Optimizing parameters affecting synthesis of a novel Co–Mo/GO catalyst in a Naphtha HDS reaction utilizing D-optimal experimental design method

Hajjar, Zeinab; Kazemeini, Mohammad; Rashidi, Alimorad; Soltanali, Saeed

doi:10.1016/j.jtice.2017.06.048

Cited by 14 publications

(8 citation statements)

References 18 publications

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“…Shan et al (2015) have utilized NiW catalysts for selective HDS of FCC naphtha, and it was reported that better dispersion of W enables modification of the active phase which results in higher selective HDS. Also, various graphene-supported catalysts have been used for HDS of naphtha (Hajjar et al 2015(Hajjar et al , 2016(Hajjar et al , 2017a. MoS 2 supported over graphene was used for HDS of naphtha for which a simultaneous chemical exfoliation method was followed for preparing the catalyst by which high conversion was obtained (Hajjar et al 2015).…”

Section: Edited By Xiu-qin Zhumentioning

confidence: 99%

Comparison of performance of Ni–Mo/γ-alumina catalyst in HDS and HDN reactions of main distillate fractions

2019

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A bimetallic nickel-molybdenum catalyst supported on γ-alumina was synthesized by the two-step incipient wetness impregnation technique. The activity of the prepared Ni-Mo/γ-alumina catalyst was evaluated in a down flow fixed-bed microreactor. In this way, hydrodesulfurization (HDS) and hydrodenitrogenation (HDN) reactions of the main distillate fractions of crude oil were assessed. XRD, SEM, TPR, ICP-OES, BET-BJH and nitrogen adsorption/desorption methods were used for characterizing the synthesized Ni-Mo/γ-alumina catalyst. The active metals with Ni/Mo mass ratio of 0.23 and total metal of 13.7 wt% were loaded on the support, similar to the commercial industrial catalyst. The performance tests were conducted at 3.0 MPa (for light naphtha and heavy naphtha) and at 4.5 MPa (for kerosene and gas oil). The results revealed that the total sulfur conversion of the light naphtha, heavy naphtha, kerosene and gas oil fractions was 98.3%, 95%, 91.7% and 90.1% (after 24 h), respectively.

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Section: Edited By Xiu-qin Zhumentioning

confidence: 99%

Comparison of performance of Ni–Mo/γ-alumina catalyst in HDS and HDN reactions of main distillate fractions

2019

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“…Therefore, the amount of sulfur and nitrogen compounds in fuels should be decreased to provide a clean environment. Hydrodesulfurization (HDS) (Gao et al, 2011;González-Cortés et al, 2006;Hajjar et al, 2015Hajjar et al, , 2016aHajjar et al, , 2017aHajjar et al, , 2017bHuirache-Acuña et al, 2012;Mendoza-Nieto et al, 2015;Miller et al, 2000;Shan et al, 2015) and hydrodenitrogenation (HDN) (Sundaramurthy et al, 2006;Zepeda et al, 2016) are the main processes considered for reducing the Total Sulfur (T.S.) and Total Nitrogen (T.N.)…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, activated carbon has also been used as catalyst support of NiMo in HDT of light gas oil (Rambabu et al, 2014). Recently, graphene has been widely used as an efficient catalyst support (Al-Daous, 2015;Hajjar et al, 2016aHajjar et al, , 2016bHajjar et al, , 2017aJulkapli and Bagheri, 2015;Liu et al, 2014;Wang et al, 2015). Liu et al (2014) prepared MoS 2 /graphene nanocomposites for HDS of carbonyl sulfide; Wang et al (2015) supported the NiMo over graphene-modified mesoporous TiO 2 for HDS of dibenzothiophene, which reported desirable results for graphene as an efficient catalyst support.…”

Section: Introductionmentioning

confidence: 99%

Ni-Mo Supported Nanoporous Graphene as a Novel Catalyst for HDS and HDN of Heavy Naphtha

Behnejad

Abdouss

Tavasoli

2019

Braz. J. Chem. Eng.

