Modeling and optimization of synthesis parameters in nanostructure La 1 − x Ba x Ni 1 − y Cu y O 3 catalysts used in the reforming of methane with CO 2

Abbasi, Mohsen; Niaei, Aligholi; Salari, Dariush; Hosseini, Seyed Abolfazl; Abedini, Fahime; Marmarshahi, S.

doi:10.1016/j.jtice.2017.02.013

Cited by 5 publications

(4 citation statements)

References 22 publications

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“…Therefore, many experts and scholars put forward a variety of methods to improve the algorithm performance. Currently, the commonly used optimization algorithms of BP neural network include BP neural network with additional momentum term combined with adaptive learning rate [38,39], GA-BP neural network combined BP neural network with genetic algorithm [40], PSO-BP neural network combined BP neural network with particle swarm optimization [41], BP learning algorithm improved by Levenberg-Marquardt method [42,43]. Due to the different inherent characteristics of the problems to be simulated, the improvement effect of each optimization method is limited.…”

Section: The Operating Principle Of Bp Neural Networkmentioning

confidence: 99%

“…Abbasi et al [41] applied the artificial neural network (ANN) model linked with genetic algorithm (GA) to optimize of synthesis parameters in nanostructure La 1−x Ba x Ni 1−y Cu y O 3 catalysts used in the reforming of methane with CO 2 . The input layer contains 4 neurons, mole fraction of La, mole fraction of Ni, calcination temperature and reaction temperature and the output layer contains one neuron, conversion of methane as output variable.…”

Section: Application Of Ann In the Optimization Design Of Catalystmentioning

confidence: 99%

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Catalyst Optimization Design Based on Artificial Neural Network

Sun

2022

AJRCoS

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Artificial neural network (ANN) has the characteristics of self-adaptation, self-learning, parallel processing and strong nonlinear mapping ability. Compared with traditional experimental analysis modeling, ANN has obvious advantages in dealing with multivariable nonlinear complex relationships in the process of industrial catalyst design. In the face of the complex structure of catalyst, the unclear reaction mechanism and conditions, the use of neural network for small-scale experimental data analysis can save the time and energy invested in large-scale experimental research and obtain more perfect results in catalyst formulation optimization and condition selection. This paper summarizes the development of artificial neural network. The application principle, construction method and research progress of BP artificial neural network model in catalyst optimization design are summarized and analyzed. The development and innovation of artificial neural network in the future, as well as its continuous application and accumulation, will provide a powerful tool for the research of catalyst design and optimization in the future.

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Section: The Operating Principle Of Bp Neural Networkmentioning

confidence: 99%

Section: Application Of Ann In the Optimization Design Of Catalystmentioning

confidence: 99%

Catalyst Optimization Design Based on Artificial Neural Network

Sun

2022

AJRCoS

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“…The relationship between the independent parameters and the response surface is essential because it gives the real functional relationship. Additionally, the second‐order model is used in RMS …”

Section: Experimental Workmentioning

confidence: 99%

“…Identifying the optimum performance of the plasma process using standard experiments is time‐consuming and costly due to the need for multiple experiments under different test conditions . To reduce the difficulty in determining the optimum performance of the plasma process, previous studies have used the chemical model . It has been found that the chemical model is useful in determining the optimum value for output responses.…”

Section: Introductionmentioning

confidence: 99%

Optimisation of CH₄ and CO₂ conversion and selectivity of H₂ and CO for the dry reforming of methane by a microwave plasma technique using a Box–Behnken design

Alawi

Barifcani²,

Abid³

2018

Asia-Pacific J Chem Eng

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A microwave plasma was generated by N 2 gas. Synthesis gases (H 2 and CO) were produced by the interaction of CH 4 and CO 2 under plasma conditions at atmospheric pressure. The experimental pilot plant was set up, and the gases were sampled and analysed by gas chromatography-mass spectrometry. The Box-Behnken design (BBD) method was used to find the optimising conditions based on the experimental results. The response surface methodology based on a three-parameter and three-level BBD has been developed to find the effects of independent process parameters, which were represented by the gas flow rates of CH 4 , CO 2 , and N 2 and their effects on the process performance in terms of CH 4 , CO 2 , and N 2 conversion and selectivity of H 2 and CO. In this work, four models based on quadratic polynomial regression have been determined to understand the connection between the limits of the feed gas flow rate and the performance of the process.The results show that the most important factor influencing the CO 2 , CH 4 , and N 2 conversion and the selectivity of H 2 and CO was "CO 2 feed gas flow rate." At the maximum desirable value of 0.92, the optimum CH 4 , CO 2 , and N 2 conversion were 84.91%, 44.40%, and 3.37%, respectively, and the selectivities of H 2 and CO were 51.31% and 61.17%, respectively. This was achieved at a gas feed flow rate of 0.19, 0.38, and 1.49 L min −1 for CH 4 , CO 2 , and N 2 , respectively.

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Lanthanoid-containing Ni-based catalysts for dry reforming of methane: A review

Salaev

Liotta

Vodyankina

2022

International Journal of Hydrogen Energy

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Modeling and optimization of synthesis parameters in nanostructure La 1 − x Ba x Ni 1 − y Cu y O 3 catalysts used in the reforming of methane with CO 2

Cited by 5 publications

References 22 publications

Catalyst Optimization Design Based on Artificial Neural Network

Catalyst Optimization Design Based on Artificial Neural Network

Optimisation of CH₄ and CO₂ conversion and selectivity of H₂ and CO for the dry reforming of methane by a microwave plasma technique using a Box–Behnken design

Lanthanoid-containing Ni-based catalysts for dry reforming of methane: A review

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Modeling and optimization of synthesis parameters in nanostructure La 1 − x Ba x Ni 1 − y Cu y O 3 catalysts used in the reforming of methane with CO 2

Cited by 5 publications

References 22 publications

Catalyst Optimization Design Based on Artificial Neural Network

Catalyst Optimization Design Based on Artificial Neural Network

Optimisation of CH4 and CO2 conversion and selectivity of H2 and CO for the dry reforming of methane by a microwave plasma technique using a Box–Behnken design

Lanthanoid-containing Ni-based catalysts for dry reforming of methane: A review

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Optimisation of CH₄ and CO₂ conversion and selectivity of H₂ and CO for the dry reforming of methane by a microwave plasma technique using a Box–Behnken design