Dry reforming of glycerol over Rh-based ceria and zirconia catalysts: New insights on catalyst activity and stability

Bulutoglu, Pelin Su; Say, Zafer; Bac, Selin; Özensoy, Emrah; Avcı, Ahmet K.

doi:10.1016/j.apcata.2018.07.027

Cited by 49 publications

(42 citation statements)

References 76 publications

(116 reference statements)

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“…Methane dry reforming is a thermo-catalytic process used for producing synthetic gas (syngas), a mixture of hydrogen (H 2 ) and carbon monoxide (CO), by utilizing methane (CH 4 ) and carbon dioxide (CO 2 ) as feedstocks [1]. Although there are several processes such as steam methane reforming [2], coal gasification [3], glycerol reforming [4], and partial oxidation reforming [5] that can be employed for syngas production, none of these processes have the advantages of mitigating greenhouse gas emission through the consumption of CH 4 and CO 2 [6]. Besides being a potential technical route for greenhouse gas emission reduction, methane dry reforming has the advantage of producing syngas with a H 2 :CO ratio close to unity [7].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Modelling Approach for the Prediction of CO-Rich Hydrogen Production Rate from Methane Dry Reforming

et al. 2019

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This study investigates the applicability of the Leven-Marquardt algorithm, Bayesian regularization, and a scaled conjugate gradient algorithm as training algorithms for an artificial neural network (ANN) predictively modeling the rate of CO and H 2 production by methane dry reforming over a Co/Pr 2 O 3 catalyst. The dataset employed for the ANN modeling was obtained using a central composite experimental design. The input parameters consisted of CH 4 partial pressure, CO 2 partial pressure, and reaction temperature, while the target parameters included the rate of CO and H 2 production. A neural network architecture of 3 13 2, 3 15 2, and 3 15 2 representing the input layer, hidden neuron layer, and target (output) layer were employed for the Leven-Marquardt, Bayesian regularization, and scaled conjugate gradient training algorithms, respectively. The ANN training with each of the algorithms resulted in an accurate prediction of the rate of CO and H 2 production. The best prediction was, however, obtained using the Bayesian regularization algorithm with the lowest standard error of estimates (SEE). The high values of coefficient of determination (R 2 > 0.9) obtained from the parity plots are an indication that the predicted rates of CO and H 2 production were strongly correlated with the observed values.To overcome these challenges, several supported metal-based catalysts have been developed and tested. An extensive review by Abdullah et al. [10] revealed that supported nickel (Ni) catalysts have been mostly investigated for methane dry reforming due to its high catalytic performance. Nevertheless, the Ni-based catalysts are very prone to sintering and carbon deposition [11]. On the other hand, cobalt (Co)-based catalysts which have a comparative activity to Ni have been reported to show superior stability compare to Ni under the same process condition [12,13]. In our previous studies, the use of rare-earth metal oxide-supported Co catalysts for CO-rich hydrogen production showed considerable activity and stability [14][15][16]. However, one major challenge is understanding the kinetics of the methane dry reforming in terms of the rate of H 2 and CO production due to variations in the chemical composition of the various catalysts [17]. This challenge can be overcome by employing an artificial intelligence modeling approach for a better understanding of the process parameters [18,19]. Processes with non-linear and complex relationships between the input and the output parameters are often encountered in real life processes. The better understanding of the non-linear relationship between the input and the output parameters of the process can further be utilized to optimize the process operation and create the basis for the theoretical framework, process automation, and upscaling [20].An artificial intelligence modeling approach using an artificial neural network (ANN) has been widely employed for different catalytic processes, such as hydrodesulfurization [20], methanol steam reforming, glycerol steam reforming [2...

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Modelling Approach for the Prediction of CO-Rich Hydrogen Production Rate from Methane Dry Reforming

et al. 2019

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“…Comparing the other two catalysts, it seems that the Ir/CeAl is slightly more selective to H 2 than the Pd/CeAl (for the Ir/CeAl, the S H2 ranges from ≈37 to 63% and the Y H2 from 0.8 to 4.6, from 400 to 750 • C, respectively). These results may indicate that the methanation reaction (at lower temperatures) and the RWGS reaction (at higher temperatures), both leading to the consumption of hydrogen, are more dominant on the Ir/CeAl and Pd/CeAl catalysts [93]. Moreover, the much improved performance of the Pt/CeAl may be related to: (i) the increased number of Brønsted acid sites (as shown by the TPD experiments), which improved the hydrogenolysis and dehydrogenation-dehydration of condensable intermediates, leading to enhanced H 2 production, and (ii) the smaller metal particle size (electron microscopy analysis), which favours the cleavage of the C-C and C-O bonds in the C 3 H 8 O 3 molecules.…”

