Syngas Production from Catalytic CO2 Reforming of CH4 over CaFe2O4 Supported Ni and Co Catalysts: Full Factorial Design Screening

Hossain, Mohammed Anwar; Ayodele, Bamidele Victor; Cheng, Chin Kui; Khan, Maksudur R.

doi:10.9767/bcrec.13.1.1197.57-73

Cited by 11 publications

(3 citation statements)

References 25 publications

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“…Figure 2 demonstrates the other bands in the spectra which showed the presence of acetylacetonate. The band at 831-873 cm −1 corresponded to the C−O bond, while the peak at 480, 685, and 775 cm −1 corresponded to NiO, CoO, and PdO, respectively [23,24], as per the reported data. A band at around 3635-3637 cm −1 was attributed to the stretching frequency of moisture [25].…”

Section: Ft-ir Methodssupporting

confidence: 76%

Catalytic Dry-reforming of Methane Process with Co,Ni,Pd/Ca-La-O Mixed Oxides

Al-Doghachi,

Al-Najar,

Safa-Gamal

et al. 2023

Bull. Chem. React. Eng. Catal.

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A surfactant-assisted co-precipitation method was used to prepare the catalysts Co,Ni,Pd/CaO, Co,Ni,Pd/Ca0.97La3+0.03O, Co,Ni,Pd/Ca0.93La3+0.07O, and Co,Ni,Pd/Ca0.85La3+0.15O (1% each of Co, Ni, and Pd). La2O3 doping effect on the activity and stability of Co,Ni,Pd/CaO catalysts was investigated in dry reforming of methane. Catalysts were characterized by several techniques (X-ray diffraction (XRD), Brunauer–Emmett–Teller (BET), X-ray Fluorescence (XRF), Fourier Transform Infra Red (FTIR), Temperature Programmed Desorption H2 (H2-TPR), Transmission electron microscopes (TEM), and Temperature Gravimetric Analysis (TGA)) and were tested in a fixed-bed reactor at 900 °C and (Gas Hourly Specific Velocity (GHSV) = 15000 mL.gcat−1.h−1, atmospheric pressure). Adding La2O3 had little effect on the morphology of the Co,Ni,Pd/CaO catalyst. However, it played a crucial role in enhancing the catalyst’s reducibility and CO2 adsorption at high temperatures, as indicated by the activity and stability of the Co,Ni,Pd/CaO catalyst. The carbon deposition on utilized catalysts after 5 hours at 900 °C was examined using TEM and thermal gravimetric analysis (TGA) techniques. Compared to Co,Ni,Pd/CaO catalysts across the entire temperature range, the tri-metallic Co,Ni,Pd/Ca0.85La3+0.15O catalyst with a lanthanum promoter demonstrated a greater conversion of CH4 (84%) and CO2 (92 %) at a 1:1 CH4:CO2 ratio. The selectivity of H2/CO reduced in the following order: Co,Ni,Pd/Ca0.85La3+0.15O > Co,Ni,Pd/Ca0.93La3+0.07O > Co,Ni,Pd/Ca0.97La3+0.03O > Co,Ni,Pd/CaO. Copyright © 2023 by Authors, Published by BCREC Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).

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Section: Ft-ir Methodssupporting

confidence: 76%

Catalytic Dry-reforming of Methane Process with Co,Ni,Pd/Ca-La-O Mixed Oxides

Al-Doghachi,

Al-Najar,

Safa-Gamal

et al. 2023

Bull. Chem. React. Eng. Catal.

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“…The CeO 2 promoted Ni/CaFe 2 O 4 catalyst was prepared by co‐impregnation of Ce(NO 3 ) 3 ·6H 2 O and (Ni(NO 3 ) 2 ·6H 2 O) precursors equivalent to 3% and 15% of CeO 2 and Ni respectively in a solution of CaFe 2 O 4 . The use of 15% Ni‐loading for the synthesis of the catalyst was established in our initial screening (5%‐15% Ni‐loading) in which the highest catalyst activity was obtained at 15% Ni‐loading . The solution was thoroughly mixed using a magnetic stirrer and thereafter dried overnight in an oven at 297 K. The dried sample was calcined in a muffle furnace at 1073 K and consequently crushed.…”

Section: Methodsmentioning

confidence: 99%

“…The use of 15% Ni-loading for the synthesis of the catalyst was established in our initial screening (5%-15% Ni-loading) in which the highest catalyst activity was obtained at 15% Ni-loading. 24 The solution was thoroughly mixed using a magnetic stirrer and thereafter dried overnight in an oven at 297 K. The dried sample was calcined in a muffle furnace at 1073 K and consequently crushed.…”

Section: Introductionmentioning

confidence: 99%

Thermo‐catalytic conversion of greenhouse gases (CO ₂ and CH ₄ ) to CO‐rich hydrogen by CeO ₂ modified calcium iron oxide supported nickel catalyst

