Deep Catalytic Oxidative Desulfurization of Model Fuel Based on Modified Iron Porphyrins in Ionic Liquids: Anionic Ligand Effect

Zhao, Rijie; Wang, Jianlong; Zhang, Dongdong; Sun, Yahui; Han, Bin; Tang, Nan; Zhao, Jianghong; Li, Kaixi

doi:10.1021/acssuschemeng.6b02916

Cited by 57 publications

(25 citation statements)

References 58 publications

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“…Compared to other methods, ODS has attracted much interest, which is a promising and efficient method for sulfur removal due to its ambient operating conditions . During the ODS process, organic sulfur compounds are oxidized into its corresponding sulfones or sulfoxides, and then the oxidative products can be removed from the oil phase by extraction, adsorption or precipitation with ionic liquids, acetonitrile, activated carbon et al in the following process …”

Section: Introductionmentioning

confidence: 99%

Ultra‐Deep Oxidative Desulfurization of Model Oil Catalyzed by In Situ Carbon‐Supported Vanadium Oxides Using Cumene Hydroperoxide as Oxidant

et al. 2020

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A series of novel dispersed V2O5@Carbon composites, in which vanadium existed not only on the surface of carbon, but also in the matrix, were synthesized in situ from ammonium metavanadate (NH4VO3) and ethylenediaminetetraacetic acid disodium salt dihydrate (EDTA‐2Na). The structures and properties of prepared catalysts were characterized by diverse techniques. An efficient process for catalytic oxidative desulfurization with V2O5@Carbon and cumene hydroperoxide (CHP) was investigated. V2O5@Carbons is superior to the physically supported catalysts in reusability and stability. Results show that 99.8 % sulfur in fuel can be removed and the catalyst can be recycled 7 times with no significant decrease in the desulfurization efficiency. A possible reaction mechanism is proposed, in which V(V)=O was oxidized to peroxo compounds (V(O)2) by CHP along with forming the 2‐phenyl‐2‐propanol. The V(O)2 further cleaved to form electrophilic reactive VIV‐O‐O. species which could oxidize sulfur compounds to corresponding sulfones.

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Section: Introductionmentioning

confidence: 99%

Ultra‐Deep Oxidative Desulfurization of Model Oil Catalyzed by In Situ Carbon‐Supported Vanadium Oxides Using Cumene Hydroperoxide as Oxidant

et al. 2020

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“…10 The core of oxidative desulfurization is to select highly selective catalysts. Commonly used catalysts such as heteropolyacids, 11,12 ionic liquids, [13][14][15] metal oxides, 16,17 molecular sieves 18,19 and other inorganic materials 20,21 have been used to eliminate aromatic sulfur compounds in diesel. Molecular sieves stand out from many catalysts due to their better shape-selective catalytic performance and easy separation.…”

Section: Introductionmentioning

confidence: 99%

Synthesis of hierarchical CoAPO‐5 catalysts in different fluoride medium and optimization of its catalytic oxidative desulfurization process

Zhao

Shao

et al. 2022

J of Chemical Tech & Biotech

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BACKGROUND Fluoride ions can form corresponding complexes with transition metal elements (Co, Ti, V, Cr, Mn, Fe, Ni, Cu, etc.), so it is easier to synthesize heteroatomic molecular sieves in a fluorine‐containing system than in a fluorine‐free system. RESULTS In this paper, a hierarchical CoAPO‐5 molecular sieve was synthesized in a fluorine system, and the effects of different fluorine resources on its synthesis were investigated. The CoAPO‐5 samples were characterized by X‐ray diffraction, scanning electron microscopy, N2 adsorption–desorption and temperature‐programmed desorption of NH3. CONCLUSION The results showed that, compared with other fluorine resources, the sample D‐AFI‐1 synthesized with HF possessed more acid sites, which appeared to be more attractive for oxidative desulfurization. The central composite design of response surface methodology was applied to design experimental schemes employing D‐AFI‐1 as catalyst. The volume ratio of oxidant/fuel, extractant/fuel and the mass ratio catalyst/fuel were screened out as the significant factors after the investigation of single factors. The quadratic model of the three significant factors and desulfurization rate was established. On the basis of optimizing the reaction conditions, oxidant/fuel volumetric ratio, catalyst/fuel mass ratio and extractant/fuel were 0.07 mL mL−1, 0.01 g g−1 and 1.65 mL mL−1, respectively, the desulfurization rate was 85.0%, which was consistent with the predicted value of the model (83.7%). © 2022 Society of Chemical Industry (SCI).

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“…Therefore, there is an urgent need of development of intelligent strategies for rational design and/or identification of the promising solvent compounds for organosulfur capture. [9][10] Rapid development of machine learning (ML) based on big data significantly improve the efficiency of multiple fields. [11][12][13] For chemical sciences, successful prediction of reliable molecular properties, such as solubility and thermodynamic parameters, would largely accelerate the molecule screening, improve compound-hitting efficiency, and optimize reaction pathways.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Molecular Identification for High Performance Organosulfide Capture Using Active Machine Learning Algorithm

Chen

Chuanlei

et al. 2021

Preprint

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Machine learning and computer-aided approaches significantly accelerate molecular design and discovery in scientific and industrial fields increasingly relying on data science for efficiency. The typical method used is supervised learning which needs huge datasets. Semi-supervised machine learning approaches are effective to train unlabeled data with improved modeling performance, whereas they are limited by the accumulation of prediction errors. Here, to screen solvents for removal of methyl mercaptan, a type of organosulfur impurities in natural gas, we constructed a computational framework by integrating molecular similarity search and active learning methods, namely, molecular active selection machine learning (MASML). This new model framework identifies the optimal molecules set by molecular similarity search and iterative addition to the training dataset. Among all 126,068 compounds in the initial dataset, 3 molecules were identified to be promising for methyl mercaptan (MeSH) capture, including benzylamine (BZA), p-methoxybenzylamine (PZM), and N,N-diethyltrimethylenediamine (DEAPA). Further experiments confirmed the effectiveness of our modeling framework in efficient molecular design and identification for capturing methyl mercaptan, in which DEAPA presents a Henry's law constant 89.4% lower than that of methyl diethanolamine (MDEA).

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Deep Catalytic Oxidative Desulfurization of Model Fuel Based on Modified Iron Porphyrins in Ionic Liquids: Anionic Ligand Effect

Cited by 57 publications

References 58 publications

Ultra‐Deep Oxidative Desulfurization of Model Oil Catalyzed by In Situ Carbon‐Supported Vanadium Oxides Using Cumene Hydroperoxide as Oxidant

Ultra‐Deep Oxidative Desulfurization of Model Oil Catalyzed by In Situ Carbon‐Supported Vanadium Oxides Using Cumene Hydroperoxide as Oxidant

Synthesis of hierarchical CoAPO‐5 catalysts in different fluoride medium and optimization of its catalytic oxidative desulfurization process

Intelligent Molecular Identification for High Performance Organosulfide Capture Using Active Machine Learning Algorithm

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