Radial Basis Function Neural Network Model Prediction of Thermo-catalytic Carbon Dioxide Oxidative Coupling of Methane to C2-hydrocarbon

Ayodele, Bamidele Victor; Mustapa, Siti Indati; Witoon, Thongthai; Kanthasamy, Ramesh; Zwawi, Mohammed; Owabor, Chiedu N.

doi:10.1007/s11244-020-01401-0

Cited by 10 publications

(5 citation statements)

References 36 publications

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“…The transformation of this understanding into reality must rely on strict law enforcement. By only having a high-quality law enforcement team and using the law to protect the legitimate rights and interests of the public, we can enforce the law strictly and fairly, so that the legitimate rights and interests of the public can be effectively protected, so as to enhance the public's sense of trust and security in the law [ 20 , 21 ]. Once the public has a corresponding sense of security for social law and trusts the protection of their own rights and interests by law, this trust will react on legal awareness and promote the further improvement of public legal awareness, as shown in Figure 12 [ 22 ].…”

Section: Cultivation and Development Of Public Legal Consciousnessmentioning

confidence: 99%

Effective Strategies to Promote the Cultivation of Public Legal Consciousness from the Perspective of Social Psychology

2022

Journal of Environmental and Public Health

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In order to build a harmonious economic society, show the legal function in the society, enable people to live and work in a better environment, and increase people’s practicality in the legal society, it needs to be analyzed from the perspective of social psychology. This paper comprehensively analyzes the current public legal knowledge, uses a deep neural network model, and implements the cultivation of public legal awareness. On this basis, it integrates the data and information before and after 2020, uses the method of book case distribution training, constructs a legal framework, relies on the data distribution technology, constructs a web legal awareness training system, and increases the conditions for promoting the cultivation of public legal awareness based on the background operation of the integrated website. It can be seen that in the perspective of social psychology, the importance of the cultivation of public legal awareness can be achieved through new algorithms, so that people can pay attention to the cultivation of legal awareness, which can provide corresponding protection for the operation and maintenance of the legal system.

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Section: Cultivation and Development Of Public Legal Consciousnessmentioning

confidence: 99%

Effective Strategies to Promote the Cultivation of Public Legal Consciousness from the Perspective of Social Psychology

2022

Journal of Environmental and Public Health

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“…The influence of the input parameters on the best model output was examined using relative importance as shown in Figure 9 43,44 . The relative importance of the RSS particle size, the HDPE particle size, the gasification temperature, and the amount of plastic in the mixture were estimated using the modified Garson algorithm as 0.26, 0.322, 0.26, and 0.176, respectively 45 .…”

Section: Resultsmentioning

confidence: 99%

“…The influence of the input parameters on the best model output was examined using relative importance as shown in Figure 9. 43,44 The relative importance of the RSS particle size, the HDPE particle size, the gasification temperature, and the amount of plastic in the mixture were estimated using the modified Garson algorithm as 0.26, 0.322, 0.26, and 0.176, respectively. 45 Based on the relative importance of each of the input parameters, it is evident that the RSS particle size, the HDPE particle size, the gasification temperature, and the amount of plastic in the mixture have a significant influence on the 1-LMLP neural network model output.…”

Section: The Multilayer Perceptron Neural Networkmentioning

confidence: 99%

Modeling the prediction of hydrogen production by co‐gasification of plastic and rubber wastes using machine learning algorithms

et al. 2021

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This study aimed to investigate the application of radial basis function (RBF) and multilayer perceptron (MLP) artificial neural networks for modeling hydrogen production by co-gasification of rubber and plastic wastes. Both the RBF and MLP neural networks were configured by determining the besthidden neurons that could offer optimized performance. Based on the besthidden neurons, a model architecture of 4-16-1, 4-20-1, 4-17-1, and 4-3-1 was obtained for RBF (with standard activation function), RBF (with ordinary activation function), one-layer MLP, and two-layer MLP, respectively, indicating the number of input nodes, the hidden neurons, and the output nodes. The predicted hydrogen production from the co-gasification closely agrees with the observed values. The 1-layer MLP with R 2 of .990 displayed the best performance with all the input parameters having a significant influence on 9 the model output. The neural network algorithm obtained in this study could be implemented in the eventuality of making a vital decision in the process operation of the co-gasification process for hydrogen production.

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“…65, No. 9 (2022) basis function network consists of three nodes namely starch content, extrusion speed, extrusion temperature, ten hidden units, and three targeted outputs namely tensile strength, the elongation to break and the yield point [29][30][31]. The input nodes are inter-connected with the hidden layers through artificial neurons.…”

Section: Experimentation and Model Developmentmentioning

confidence: 99%

Data-Driven Radial Basis Function Approach to Evaluate the Effect of Process Variables on Solar-Assisted Degradation of Starch-Plastics Composite in an Extruder Reactor

et al. 2022

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This study investigated the effect of process variables on the biodegradation of starch-plastics composite. The extrusion process was carried out with low-density polyethylene (LDPE)-starch blends in varied extrusion temperature and extruder speed and starch content. The parametric analysis based on the three-dimensional plots revealed a non-linear relationship between the input parameters and output. The datasets obtained from the extrusion process was employed for data-driven modeling using radial basis function, a machine learning algorithm. The radial basis function was trained using the backpropagation rule resulting in the prediction of the tensile strength, elongation to break, and yield point of the LDPEstarch composite. The robustness of the radial basis function in modeling the process is evident from the R 2 of 0.804, 0.855, and 0.766 obtained to predict tensile strength, elongation to break, and yield point, respectively, with minima prediction errors. The extrusion temperature, extruder speed, and starch content significantly influenced the predicted tensile strength, elongation to break, and yield point. However, the extrusion temperature was found to have the most significant effect. This study can be employed in understanding the appropriate conditions of parameters required to obtain a degradable material based on its mechanical properties.

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Radial Basis Function Neural Network Model Prediction of Thermo-catalytic Carbon Dioxide Oxidative Coupling of Methane to C2-hydrocarbon

Cited by 10 publications

References 36 publications

Effective Strategies to Promote the Cultivation of Public Legal Consciousness from the Perspective of Social Psychology

Effective Strategies to Promote the Cultivation of Public Legal Consciousness from the Perspective of Social Psychology

Modeling the prediction of hydrogen production by co‐gasification of plastic and rubber wastes using machine learning algorithms

Data-Driven Radial Basis Function Approach to Evaluate the Effect of Process Variables on Solar-Assisted Degradation of Starch-Plastics Composite in an Extruder Reactor

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