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Small Grants for Exploratory Research: Gas Separations for Process Improvement by Inorganic Hollow Fiber Glass Membraneat Elevated Temperatures for Waste Reduction

Small Grants for Exploratory Research: Gas Separations for Process Improvement by Inorganic Hollow Fiber Glass Membraneat Elevated Temperatures for Waste Reduction
用于探索性研究的小额资助:通过提高无机中空玻璃纤维膜的温度以减少废物,从而改进气体分离工艺
批准号:
9115726
负责人:
Yi Ma
金额:
$3.5万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
1991
资助国家:
美国
项目状态:
已结题
起止时间:
1991-07-01 至 1992-12-31

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项目成果

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中文摘要
翻译
该项目探索了通过工艺改进使用中空玻璃纤维膜减少废物的可行性。一种应用是在高温(400-600℃)下分离H_2S和H_2,以提供浓度更高的H_2S蒸气作为副产品,并使该过程更节能。第二个应用是在酸性水处理过程中分离NH3和H2S。通过允许在更高的温度下进行分离,硫磺回收过程将是重要的,因为NH3的去除。此外,通过新的膜分离工艺回收高纯度的NH3将减少污染和工艺成本。该项目涉及膜选择性的测量和工艺参数的评估。该项目是美国国家科学基金会环境友好制造研究项目中的SGER赠款。NSF的这个项目是计划中的NSF-CCR(化学研究理事会)合资企业的先行者。
英文摘要
The project explores the feasibility of using hollow fiber glass membranes for waste reduction through process improvement. One application is separation of H2S and H2 at high stream temperatures (400 - 600>F), to provide a more concentrated H2S - stream as a byproduct and to make the process more energy efficient. A second application is the separation of NH3 from H2S in a sour water treatment process. By allowing the separation to take place at a higher temperature, the process for sulfur recovery will be important because of the removal of NH3. Furthermore, the recovery of NH3 in high purity through the novel membrane separation process would reduce both pollution and the process costs. The project involves measurements of membranes selectivity and evaluation of process parameters. The project is an SGER grant in the emerging NSF program on studies of Environmentally Benign Manufacturing. This NSF program is a forerunner to a planned NSF-CCR (Council of Chemical Research) joint venture.
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Collaborative Research: Transferable, Hierarchical, Expressive, Optimal, Robust, Interpretable Networks
  • 批准号:
    2031899
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $100.0万
  • 财政年份:
    2020
  • 负责人:
    Yi Ma
  • 依托单位:
SGER: Explorations of Robust Image Classification
Estimation of Hybrid Models as Algebraic Sets
CRS--EHS: Collaborartive Research: An Algebraic Geometric Approach to Hybrid Systems Identification
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