Statistical Modeling of Hydrogen Production Via Carbonaceous Catalytic Methane Decomposition

2018 ◽  
Vol 140 (7) ◽  
Author(s):  
Vidyasagar Shilapuram ◽  
Bishwadeep Bagchi ◽  
Nesrin Ozalp ◽  
Richard Davis
Keyword(s):  
Hydrogen Production ◽  
Statistical Modeling ◽  
Catalyst Deactivation ◽  
Catalyst Activity ◽  
Decomposition Reaction ◽  
Initial Reaction Rate ◽  
Methane Decomposition ◽  
Quadratic Model ◽  
Initial Reaction ◽  
Statistical Regression

Hydrogen production via carbonaceous catalytic methane decomposition is a complex process with simultaneous reaction, catalyst deactivation, and carbon agglomeration. Conventional reaction and deactivation models do not predict the progress of reaction accurately. Thus, statistical modeling using the method of design of experiments (DoEs) was used to design, model, and analyze experiments of methane decomposition to determine the important factors that affect the rates of reaction and deactivation. A variety of statistical models were tested in order to identify the best one agreeing with the experimental data by analysis of variance (ANOVA). Statistical regression models for initial reaction rate, catalyst activity, deactivation rate, and carbon weight gain were developed. The results showed that a quadratic model predicted the experimental findings. The main factors affecting the dynamics of the methane decomposition reaction and the catalyst deactivation rates for this process are partial pressure of methane, reaction temperature, catalytic activity, and residence time.

Hydrogen production by methane decomposition over Ni–Cu–SiO2 catalysts: effect of temperature on catalyst deactivation

RSC Advances ◽  
2016 ◽  
Vol 6 (57) ◽  
pp. 52154-52163 ◽  
Author(s):  
Jiamao Li ◽  
Chao Xiao ◽  
Liangping Xiong ◽  
Xiaojun Chen ◽  
Linjie Zhao ◽  
...  
Keyword(s):  
Hydrogen Production ◽  
Catalyst Deactivation ◽  
Methane Decomposition ◽  
Effect Of Temperature

The activities of 65% Ni–10% Cu–SiO2 catalysts under different reaction temperatures using route I and route II.

scholarly journals Revealing the Effect of Nickel Particle Size on Carbon Formation Type in the Methane Decomposition Reaction

Catalysts ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 890
Author(s):  
Wei Liang ◽  
Hao Yan ◽  
Chen Chen ◽  
Dong Lin ◽  
Kexin Tan ◽  
...  
Keyword(s):  
Particle Size ◽  
Rational Design ◽  
Catalyst Deactivation ◽  
Catalyst Activity ◽  
Catalytic Decomposition ◽  
Nickel Particle ◽  
Decomposition Reaction ◽  
Precipitation Method ◽  
Carbon Species ◽  
Nickel Particles

Carbon species deposition is recognized as the primary cause of catalyst deactivation for hydrocarbon cracking and reforming reactions. Exploring the formation mechanism and influencing factors for carbon deposits is crucial for the design of rational catalysts. In this work, a series of NixMgyAl-800 catalysts with nickel particles of varying mean sizes between 13.2 and 25.4 nm were obtained by co-precipitation method. These catalysts showed different deactivation behaviors in the catalytic decomposition of methane (CDM) reaction and the deactivation rate of catalysts increased with the decrease in nickel particle size. Employing TG-MS and TEM characterizations, we found that carbon nanotubes which could keep catalyst activity were more prone to form on large nickel particles, while encapsulated carbon species that led to deactivation were inclined to deposit on small particles. Supported by DFT calculations, we proposed the insufficient supply of carbon atoms and rapid nucleation of carbon precursors caused by the lesser terrace/step ratio on smaller nickel particles, compared with large particles, inhibit the formation of carbon nanotube, leading to the formation of encapsulated carbon species. The findings in this work may provide guidance for the rational design of nickel-based catalysts for CDM and other methane conversion reactions.

