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

Carbon 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...

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Main Authors: Ayodele O.F., Ayodele B.V., Mustapa S.I., Fernando Y.
Other Authors: 57377355900
Format: Article
Published: Elsevier Ltd 2023
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spelling my.uniten.dspace-258592023-05-29T17:05:19Z Effect of activation function in modeling the nexus between carbon tax, CO2 emissions, and gas-fired power plant parameters Ayodele O.F. Ayodele B.V. Mustapa S.I. Fernando Y. 57377355900 56862160400 36651549700 26664524300 Carbon 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 control Huge 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) Final 2023-05-29T09:05:19Z 2023-05-29T09:05:19Z 2021 Article 10.1016/j.ecmx.2021.100111 2-s2.0-85121449104 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121449104&doi=10.1016%2fj.ecmx.2021.100111&partnerID=40&md5=e75566d6a9748b6e98d03eb4394a7367 https://irepository.uniten.edu.my/handle/123456789/25859 12 100111 All Open Access, Gold Elsevier Ltd Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Carbon 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 control
author2 57377355900
author_facet 57377355900
Ayodele O.F.
Ayodele B.V.
Mustapa S.I.
Fernando Y.
format Article
author Ayodele O.F.
Ayodele B.V.
Mustapa S.I.
Fernando Y.
spellingShingle Ayodele O.F.
Ayodele B.V.
Mustapa S.I.
Fernando Y.
Effect of activation function in modeling the nexus between carbon tax, CO2 emissions, and gas-fired power plant parameters
author_sort Ayodele O.F.
title Effect of activation function in modeling the nexus between carbon tax, CO2 emissions, and gas-fired power plant parameters
title_short Effect of activation function in modeling the nexus between carbon tax, CO2 emissions, and gas-fired power plant parameters
title_full Effect of activation function in modeling the nexus between carbon tax, CO2 emissions, and gas-fired power plant parameters
title_fullStr Effect of activation function in modeling the nexus between carbon tax, CO2 emissions, and gas-fired power plant parameters
title_full_unstemmed Effect of activation function in modeling the nexus between carbon tax, CO2 emissions, and gas-fired power plant parameters
title_sort effect of activation function in modeling the nexus between carbon tax, co2 emissions, and gas-fired power plant parameters
publisher Elsevier Ltd
publishDate 2023
_version_ 1806427614044225536
score 13.222552

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