Prediction of Cetane Number and Ignition Delay of Biodiesel Using Artificial Neural Networks

Sánchez-Borroto, Yisel; Piloto‐Rodríguez, Ramón; Errasti, Michel; Sierens, Roger; Verhelst, Sebastian

doi:10.1016/j.egypro.2014.10.297

Cited by 22 publications

(18 citation statements)

References 16 publications

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“…These oxygenated fuels include alcohols [24][25][26][27][28][29], ethers [12][13][14]30,31], esters [30,32] etc. Prediction of DCN of pure fuels (both oxygenated and non-oxygenated) have been a focus of many recent studies [16,[33][34][35][36][37][38][39]. Although engine operation with pure oxygenated fuels is possible, the use of pure oxygenated fuels in CI engine applications may be limited due to fuel compatibility issues such as, poor lubricating properties, corrosiveness, and low energy density [57].…”

Section: Introductionmentioning

confidence: 99%

“…Several methods and correlations have been developed to predict ignition quality (i.e. D/CN) of n-paraffins [41], pure hydrocarbons [35,37,[42][43][44], blends of pure hydrocarbons [37], oxygenated hydrocarbons [34,39,[45][46][47], diesel fuels [36,37,[48][49][50][51][52][53][54][55][56][57][58], synthetic diesels [59], biofuel candidates [33], furanic biofuel additives [16] and biodiesel [38,[60][61][62]. The D/CN prediction models reported in the literature have used both physical and chemical properties of the fuel as model inputs.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Ignition Quality of Oxygenated Fuels Using Artificial Neural Networks

Jameel

Oudenhoven

Naser

et al. 2021

SAE Int. J. Fuels Lubr.

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Artificial intelligence based computing systems like artificial neural networks (ANN) have recently found increasing applications in predicting complex chemical phenomena like combustion properties. The present work deals with the development of an ANN model which can predict the derived cetane number (DCN) of oxygenated fuels containing alcohol and ether functionalities. Experimental DCN's of 499 fuels comprising of 116 pure compounds, 222 pure compound blends, and 159 real fuel blends were used as the dataset for model development. DCN measurements of sixty new fuels were carried out in the present work and the data for the rest was collected from the literature. Fuel chemical composition expressed in the form of eight 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, along with two structural parameters namely, molecular weight and branching index (BI), were used as the ten input features of the model. The qualitative and quantitative determination of functional groups present in real fuels was performed using 1 H nuclear magnetic resonance (NMR) spectroscopy. A robust ANN methodology was followed to prevent over fitting using a multilevel grid search and genetic algorithm. The final developed model with two hidden layers was tested with 15 % of randomly generated unseen points from the dataset and a regression coefficient (R 2 ) of 0.992 was observed between the experimental and predicted DCN values. An average absolute error of 0.91 obtained from the test set indicates that the developed ANN model is successful in predicting the DCN of oxygenated fuels and captures the dependence of the fuel's ignition quality (i.e. DCN) on its constituent functional groups.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Ignition Quality of Oxygenated Fuels Using Artificial Neural Networks

Jameel

Oudenhoven

Naser

et al. 2021

SAE Int. J. Fuels Lubr.

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“…This would enable the determination of an average value for the polytropic coefficient k on which the linear function log P-log V depends. The straight-line portion of the compression process will start deviating from its path with the start of combustion [41,63]. The first and second derivatives of the pressure curve with respect to time (dp/dt and d 2 p/dt 2 ) also allow the evaluation of the ID with good accuracy [40,55].…”

Section: Analysis Of Ignition Delaymentioning

confidence: 99%

Study of effects of ignition improvers on ethanol compression ignition in the rapid compression machine

Sánchez

Martins

Braga

et al. 2020

J Braz. Soc. Mech. Sci. Eng.

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The world recognizes that the main source of energy used in transportation is diesel fuel because it is more economical and is also more efficient. For this reason, various sectors of transport and fuel production are developing new technologies aiming at the replacement of traditional fossil fuels by other renewable sources. In the current market, it is possible to find systems that run on blends of diesel and other renewable fuels and test systems that run on mixtures of ethanol and some other substance to reduce auto-ignition. To be able to use ethanol in compression ignition (CI) engines where diesel fuel is typically used, the poor flammability of ethanol under compression ignition conditions is the major problem to be overcome. The techniques used in trials with CI engines to correct this problem are preheating the air in the intake manifold, increasing the compression ratio or the addition of an ignition improver for ethanol. In this study, different tests were carried out in a rapid compression machine using ignition improvers derived from polyethylene glycol. Tests were conducted for different instants for the start of injection of the mixtures and different compression ratios.

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“…Most biodiesel forms have higher cetane numbers than diesel, so the usage of biodiesel may increase the life of the engine (Ramkumar & Kirubakaran, ). Sánchez‐Borroto, Piloto‐Rodriguez, Errasti, Sierens, and Verhelst () used Artificial Neural Network models for the prediction of cetane numbers and ignition delays of various types of biodiesel.…”

Section: Fuel Characterizationmentioning

confidence: 99%

Production, engine performance, combustion, emission characteristics and economic feasibility of biodiesel from waste cooking oil: A review

Rekhate

Prajapati

2019

Environmental Quality Mgmt

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The depletion of fossil fuel reserves and increasing demands for diesel are considered to be important triggers for many of the initiatives that have been taken to search for possible sources for the production of biodiesel from materials available within the country. It is possible to produce biodiesel from waste/used cooking oils (WCO) that is comparable in quality to that of fresh vegetable oil. Not only does reuse of WCO, which can otherwise harm human health, reduce the burden on the government of treating oily wastewater, disposing of the waste, and maintaining public sewers, it also significantly lowers the production cost of biodiesel. In the process of frying, oil undergoes many reactions, leading to the formation of a number of undesirable compounds, such as polymers, free fatty acids, and many other chemicals. This poses challenges in the transesterification of WCO. This article covers different techniques in the production of biodiesel from WCO.It also compares combustion, emissions, and engine performance characteristics of biodiesel from WCO as well as factors affecting biodiesel production from WCO and its economic feasibility. K E Y W O R D S

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Prediction of Cetane Number and Ignition Delay of Biodiesel Using Artificial Neural Networks

Cited by 22 publications

References 16 publications

Predicting Ignition Quality of Oxygenated Fuels Using Artificial Neural Networks

Predicting Ignition Quality of Oxygenated Fuels Using Artificial Neural Networks

Study of effects of ignition improvers on ethanol compression ignition in the rapid compression machine

Production, engine performance, combustion, emission characteristics and economic feasibility of biodiesel from waste cooking oil: A review

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