Hydrogen Production by Carbon-Catalyzed Methane Decomposition Via Thermogravimetry

2016 ◽  
Vol 139 (1) ◽  
Author(s):  
Vidyasagar Shilapuram ◽  
Nesrin Ozalp
Keyword(s):  
Weight Gain ◽  
Hydrogen Production ◽  
Reaction Temperature ◽  
Reaction Rate ◽  
Carbon Deposition ◽  
Activated Carbons ◽  
Catalyst Activity ◽  
High Energy ◽  
Methane Decomposition ◽  
Carbon Formation

Hydrogen is a high energy content fuel and methane is currently the most preferred feedstock for hydrogen production. Direct thermal splitting of methane offers the cleanest technique to produce hydrogen and carbon as coproduct fuel. Carbonaceous catalysts have significant impact on methane to hydrogen conversion. This study presents thermogravimetric experiment results of carbon-catalyzed methane decomposition using commercial catalyst. Results are presented in terms of carbon formation rate, amount of carbon deposition on the catalyst, sustainability factor, catalyst activity, and kinetics of the reaction. The results show that weight gain because of carbon formation depends on reaction temperature, methane volume percent in the feed gas, and nature of the carbonaceous catalyst. It was observed that the reaction rate was dominant at the beginning, and deactivation rate was dominant toward the end of reaction. X-ray diffraction (XRD) and scanning electron microscopic (SEM) analysis of deactivated catalytic samples show decreasing disorder with increasing reaction temperature. Finally, performance comparison of activated carbons (ACs) studied in literature shows that activated carbon sample chosen in this study outperforms in terms of carbon deposition, reaction rate, carbon weight gain, and sustainability factor.

scholarly journals Promising Immobilization of Industrial-Class Phospholipase A1 to Attain High-Yield Phospholipids Hydrolysis and Repeated Use with Optimal Water Content in Water-in-Oil Microemulsion Phase

2021 ◽  
Vol 11 (4) ◽  
pp. 1456
Author(s):  
Yusuke Hayakawa ◽  
Ryoichi Nakayama ◽  
Norikazu Namiki ◽  
Masanao Imai
Keyword(s):  
Water Content ◽  
Reaction Rate ◽  
Enzyme Reaction ◽  
Initial Reaction Rate ◽  
Industrial Applications ◽  
Molar Ratio ◽  
High Yield ◽  
Initial Reaction ◽  
Phospholipase A1 ◽  
Repeated Use

In this study, we maximized the reactivity of phospholipids hydrolysis with immobilized industrial-class phospholipase A1 (PLA1) at the desired water content in the water-in-oil (W/O) microemulsion phase. The optimal hydrophobic-hydrophilic condition of the reaction media in a hydrophobic enzyme reaction is critical to realize the maximum yields of enzyme activity of phospholipase A1. It was attributed to enzymes disliking hydrophobic surroundings as a special molecular structure for reactivity. Immobilization of PLA1 was successfully achieved with the aid of a hydrophobic carrier (Accurel MP100) combination with the treatment using glutaraldehyde. The immobilized yield was over 90% based on simple adsorption. The hydrolysis reaction was kinetically investigated through the effect of glutaraldehyde treatment of carrier and water content in the W/O microemulsion phase. The initial reaction rate increased linearly with an increasing glutaraldehyde concentration and then leveled off over a 6% glutaraldehyde concentration. The initial reaction rate, which was predominantly driven by the water content in the organic phase, changed according to a typical bell-shaped curve with respect to the molar ratio of water to phospholipid. It behaved in a similar way with different glutaraldehyde concentrations. After 10 cycles of repeated use, the reactivity was well sustained at 40% of the initial reaction rate and the creation of the final product. Accumulated yield after 10 times repetition was sufficient for industrial applications. Immobilized PLA1 has demonstrated potential as a biocatalyst for the production of phospholipid biochemicals.

Ensemble Learning Approach with LASSO for Predicting Catalytic Reaction Rates

Synlett ◽  
2020 ◽  
Author(s):  
Akira Yada ◽  
Kazuhiko Sato ◽  
Tarojiro Matsumura ◽  
Yasunobu Ando ◽  
Kenji Nagata ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Reaction Rates ◽  
Initial Reaction Rate ◽  
Training Dataset ◽  
Initial Reaction ◽  
Learning Approach ◽  
Learning Framework ◽  
Machine Learning Approach ◽  
Reasonable Prediction ◽  
Epoxidation Of Alkenes

AbstractThe prediction of the initial reaction rate in the tungsten-catalyzed epoxidation of alkenes by using a machine learning approach is demonstrated. The ensemble learning framework used in this study consists of random sampling with replacement from the training dataset, the construction of several predictive models (weak learners), and the combination of their outputs. This approach enables us to obtain a reasonable prediction model that avoids the problem of overfitting, even when analyzing a small dataset.

Sign in / Sign up

Export Citation Format

Share Document