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Effect of activation function in modeling the nexus between carbon tax, CO2 emissions, and gas-fired power plant parameters

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dc.citedby4
dc.contributor.authorAyodele O.F.en_US
dc.contributor.authorAyodele B.V.en_US
dc.contributor.authorMustapa S.I.en_US
dc.contributor.authorFernando Y.en_US
dc.contributor.authorid57377355900en_US
dc.contributor.authorid56862160400en_US
dc.contributor.authorid36651549700en_US
dc.contributor.authorid26664524300en_US
dc.date.accessioned2023-05-29T09:05:19Z
dc.date.available2023-05-29T09:05:19Z
dc.date.issued2021
dc.descriptionCarbon dioxide; Chemical activation; Errors; Flow of gases; Forecasting; Fossil fuel power plants; Fossil fuels; Gas emissions; Global warming; Hyperbolic functions; Multilayer neural networks; Natural gas; Sensitivity analysis; Activated function; Carbon taxes; CO 2 emission; Emissions Trading; Gas-fired power plants; Hidden layers; Outer layer; Perceptron neural networks; Performance; Sigmoid activation function; Emission controlen_US
dc.description.abstractHuge emissions of carbon dioxide (CO2) from the utilization of fossil fuel for power generation has significantly contributed to global warming. In view of this, technological pathways have been initiated to mitigate the effect of CO2 emissions through capture, storage, and utilization. Besides, there is an increasing acceptance of carbon tax which is levied in the proportion of carbon emissions from the utilization of fossil fuel. In this study, the nexus between carbon tax, equivalent CO2 emissions from the gas-fired power plant, natural gas flow rate, and air-to-fuel ratio was modeled using a perceptron neural network. The effect of various combinations of identity, hyperbolic tangent, and sigmoid activation functions at the hidden and outer layer of the neural network on the performance of the models was investigated. The various network configurations were trained using the Levenberg-Marquardt algorithm with the network errors backpropagated to enhance the performance. The optimized networks consist of three input units, 15 hidden neurons, and one output unit. The network performance in modeling the carbon tax prediction resulted in R2 of 0.999, 0.999, 0.999, 0.998, and 0.999 for model 1, model 2, model 3, model 4, and model 5, respectively which is an indication that the calculated carbon tax was strongly correlated with the predicted values. The prediction errors of 0.019, 0.009, 0.002, 0.016, 0.002 obtained from model 1, model 2, model 3, model 4, and model 5, respectively revealed the robustness of the models in predicting the carbon tax with minimum error. Among the various configurations investigated, the perceptron neural network configured with hyperbolic tangent and sigmoid activation function at the hidden and outer layers, as well as the configuration with sigmoid activation functions at the hidden and outer layers, offer the best performance. The sensitivity analysis shows that the flow rate of the natural gas had the most significant effect on the predicted carbon tax. � 2021 The Author(s)en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo100111
dc.identifier.doi10.1016/j.ecmx.2021.100111
dc.identifier.scopus2-s2.0-85121449104
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85121449104&doi=10.1016%2fj.ecmx.2021.100111&partnerID=40&md5=e75566d6a9748b6e98d03eb4394a7367
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25859
dc.identifier.volume12
dc.publisherElsevier Ltden_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleEnergy Conversion and Management: X
dc.titleEffect of activation function in modeling the nexus between carbon tax, CO2 emissions, and gas-fired power plant parametersen_US
dc.typeArticleen_US
dspace.entity.typePublication
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