{"title":"Estimation of Net Heat of Combustion of Light Kerosene Distillates Using Artificial Neural Networks","authors":"Kahina Bedda","doi":"10.1134/s0036024424700183","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>In this study, six feedforward neural network models were developed to estimate the net heat of combustion of light kerosene distillates. These networks use different sets of physicochemical properties of the distillates as input variables and are all composed of 8 sigmoid hidden neurons and one linear output neuron. The networks were designed in MATLAB software with 205 data points using the nftool command. Determining the relative importance of input variables in the networks revealed the significant effect of density on the estimates. The developed models as well as two correlative methods taken from the literature were used to predict the net heat of combustion of 40 other samples. The statistical analysis of the results was carried out by calculating for each estimation method the absolute errors, the mean absolute error, the standard deviation of the absolute errors and the coefficient of determination. It was found that the most accurate method is the neural network model based on the density, viscosity, aromatics content and sulfur content of the distillates. The least efficient method is the neural network that does not include density in its inputs, which once again indicates the importance of this property. Consequently, density should be taken into account to ensure high prediction ability of estimation methods.</p>","PeriodicalId":767,"journal":{"name":"Russian Journal of Physical Chemistry A","volume":null,"pages":null},"PeriodicalIF":0.7000,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Russian Journal of Physical Chemistry A","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1134/s0036024424700183","RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
引用次数: 0
Abstract
In this study, six feedforward neural network models were developed to estimate the net heat of combustion of light kerosene distillates. These networks use different sets of physicochemical properties of the distillates as input variables and are all composed of 8 sigmoid hidden neurons and one linear output neuron. The networks were designed in MATLAB software with 205 data points using the nftool command. Determining the relative importance of input variables in the networks revealed the significant effect of density on the estimates. The developed models as well as two correlative methods taken from the literature were used to predict the net heat of combustion of 40 other samples. The statistical analysis of the results was carried out by calculating for each estimation method the absolute errors, the mean absolute error, the standard deviation of the absolute errors and the coefficient of determination. It was found that the most accurate method is the neural network model based on the density, viscosity, aromatics content and sulfur content of the distillates. The least efficient method is the neural network that does not include density in its inputs, which once again indicates the importance of this property. Consequently, density should be taken into account to ensure high prediction ability of estimation methods.
期刊介绍：
Russian Journal of Physical Chemistry A. Focus on Chemistry (Zhurnal Fizicheskoi Khimii), founded in 1930, offers a comprehensive review of theoretical and experimental research from the Russian Academy of Sciences, leading research and academic centers from Russia and from all over the world.
Articles are devoted to chemical thermodynamics and thermochemistry, biophysical chemistry, photochemistry and magnetochemistry, materials structure, quantum chemistry, physical chemistry of nanomaterials and solutions, surface phenomena and adsorption, and methods and techniques of physicochemical studies.
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