利用高岭石和回收母液合成生态友好型分层β沸石及其环境影响评估

IF 9.1 Q1 ENGINEERING, CHEMICAL Green Chemical Engineering Pub Date : 2024-01-10 DOI:10.1016/j.gce.2024.01.002
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引用次数: 0

摘要

开发生产高性能沸石的可持续技术对于实现工业领域的绿色生产至关重要。在此,我们报告了一种利用高岭石和回收母液合成分层 Beta 沸石的生态友好型路线。结果表明,在 11 次循环实验中,母液中未被利用的物质(如硅和 Na+)保持了稳定的浓度。此外,在乙醇酯化乙酰丙酸的过程中,合成的 Beta 沸石与初始沸石相比,在 11 次循环实验中仍具有相当的理化性质和催化性能。生命周期评估表明,与不使用回收母液相比,使用回收母液合成 Beta 沸石可减少 23% 的全球升温潜能值和 36% 的资源损耗-用水量。这从数量上表明,这项工作提出的方法确实是一种可持续的方法,它极大地提高了原材料的利用效率,减轻了环境负担。
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Eco-friendly synthesis and environmental impact assessment of hierarchical Beta zeolite from kaolinite and recycled mother liquor

The development of sustainable techniques to produce high-performance zeolite is essential to achieve green production in industry. Herein, we report an eco-friendly route to synthesizing hierarchical Beta zeolite from kaolinite and recycled mother liquor. The results reveal that the unutilized species (such as silicon species and Na+) in mother liquor stayed in a stable concentration during eleven recycled experiments. Moreover, the synthesized Beta zeolites still have comparable physicochemical properties and catalytic performance in the esterification of levulinic acid with ethanol over the initial zeolite although eleven recycled experiments. Life cycle assessment exhibits that the synthesis of Beta zeolite with recycled mother liquor can reduce global warming potential by 23% and resource depletion-water use by 36% compared to that without recycled mother liquor. This quantitatively demonstrates that the approach proposed in this work is really a sustainable one, extremely increasing the utilization efficiency of raw materials and decreasing the environmental burden.

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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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