Dataset Information

General Model Based on Artificial Neural Networks for Estimating the Viscosities of Oxygenated Fuels.

ABSTRACT: Oxygenated fuel is a promising alternative fuel for engines because of the advantage of low emission. In this work, a general model based on back-propagation neural networks was developed for estimating the viscosities of different kinds of oxygenated fuels including esters, alcohols, and ethers, whose input variables are pressure, temperature, critical pressure, critical temperature, molar mass, and acentric factor. The viscosity data of 31 oxygenated fuels (1574 points) at temperatures ranging from 243.15 to 413.15 K and at pressures ranging from 0.1 to 200 MPa were collected to train and test the back-propagation neural network model. The comparison result shows that the predictions of the proposed back-propagation neural network model agree well with the experimental viscosity data of all studied oxygenated fuels using the general parameters (weight and bias). The average absolute relative deviations for training data, validation data, and testing data are 1.19%, 1.27%, and 1.30%, respectively.

SUBMITTER: Liu X

PROVIDER: S-EPMC6788068 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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General Model Based on Artificial Neural Networks for Estimating the Viscosities of Oxygenated Fuels.

Liu Xiangyang X Yang Feng F Chu Jianchun J Zhu Chenyang C He Maogang M Zhang Ying Y

ACS omega 20190925 15

Oxygenated fuel is a promising alternative fuel for engines because of the advantage of low emission. In this work, a general model based on back-propagation neural networks was developed for estimating the viscosities of different kinds of oxygenated fuels including esters, alcohols, and ethers, whose input variables are pressure, temperature, critical pressure, critical temperature, molar mass, and acentric factor. The viscosity data of 31 oxygenated fuels (1574 points) at temperatures ranging ...[more]

PMID: 31616836

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Project description:BackgroundThe performance of previously published glomerular filtration rate (GFR) estimation equations degrades when directly used in Chinese population. We incorporated more independent variables and using complicated non-linear modeling technology (artificial neural network, ANN) to develop a more accurate GFR estimation model for Chinese population.MethodsThe enrolled participants came from the Third Affiliated Hospital of Sun Yat-sen University, China from Jan 2012 to Jun 2016. Participants with age < 18, unstable kidney function, taking trimethoprim or cimetidine, or receiving dialysis were excluded. Among the finally enrolled 1952 participants, 1075 participants (55.07%) from Jan 2012 to Dec 2014 were assigned as the development data whereas 877 participants (44.93%) from Jan 2015 to Jun 2016 as the internal validation data. We in total developed 3 GFR estimation models: a 4-variable revised CKD-EPI (chronic kidney disease epidemiology collaboration) equation (standardized serum creatinine and cystatin C, age and gender), a 9-variable revised CKD-EPI equation (additional auxiliary variables: body mass index, blood urea nitrogen, albumin, uric acid and hemoglobin), and a 9-variable ANN model.ResultsCompared with the 4-variable equation, the 9-variable equation could not achieve superior performance in the internal validation data (mean of difference: 5.00 [3.82, 6.54] vs 4.67 [3.55, 5.90], P = 0.5; interquartile range (IQR) of difference: 18.91 [17.43, 20.48] vs 20.11 [18.46, 21.80], P = 0.05; P30: 76.6% [73.7%, 79.5%] vs 75.8% [72.9%, 78.6%], P = 0.4), but the 9-variable ANN model significantly improve bias and P30 accuracy (mean of difference: 2.77 [1.82, 4.10], P = 0.007; IQR: 19.33 [17.77, 21.17], P = 0.3; P30: 80.0% [77.4%, 82.7%], P < 0.001).ConclusionsIt is suggested that using complicated non-linear models like ANN could fully utilize the predictive ability of the independent variables, and then finally achieve a superior GFR estimation model.

Dataset Information

General Model Based on Artificial Neural Networks for Estimating the Viscosities of Oxygenated Fuels.

Publications

General Model Based on Artificial Neural Networks for Estimating the Viscosities of Oxygenated Fuels.

Similar Datasets

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