Predicting Sooting Propensity of Oxygenated Fuels Using Artificial Neural Networks

Abstract

The self-learning capabilities of artificial neural networks (ANNs) from large datasets have led to their deployment in the prediction of various physical and chemical phenomena. In the present work, an ANN model was developed to predict the yield sooting index (YSI) of oxygenated fuels using the functional group approach. A total of 265 pure compounds comprising six chemical classes, namely paraffins (n and iso), olefins, naphthenes, aromatics, alcohols, and ethers, were dis-assembled into eight constituent functional groups, namely paraffinic CH3 groups, paraffinic CH2 groups, paraffinic CH groups, olefinic –CH=CH2 groups, naphthenic CH-CH2 groups, aromatic C-CH groups, alcoholic OH groups, and ether O groups. These functional groups, in addition to molecular weight and branching index, were used as inputs to develop the ANN model. A neural network with two hidden layers was used to train the model using the Levenberg–Marquardt (ML) training algorithm. The developed model was tested with 15% of the random unseen data points. A regression coefficient (R2) of 0.99 was obtained when the experimental values were compared with the predicted YSI values from the test set. An average error of 3.4% was obtained, which is less than the experimental uncertainty associated with most reported YSI measurements. The developed model can be used for YSI prediction of hydrocarbon fuels containing alcohol and ether-based oxygenates as additives with a high degree of accuracy.

Pyrolysis and Oxidation of Waste Tire Oil: Analysis of Evolved Gases

“…The functional groups present in a fuel often dictate its properties, 39 − 43 and the evolved groups dictate the propensity to form soot. 40 , 44 , 45 The IR band assignments for the aforementioned functional groups are provided in Table 3 including the selected wavenumber for each functional group.…”

“…Analysis of these functional groups indicates the thermal decomposition reactions occurring during pyrolysis, and this information could help optimize gasifier conditions such that high-value gases could be obtained while minimizing emissions. The functional groups present in a fuel often dictate its properties, − and the evolved groups dictate the propensity to form soot. ,, The IR band assignments for the aforementioned functional groups are provided in Table including the selected wavenumber for each functional group.…”

A Methodology for Designing Octane Number of Fuels Using Genetic Algorithms and Artificial Neural Networks

“…Petroleum gasoline is composed of paraffin, olefins, naphthene, and aromatic compounds that can be disassembled into a finite number of functional groups which define the fuel’s properties. The functional groups in fuel have been used to predict the cetane number of diesel fuel and the sooting propensity of a wide class of neat compounds and also to formulate surrogates for gasoline, diesel, and jet fuels. − Along with functional groups, two additional structural parameters called branching index (BI) and molecular weight (MW) have been included as input features in the ANN model. After each generated mixture is converted into the input features (i.e., functional groups, BI and MW), the ANN model predicts the RON and MON of each.…”

Identification and Quantification of Hydrocarbon Functional Groups in Gasoline Using 1H-NMR Spectroscopy for Property Prediction

“…The maximum soot volume fraction is then determined, from which YSI is calculated. Recently a functional group-based neural network model [45] was proposed for prediction of the TSI and YSI of oxygenated fuels. The YSI model was developed by using 265 neat compounds with hidden layers consisting of 25 nodes in each layer.…”