Section: Catalytic Activity and Selectivitymentioning

confidence: 95%

The Effect of Noble Metal (M: Ir, Pt, Pd) on M/Ce2O3-γ-Al2O3 Catalysts for Hydrogen Production via the Steam Reforming of Glycerol

et al. 2020

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A promising route for the energetic valorisation of the main by-product of the biodiesel industry is the steam reforming of glycerol, as it can theoretically produce seven moles of H2 for every mole of C3H8O3. In the work presented herein, CeO2–Al2O3 was used as supporting material for Ir, Pd and Pt catalysts, which were prepared using the incipient wetness impregnation technique and characterized by employing N2 adsorption–desorption, X-Ray Diffraction (XRD), Temperature Programmed Reduction (TPR), Temperature Programmed Desorption (TPD), X-ray Photoelectron Spectroscopy (XPS) and Transmission Electron Microscopy (TEM). The catalytic experiments aimed at identifying the effect of temperature on the total conversion of glycerol, on the conversion of glycerol to gaseous products, the selectivity towards the gaseous products (H2, CO2, CO, CH4) and the determination of the H2/CO and CO/CO2 molar ratios. The main liquid effluents produced during the reaction were quantified. The results revealed that the Pt/CeAl catalyst was more selective towards H2, which can be related to its increased number of Brønsted acid sites, which improved the hydrogenolysis and dehydrogenation–dehydration of condensable intermediates. The time-on-stream experiments, undertaken at low Water Glycerol Feed Ratios (WGFR), showed gradual deactivation for all catalysts. This is likely due to the dehydration reaction, which leads to the formation of unsaturated hydrocarbon species and eventually to carbon deposition. The weak metal–support interaction shown for the Ir/CeAl catalyst also led to pronounced sintering of the metallic particles.

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“…For dry reforming, different catalysts can be used, highlighting Ni, Rh, Ru, Ir, Pd, or Pt [20,[100][101][102][103][104], with coke accumulation and the sintering of catalyst particles (especially for Rh) as the main disadvantages, as in other cases [20]. In order to avoid coke accumulation, La was added to these catalysts to promote active sites, due to its redox property to remove carbon and its lower acidity compared to other popular catalysts such as Ni [100].…”

Section: Dry Reformingmentioning

confidence: 99%

“…Additionally, the use of supports such as MgO and CaO can increase the carbon resistance within the catalyst, reducing carbon deposition [105]. On the other hand, ZrO 2 and CeO 2 represent promising materials as supports for the dry reforming of glycerol, due to their oxygen transfer properties, helping in the dissociation of CO 2 into CO and O [104].…”

Section: Dry Reformingmentioning

confidence: 99%

Recent Advances in Glycerol Catalytic Valorization: A Review

et al. 2020

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Once a biorefinery is ready to operate, the main processed materials need to be completely evaluated in terms of many different factors, including disposal regulations, technological limitations of installation, the market, and other societal considerations. In biorefinery, glycerol is the main by-product, representing around 10% of biodiesel production. In the last few decades, the large-scale production of biodiesel and glycerol has promoted research on a wide range of strategies in an attempt to valorize this by-product, with its transformation into added value chemicals being the strategy that exhibits the most promising route. Among them, C3 compounds obtained from routes such as hydrogenation, oxidation, esterification, etc. represent an alternative to petroleum-based routes for chemicals such as acrolein, propanediols, or carboxylic acids of interest for the polymer industry. Another widely studied and developed strategy includes processes such as reforming or pyrolysis for energy, clean fuels, and materials such as activated carbon. This review covers recent advances in catalysts used in the most promising strategies considering both chemicals and energy or fuel obtention. Due to the large variety in biorefinery industries, several potential emergent valorization routes are briefly summarized.

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Dry reforming of glycerol over Rh-based ceria and zirconia catalysts: New insights on catalyst activity and stability

Cited by 49 publications

References 76 publications

Artificial Intelligence Modelling Approach for the Prediction of CO-Rich Hydrogen Production Rate from Methane Dry Reforming

Artificial Intelligence Modelling Approach for the Prediction of CO-Rich Hydrogen Production Rate from Methane Dry Reforming

The Effect of Noble Metal (M: Ir, Pt, Pd) on M/Ce2O3-γ-Al2O3 Catalysts for Hydrogen Production via the Steam Reforming of Glycerol

Recent Advances in Glycerol Catalytic Valorization: A Review

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