–Exergy optimization to improved economic and environmental performance in sustainable urea production: A comparative study of gray, blue, and green processes-
dc.typeArticle-
dc.identifier.doi10.1016/j.jclepro.2026.148138-
dc.description.journalClass1-
dc.identifier.bibliographicCitationJournal of Cleaner Production, v.554-
dc.citation.titleJournal of Cleaner Production-
dc.citation.volume554-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.identifier.wosid001733595800001-
dc.identifier.scopusid2-s2.0-105033973571-
dc.relation.journalWebOfScienceCategoryGreen & Sustainable Science & Technology-
dc.relation.journalWebOfScienceCategoryEngineering, Environmental-
dc.relation.journalWebOfScienceCategoryEnvironmental Sciences-
dc.relation.journalResearchAreaScience & Technology - Other Topics-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalResearchAreaEnvironmental Sciences & Ecology-
dc.type.docTypeArticle-
dc.subject.keywordPlusMULTIOBJECTIVE OPTIMIZATION-
dc.subject.keywordPlusTECHNOECONOMIC ANALYSIS-
dc.subject.keywordAuthorSustainable urea-
dc.subject.keywordAuthorExergy analysis-
dc.subject.keywordAuthorMulti-objective optimization-
dc.subject.keywordAuthorTechno-economic analysis-
dc.subject.keywordAuthorEnvironmental impact analysis-
dc.subject.keywordAuthorPolicy-embedded analysis-
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