Chemical reactions of oily sludge catalyzed by iron oxide under supercritical water gasification condition

Houjun Zhang, Fang Chen, Jipeng Xu, Jinli Zhang, You Han

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PDF(4029 KB)
Front. Chem. Sci. Eng. ›› 2022, Vol. 16 ›› Issue (6) : 886-896. DOI: 10.1007/s11705-021-2125-z
RESEARCH ARTICLE
RESEARCH ARTICLE

Chemical reactions of oily sludge catalyzed by iron oxide under supercritical water gasification condition

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Abstract

Supercritical water gasification is a promising technology in dealing with the degradation of hazardous waste, such as oily sludge, accompanied by the production of fuel gases. To evaluate the mechanism of Fe2O3 catalyst and the migration pathways of heteroatoms and to investigate the systems during the process, reactive force field molecular dynamics simulations are adopted. In terms of the catalytic mechanisms of Fe2O3, the surface lattice oxygen is consumed by small carbon fragments to produce CO and CO2, improving the catalytic performance of the cluster due to more unsaturated coordination Fe sites exposed. Lattice oxygen combines with •H radicals to form water molecules, improving the catalytic performance. Furthermore, the pathway of asphaltene degradation was revealed at an atomic level, as well as products. Moreover, the adsorption of hydroxyl radical on the S atom caused breakage of the two C–S bonds in turn, forming •HSO intermediate, so that the organic S element was fixed into the inorganic liquid phase. The heteroatom O was removed under the effects of supercritical water. Heavy metal particles presented in the oily sludge, such as iron in association with Fe2O3 catalyst, helped accelerate the degradation of asphaltenes.

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Keywords

oily sludge / SCWG / ReaxFF / Fe2O3 / heteroatoms

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Houjun Zhang, Fang Chen, Jipeng Xu, Jinli Zhang, You Han. Chemical reactions of oily sludge catalyzed by iron oxide under supercritical water gasification condition. Front. Chem. Sci. Eng., 2022, 16(6): 886‒896 https://doi.org/10.1007/s11705-021-2125-z

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant Nos. 21978210 and U20A20151), Tianjin Natural Science Foundation, China (Grant No. 19JCYBJC20000) and the National Key R&D Program of China (Grant No. 2018YFA0702403). The Gaussian 09 software was supported by the National Supercomputing Center in Shenzhen.

Electronic Supplementary Material

Supplementary material is available in the online version of this article at https://dx.doi.org/10.1007/s11705-021-2125-z and is accessible for authorized users.

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