Hossain

Ayodele

Ong³

et al. 2020

Int J Energy Res

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SUMMARY In this study, the thermo‐catalytic conversion of two principal greenhouse gases (methane and carbon dioxide) to carbon monoxide (CO)‐rich hydrogen (H2) is investigated over cerium oxide (CeO2) promoted calcium ferrite supported nickel (Ni/CaFe2O4) catalyst. The CeO2 promoted Ni/CaFe2O4 catalyst was prepared using wet‐impregnation technique. To ascertain the physicochemical properties, the as‐prepared catalyst was characterized using various instrument techniques. The characterization of the catalysts reveals that CeO2‐Ni/CaFe2O4 possesses suitable physicochemical properties for the conversion of methane (CH4) and carbon dioxide (CO2) to CO‐rich H2. The thermo‐catalytic reaction revealed that the CeO2 promoted Ni/CaFe2O4 catalyst displayed a higher CH4 and CO2 conversions of 90.04% and 91.2%, respectively, at a temperature of 1073 K compared to the unpromoted catalyst. The highest H2 and CO yields of 78% and 76%, respectively, were obtained over the CeO2‐Ni/CaFe2O4 at 1073 K and CH4/CO2 ratio of 1. The CeO2 promoted Ni/CaFe2O4 catalyst remained stable throughout the 30 hours time on stream (TOS) while that of the unpromoted Ni/CaFe2O4 catalyst sharply decreased after 22 hours TOS. The characterization of the used catalysts confirms the evidence of carbon depositions on the unpromoted Ni/CaFe2O4 which is solely responsible for its deactivation. Whereas, there was a slightly gasifiable carbon deposited on the CeO2 promoted Ni/CaFe2O4 catalyst which could be ascribed to the interaction effect of the CeO2 promoter on the Ni/CaFe2O4 catalyst.

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Modeling the effect of non‐linear process parameters on the prediction of hydrogen production by steam reforming of bio‐oil and glycerol using artificial neural network

2020

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Biomass-derived substrates such as bio-oil and glycerol are gaining wide acceptability as feedstocks to produce hydrogen using a steam reforming process. The wide acceptability can be attributed to a huge amount of glycerol and bio-oil obtained as by-products of biodiesel production and pyrolysis processes. Several parameters have been reported to affect the production of hydrogen by biomass steam reforming. This study investigates the effect of non-linear process parameters on the prediction of hydrogen production by biomass (bio-oil and glycerol) steam reforming using artificial neural network (ANN) modeling technique. Twenty different multilayer ANN model architectures were tested using datasets obtained from the bio-oil and glycerol steam reforming. Two algorithms namely Levenberg-Marquardt and Bayesian regularization were employed for the training of the ANNs. An optimized network configuration consisting of 3 input layer 14 hidden neurons, 1 output layer, and 3 input layer, 5 hidden neurons, and 1 output layer were obtained for the Levenberg-Marquardt and Bayesian regularization trained network, respectively for hydrogen production by bio-oil steam reforming. While an optimized network configuration consisting of 5 input nodes, 9 hidden neurons, 1 output node, and 5 input nodes, 8 hidden neurons, and 1 output node were obtained for Levenberg-Marquardt and Bayesian regularization trained network, respectively for hydrogen production by glycerol steam reforming. Based on the optimized network, the predicted hydrogen production from the bio-oil and glycerol steam agreed with the actual values with the coefficient of determination (R 2) > 0.9. A low mean square error of 3.024 × 10 −24 and 6.22 × 10 −15 for the optimized for Levenberg-Marquardt and Bayesian regularization-trained ANN, respectively. The neural network analyses of the two processes showed that reaction temperature and glycerol-to-water molar ratio were the most relevant factors that influenced the production of hydrogen by bio-oil and glycerol steam reforming, respectively. This study has demonstrated the robustness of the ANN as a technique for investigating the effect of non-linear process parameters on hydrogen production by bio-oil and glycerol steam reforming.

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Syngas Production from Catalytic CO2 Reforming of CH4 over CaFe2O4 Supported Ni and Co Catalysts: Full Factorial Design Screening

Cited by 11 publications

References 25 publications

Catalytic Dry-reforming of Methane Process with Co,Ni,Pd/Ca-La-O Mixed Oxides

Catalytic Dry-reforming of Methane Process with Co,Ni,Pd/Ca-La-O Mixed Oxides

Thermo‐catalytic conversion of greenhouse gases (CO ₂ and CH ₄ ) to CO‐rich hydrogen by CeO ₂ modified calcium iron oxide supported nickel catalyst

Modeling the effect of non‐linear process parameters on the prediction of hydrogen production by steam reforming of bio‐oil and glycerol using artificial neural network

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Syngas Production from Catalytic CO2 Reforming of CH4 over CaFe2O4 Supported Ni and Co Catalysts: Full Factorial Design Screening

Cited by 11 publications

References 25 publications

Catalytic Dry-reforming of Methane Process with Co,Ni,Pd/Ca-La-O Mixed Oxides

Catalytic Dry-reforming of Methane Process with Co,Ni,Pd/Ca-La-O Mixed Oxides

Thermo‐catalytic conversion of greenhouse gases (CO 2 and CH 4 ) to CO‐rich hydrogen by CeO 2 modified calcium iron oxide supported nickel catalyst

Modeling the effect of non‐linear process parameters on the prediction of hydrogen production by steam reforming of bio‐oil and glycerol using artificial neural network

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Thermo‐catalytic conversion of greenhouse gases (CO ₂ and CH ₄ ) to CO‐rich hydrogen by CeO ₂ modified calcium iron oxide supported nickel catalyst