光催化反应二氧化碳转化的前瞻性生命周期评估

IF 9.1 Q1 ENGINEERING, CHEMICAL Green Chemical Engineering Pub Date : 2023-10-11 DOI:10.1016/j.gce.2023.10.003
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引用次数: 0

摘要

二氧化碳转化逐渐被视为社会有效利用碳源、避免化石二氧化碳排放带来的气候危机的更好途径。而二氧化碳技术规模的决定应依赖于其对环境的影响。在这项工作中,生命周期评估模型评估了采用两种不同催化剂(NiAl-LDH 和 Co-ZIF-9)的光催化反应过程转化二氧化碳的环境性能。该分析考虑了六个影响类别,包括气候变化、酸化潜力、非生物资源损耗、富营养化潜力、臭氧层破坏潜力和光化学氧化潜力。结果表明,与 NiAl-LDH 光催化剂路线相比,Co-ZIF-9 光催化剂的二氧化碳转化具有更好的环境影响。此外,Co-ZIF-9 催化剂方案的总环境负担也低于传统的 CO 生产路线。敏感性分析表明,在两种方案中,催化剂的回收性能对生产工艺非常敏感。这项研究可为光催化反应转化二氧化碳的稳健决策提供一个框架,有助于决策者决定工业化的可行性。
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Prospective life cycle assessment of CO2 conversion by photocatalytic reaction

CO2 conversion is gradually seen as a better way for society to effectively use carbon sources and avoid climate crisis associated with fossil CO2 emissions. And the decision to deploy CO2 technology scale should be relied on its environmental impact. In this work, life cycle assessment model evaluates the environmental performance of CO2 conversion by photocatalytic reaction process with two different catalysts (NiAl-LDH and Co-ZIF-9). Six impact categories considered in this analysis, including climate change, acidification potential, depletion of abiotic resources, eutrophication potential, ozone layer depletion potential, and photochemical oxidation potential. Results indicated that CO2 conversion with Co-ZIF-9 photocatalyst has a better environmental impact than the NiAl-LDH photocatalyst route. Moreover, the Co-ZIF-9 catalyst scenario also has a lower total environmental burden than the conventional CO production route. Sensitivity analysis shows that recycle performance of the catalyst is highly sensitive to the production process in two scenarios. This study could provide a framework for robust decisions in CO2 conversion by photocatalytic reaction, which is useful for policymakers to decide the feasibility of industrialization.

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来源期刊
Green Chemical Engineering
Green Chemical Engineering Process Chemistry and Technology, Catalysis, Filtration and Separation
CiteScore
11.60
自引率
0.00%
发文量
58
审稿时长
51 days
期刊最新文献
OFC: Outside Front Cover Outside Back Cover Outside Back Cover OFC: Outside Front Cover Integration of physical information and reaction mechanism data for surrogate prediction model and multi-objective optimization of glycolic acid production
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