Characterization of proteins in Soy Sauce Residue and its hydrolysis by enzymes

Yang, Chunhui; Wang, Wenping; Zhang, Jinlan; Yan, Zhao; Zhao, Jinyang

doi:10.1201/b19136-68

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“…In this case, the generated hematite should be cubic structure γ-hematite [(Fe3+)8,Tetra(Fe40/33+,V8/3)OctaO32] [45], as shown in Figure 8. However, our previous work and some available literature indicated that the characteristic peak located at 2θ ≈ 33°, belonging to α-hematite as depicted in the blue circled area of Figure 8b, was generally observed in X-ray diffraction (XRD) patterns of iron-based steels exposed to SCW with a dissolved oxygen content of several ppm [3,64], or in ppb level DO SCW for thousands of hours [65]. This phenomenon implys that the generated hematite may be predominant rhombohedral, corundum structure α-hematite with all the Fe 3+ located on the octahedral sites [62].…”

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confidence: 99%

Modelling and Analysis of the Corrosion Characteristics of Ferritic-Martensitic Steels in Supercritical Water

Wang

et al. 2019

Materials

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The dependencies of weight gain of 9-12 Cr ferritic-martensitic steels in supercritical water on each of seven principal independent variables (temperature, oxygen concentration, flow rate, exposure time, and key chemical composition and surface condition of steels) have been predicted using a supervised artificial neural network (ANN). The relative significance of each independent variable was uncovered by fuzzy curve analysis, which ranks temperature and exposure time as the most important. The optimized ANN, not only satisfactorily represents the experimentally-known non-linear relationships between the corrosion characteristics of F-M steels and the key independent variables (demonstrating the effectiveness of this technique), but also predicts and reveals that the effects of oxygen concentration on the weight gains, to a certain degree, is influenced by the flow rate and temperature. Finally, according to the ANN predicted-results, departure of oxidation kinetics from the parabolic law, and basic cause of chromium content in steel substrate influencing the corrosion rate, and the synergetic effects of dissolved oxygen concentration, flow rate, and temperature, are discussed and analyzed.

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Section: Resultsmentioning

confidence: 99%

Modelling and Analysis of the Corrosion Characteristics of Ferritic-Martensitic Steels in Supercritical Water

Wang

et al. 2019

Materials

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Characterization of proteins in Soy Sauce Residue and its hydrolysis by enzymes

Cited by 1 publication

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Modelling and Analysis of the Corrosion Characteristics of Ferritic-Martensitic Steels in Supercritical Water

Modelling and Analysis of the Corrosion Characteristics of Ferritic-Martensitic Steels in Supercritical Water

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