Role of Metal Surface Catalysis in the Thermolysis of Morpholine and Ethanolamine under Superheater Conditions
Amines like ethanolamine and morpholine have great potential for protecting steam–water cycles against corrosion, but their thermal stability is limited and acidic decomposition products are a concern because of increased corrosion risk. In this study, metal catalysis of amine thermolysis caused by...
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Published in: | Industrial & engineering chemistry research Vol. 53; no. 50; pp. 19392 - 19397 |
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Main Authors: | , , |
Format: | Journal Article |
Language: | English |
Published: |
American Chemical Society
17-12-2014
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Subjects: | |
Online Access: | Get full text |
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Summary: | Amines like ethanolamine and morpholine have great potential for protecting steam–water cycles against corrosion, but their thermal stability is limited and acidic decomposition products are a concern because of increased corrosion risk. In this study, metal catalysis of amine thermolysis caused by oxides on the inner surface of superheater tubes has been investigated by using a flow reactor and metal tubes of varying sizes and elemental composition. The kinetics of morpholine and ethanolamine thermolysis decreased as the tube size increased. The relationship between the S:V ratio and the degradation rate constant k was linear. Heterogeneous thermolysis accounted for 82–92% of the value of the degradation rate constant k at an S:V ratio of 4.65 mm–1. This decreased to only 6–17% at an S:V ratio of 0.4 mm–1. Although the results varied between the two applied materials, there is no consistent trend that can link thermolysis kinetics to the tube wall composition. Organic acid anion production was weakly related to the amine structure, temperature, and tube diameter but strongly related to the metal oxide composition, with formate and acetate altering as the dominant organic acid anion. A distinction between homogeneous and heterogeneous thermolysis has been made, but the results indicate that a laboratory study with a large enough tube diameter will lead to more reliable predictions. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0888-5885 1520-5045 |
DOI: | 10.1021/ie504217p |