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ABSTRACTFor centrifugal casting 27Cr44Ni5W3Al+MA alloy used in ethylene pyrolysis furnace, its creep behaviours at 1050°C are investigated. The results show that the alloy undergoes three stages during the creep tests under different stress levels ranging from 18 MPa to 30 MPa. The apparent stress index n of the alloy is 6.11, and the creep deformation mechanism is mainly dislocation climbing. During the creep test, the fishbone-like M7C3inside eutectic precipitates in casted alloy gradually transform to massive M23C6at the edge of the precipitate. Creep induced voids germinate and connect at the interface of austenite/M23C6and austenite/Ni3Al, the cavities expand along the M23C6and Ni3Al, forming micro-cracks and the alloy eventually cracks.

Vortex cavitation may be harnessed to intensify many industrial processes. It is essential to develop a highly efficient vortex cavitator for refractory organics degradation. In the present work, a novel swirling vortex cavitator designed with a special structure of six vortex cavities and spiral flow passages was optimized using computational fluid dynamics (CFD) method by the aid of Gambit software and ANSYS fluid simulation software, and its oxytetracycline degradation characteristics were investigated. The optimized vortex cavitaor was superior to the original vortex cavitator resulting from more hydroxyl radicals generated during operation and higher oxytetracycline deg­ radation efficiencies. The degradation efficiency was 89.52% and 92.41% of about 2.0 mg/L initial concentration of oxytetracycline in 10 L solution at 80 min by the original and optimized swirling vortex cavitator, respectively. The optimized vortex cavitator also showed good oxytetracycline degradation ability in aquaculture simulation wastewater. All the degradation processes followed the second­order kinetics model. In the two degradation pathways, multiple by­products were generated by dealkylation, deamination, dihydroxylation and ring­opening induced by vortex cavitation effect.

In the petrochemical industry, centrifugal cast Fe–Cr–Ni heat-resistant alloy tubes are critical components that are exposed to severe conditions for long-time service. The life of the alloy tubes is primarily limited by creep damages, which can evolve into catastrophic failures. In order to improve the creep properties in a low-cost approach, the influence of N + C content on the microstructure evolution and creep behavior of the 25Cr35NiNb alloys has been studied in this work. Creep tests were carried out at 1173 K (900 °C) and 1323 K (1050 °C) with different stress levels. Microstructure investigation on precipitates revealed that the C/N ratio significantly affects the content and morphology of Cr-rich carbides and the phase transition of Nb-rich carbonitrides after thermal exposure. By adopting the threshold stress analysis, the true creep activation energy and stress exponent of all the alloys were found to be ∼270 kJ mol−1and ∼5, respectively, which indicated that dislocation climb is the rate-controlling mechanism of creep. Furthermore, the tailored novel N-bearing alloy possessed superior comprehensive properties of creep resistance and damage tolerance compared to the 25Cr35NiNb grade alloys. Our current findings not only realize improved creep properties by the combined modification of N addition and C/N ratio, but also provide new insights into understanding the microalloying mechanisms of high-temperature materials in general.

Prediction of creep properties is crucial but still very challenging for the heat-resistant alloys, for which data-driven materials genome technologies provide a promising approach. To this end, we have explored the application of deep learning to quantitatively predict the creep rupture time of Fe–Cr–Ni heat-resistant alloys using textual (chemical composition and creep testing conditions) and visual (as-cast microstructure) data. The multi-layer perceptron (MLP) and the convolutional neural network (CNN) are adopted to analyze the textual and visual data, respectively. Then the multi-source heterogeneous data are integrated by the fusion deep learning model, which achieves a significant enhancement in prediction accuracy compared with the single MLP and CNN models. The results demonstrate that the fusion model is efficient in leveraging information from diverse types of data and learning the relation between various features and creep properties in a high-throughput, statistically robust, and physically meaningful manner. The developed model can be regarded as a new workflow to predict creep properties and give insights into the inverse design of alloys. Our work is equally applicable to other classes of materials and properties as well, where the accumulated data can be reconsidered to guide discoveries.

ABSTRACTIn the present study, the microstructure evolution of 35Cr45NiNb+MA ethylene cracking furnace tubes during service and its effect on the properties were investigated. According to the results, in the early stage of service, the skeletal M7C3 and vermicular NbC were transformed into blocky M23C6 and G phase (Ni16Nb6Si7), respectively, accompanied with many dispersed M23C6 secondary carbides. With the extension of service time, M23C6 carbides on the grain boundaries were transformed into M7C3 with high stacking fault structure and coarsened, and the blocky G phase was transformed into granular NbC. The yield strength and ultimate tensile strength of the aging furnace tubes decreased by 9%-18%, yet the elongation after fracture decreased significantly from 10.0%-14.0% to 3.0%-5.0%. The hardness of the carburized zone of the carburized tubes increased by 10%-17%, and the rupture time decreased by 45%-75% under the test condition of 1100℃ and 16MPa. Finally, the evolution map was summarized.