Biphasic interface engineering: A machine learning-guided strategy for optimizing selective oxidative desulfurization of FCC slurry oil

doi:10.1016/S1872-2067(26)64998-4

Abstract

Abstract:

The non-destructive desulfurization of aromatic structures is crucial for the high-value utilization of FCC slurry oil. Hydrodesulfurization causes aromatic saturation, impairing the suitability of slurry oil as needle coke feedstock. Therefore, developing methods capable of selective desulfurization while preserving aromatics is essential. Herein, we address the critical challenges impeding the application of oxidative desulfurization (ODS) to slurry oil, specifically its complex composition, high sulfur content, prohibitively high viscosity, and inefficient oil-water interfacial mass transfer. An innovative ODS strategy based on biphasic interface regulation was proposed. By constructing a catalytic system through the combination of polyoxometalate and organic cationic modifiers to stabilize the oil-water interface, enhanced mass transfer efficiency was achieved. These catalysts function as surfactant-like homogeneous catalysts during H₂O₂ mediated oxidation, while enabling rapid separation after reaction. Systematic model system studies identified catalysts with exceptional sulfur-oxidation selectivity, operating via dynamic peroxo-species formation from terminal oxygen of W=O activation by superoxide radicals. Deployment in real slurry oil under Bayesian-optimized conditions reduced sulfur content from 1.60 wt% to 0.34 wt% while completely preserving the core feedstock components 3-4 ring aromatic components and maintaining 86.4% slurry recovery. This research provides a technologically innovative and practically viable pathway for desulfurization of slurry oils with remaining high aromatic contents.

Key words: FCC slurry oil, Oxidative desulfurization, Interface catalysis, Needle coke, Machine learning

Xiaoxiao Xing, Peiwen Wu, Yiru Zou, Zhaozeng Gao, Zhendong Yu, Minmeng Tang, Yanhong Chao, Wenshuai Zhu, Zhichang Liu, Chunming Xu. Biphasic interface engineering: A machine learning-guided strategy for optimizing selective oxidative desulfurization of FCC slurry oil[J]. Chinese Journal of Catalysis, 2026, 84: 375-389.

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URL: https://www.cjcatal.com/EN/10.1016/S1872-2067(26)64998-4

https://www.cjcatal.com/EN/Y2026/V84/I5/375

Figures/Tables 19

Scheme 1. Comparison of HDS and ODS in desulfurization of slurry oils.

Scheme 2. The structures of as-prepared catalysts in this work.

Fig. 1. Morphology characterizations of DDA-VW12. SEM image (a), HR-TEM images (b,c), EDS spectra (d) and EDS mapping (e?i).

Fig. 2. XPS spectra of DDA-VW12. Survey of the catalyst (a), C 1s (b), N 1s (c), O 1s (d), V 2p (e), and W 4f (f).

Fig. 3. Rheological analysis of slurry oil. (a) Curve of viscosity and temperature of slurry oil. (b) Arrhenius viscosity plot of slurry oil. (c) Curve of viscosity and temperature of the slurry oil after viscosity reduction. (d) Comparison of slurry oil viscosity before and after viscosity reduction.

Fig. 4. The hydrocarbon (a) and [S1+Ag+] distribution characterized (b) of slurry oil by Ag+ complexation +ESI Orbitrap MS.

Fig. 5. (a) The performance of the single-component control group. (b) Hot-filtration experiment. (c) CA of different catalysts with water. Reaction conditions: mcat. = 20 mg, O/S = 4, T = 60 °C, t = 2 h.

Fig. 6. Aromatics oxidation analysis. (a) UV-vis characteristic peaks of BNT and pyrene. (b) UV-vis spectra of oil after oxidation. (c) GC signals of pyrene after oxidation. Aromatics removal (d) and color (e) of oil samples after oxidation.

Fig. 7. Influence of O/S (a), catalyst dosage (b), reaction temperature (c) on the removal of BNT. (d) Pseudo-first-order kinetics for oxidation of BNT.

Fig. 8. The predictive performance of the random forest model (a) and importance of each feature to the response value (b).

Fig. 9. (a) Effect of sulfur substrate on catalytic performance. (b) Pseudo-first-order kinetics for oxidation of different substrates. Reaction conditions: mcat. = 20 mg, O/S = 3, T = 60 °C.

Table 1 he initial conditions of Bayesian optimization.

	Temperature (°C)	Time (min)	Dosage (mg)	O/S	R
1	30	60	20	3	150
2	60	60	20	3	158

Fig. 10. Bayesian optimization of ODS process in real FCC slurry oil.

Fig. 11. GC-SCD chromatograms of the slurry oil before and after oxidation.

Table 2 Composition analysis of slurry oil.

Aromatic type	Pre-oxidation slurry oil (wt%)	Refined slurry oil (wt%)
Dicyclic aromatic hydrocarbons	10.1	9.2
Tricyclic aromatic hydrocarbons	10.5	12.4
Tetracyclic aromatic hydrocarbons	29.0	31.6
Pentacyclic aromatic hydrocarbons	5.8	5.3
Total aromatic (2-5 rings)	55.4	58.5

Fig. 12. Recyclability tests for DDA-VW12.

Fig. 13. (a,b) Optical images of desulfurization products. FT-IR (c) and GC-MS (d) spectra of the oxidation product.

Fig. 14. Determination of radicals during the ODS process. Radical quenching experiment (a) and EPR spectra (b).

Fig. 15. ODS mechanism of slurry oil.

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