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Reducing sulfur and nitrogen compounds is essential in producing clean fuels for providing a green environment. In this regard, a novel nanocatalyst has been proposed for Hydrodesulfurization (HDS) and Hydrodenitrogenation (HDN) of heavy naphtha. To this end, Ni-Mo was loaded on nanoporous graphene (Ni-Mo/NG) through the incipient wetness impregnation method. The synthesized nanocatalyst was characterized by FE-SEM, ICP, BET, FTIR, XRD and TPR methods. The catalytic activity of the Ni-Mo/NG catalyst was evaluated at 290 o C and 30 bar. For the synthesized Ni-Mo/NG catalyst, conversions of total sulfur, total nitrogen and R-SH compounds (mercaptans) were obtained as 99.5, 99.4 and 99.3%, respectively, which shows considerable enhancement in comparison to the Ni-Mo/γ-Alumina industrial catalyst.

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“…Different designs, such as the Plackett-Burman design (PBD), central composite design (CCD), Box-Behnken design (BBD), and Taguchi, fall into the category of fractional factorial designs [61]. In optimal designs, the parameters are determined without bias and with minimum variance; hence, they require a lower num ber of sample points [62].…”

Section: Samplingmentioning

confidence: 99%

A Review of Proxy Modeling Highlighting Applications for Reservoir Engineering

2022

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Numerical models can be used for many purposes in oil and gas engineering, such as production optimization and forecasting, uncertainty analysis, history matching, and risk assessment. However, subsurface problems are complex and non-linear, and making reliable decisions in reservoir management requires substantial computational effort. Proxy models have gained much attention in recent years. They are advanced non-linear interpolation tables that can approximate complex models and alleviate computational effort. Proxy models are constructed by running high-fidelity models to gather the necessary data to create the proxy model. Once constructed, they can be a great choice for different tasks such as uncertainty analysis, optimization, forecasting, etc. The application of proxy modeling in oil and gas has had an increasing trend in recent years, and there is no consensus rule on the correct choice of proxy model. As a result, it is crucial to better understand the advantages and disadvantages of various proxy models. The existing work in the literature does not comprehensively cover all proxy model types, and there is a considerable requirement for fulfilling the existing gaps in summarizing the classification techniques with their applications. We propose a novel categorization method covering all proxy model types. This review paper provides a more comprehensive guideline on comparing and developing a proxy model compared to the existing literature. Furthermore, we point out the advantages of smart proxy models (SPM) compared to traditional proxy models (TPM) and suggest how we may further improve SPM accuracy where the literature is limited. This review paper first introduces proxy models and shows how they are classified in the literature. Then, it explains that the current classifications cannot cover all types of proxy models and proposes a novel categorization based on various development strategies. This new categorization includes four groups multi-fidelity models (MFM), reduced-order models (ROM), TPM, and SPM. MFMs are constructed based on simplifying physics assumptions (e.g., coarser discretization), and ROMs are based on dimensional reduction (i.e., neglecting irrelevant parameters). Developing these two models requires an in-depth knowledge of the problem. In contrast, TPMs and novel SPMs require less effort. In other words, they do not solve the complex underlying mathematical equations of the problem; instead, they decouple the mathematical equations into a numeric dataset and train statistical/AI-driven models on the dataset. Nevertheless, SPMs implement feature engineering techniques (i.e., generating new parameters) for its development and can capture the complexities within the reservoir, such as the constraints and characteristics of the grids. The newly introduced parameters can help find the hidden patterns within the parameters, which eventually increase the accuracy of SPMs compared to the TPMs. This review highlights the superiority of SPM over traditional statistical/AI-based proxy models. Finally, the application of various proxy models in the oil and gas industry, especially in subsurface modeling with a set of real examples, is presented. The introduced guideline in this review aids the researchers in obtaining valuable information on the current state of PM problems in the oil and gas industry.

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Optimizing parameters affecting synthesis of a novel Co–Mo/GO catalyst in a Naphtha HDS reaction utilizing D-optimal experimental design method

Cited by 14 publications

References 18 publications

Comparison of performance of Ni–Mo/γ-alumina catalyst in HDS and HDN reactions of main distillate fractions

Comparison of performance of Ni–Mo/γ-alumina catalyst in HDS and HDN reactions of main distillate fractions

Ni-Mo Supported Nanoporous Graphene as a Novel Catalyst for HDS and HDN of Heavy Naphtha

A Review of Proxy Modeling Highlighting Applications for Reservoir Engineering

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