Application of artificial neural networks to predict viscosity, iodine value and induction period of biodiesel focused on the study of oxidative stability

Filho, Alex Oliveira Barradas; Barros, Allan Kardec; Labidi, Sofiane; Viegas, Isabelle Moraes Amorim; Marques, Delano Brandes; Romariz, Alexandre Ricardo Soares; Sousa, Raquel Machado de; Marques, Aldaléa Lopes Brandes; Marques, Edmar P.

doi:10.1016/j.fuel.2014.12.016

Cited by 52 publications

(31 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The approach has been taken into more complex mixtures such as biodiesel that contains various components. Several properties, including density, viscosity, cetane number, iodine value and induction period, were correlated using ANNs by Rocabruno-Valdés et al [20] and Barradas Filho et al [21].…”

Section: Viscosity Correlationmentioning

confidence: 99%

Artificial Neural Networks (ANNs) for Density and Viscosity Predictions of CO2 Loaded Alkanolamine + H2O Mixtures

et al. 2020

View full text Add to dashboard Cite

The physical properties, like density and viscosity, of alkanolamine + H 2 O (water) + CO 2 (carbon dioxide) mixtures receive a significant amount of attention as they are essential in equipment sizing, mathematical modelling and simulations of amine-based post-combustion CO 2 capture processes. Non-linear models based on artificial neural networks (ANNs) were trained to correlate measured densities and viscosities of monoethanol amine (MEA) + H 2 O, MEA + H 2 O + CO 2 , and 2-amino-2-methyl-1-propanol (AMP) + MEA + H 2 O + CO 2 mixtures and results were compared with conventional correlations found in literature. For CO 2 -loaded aqueous amine mixtures, results from the ANN models are in good agreement with measured properties with less than 1% average absolute relative deviation (AARD). The ANN-based methodology shows much better agreement (R 2 > 0.99) between calculated and measured values than conventional correlations.

show abstract

Section: Viscosity Correlationmentioning

confidence: 99%

Artificial Neural Networks (ANNs) for Density and Viscosity Predictions of CO2 Loaded Alkanolamine + H2O Mixtures

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The main products formed by the oxidation of biodiesel can cause formation of insoluble gums in the engine, filter clogging, injector cocking and corrosion of the metal parts of the engine. Therefore, the evaluation of the oxidative stability of biodiesel is considered by many researchers in the literature to be a very important analysis that should be done because it is directly related to the deterioration capacity (oxidation) and to the time in which the biofuel can be stored (induction period) [26].…”

Section: Advanced Applications For Artificial Neural Networkmentioning

confidence: 99%

“…In the paper "Application of artificial neural networks to predict viscosity, iodine value and induction period of biodiesel focused on the study of oxidative stability", Barradas Filho and collaborators optimized ANN models to predict viscosity, iodine value and induction period (oxidative stability) of 98 biodiesel samples by its fatty esters composition [26].…”

mentioning

confidence: 99%

See 1 more Smart Citation

Applications of Artificial Neural Networks in Biofuels

Filho¹,

Viegas²

2018

Advanced Applications for Artificial Neural Networks

Self Cite

View full text Add to dashboard Cite

This chapter is focused on the application of artificial neural networks (ANNs) in the development of alternative methods for biofuel quality issues. At first, the advances and the proliferation of models and architectures of artificial neural networks are highlighted in the text by the characteristics of robustness and fault tolerance, learning capacity, uncertain information processing and parallelism, which allow the application in problems of complex nature. In this scenario, biofuels are contextualized and focused on issues of quality control and monitoring. Therefore, this chapter leads to a study of prediction and/or classification of biofuels quality parameters by the description of published works on the topic under discussion. Afterwards, a case study is performed to demonstrate, in a practical way, the steps and procedures to build alternative models for predicting the oxidative stability of biodiesel. The procedure goes from the processing of the data obtained by the near infrared until the evaluation of the alternative method developed by the neural network. In addition, some evaluation parameters are described for the assessment of the alternative method built. As a result, the feasibility and practicality of the application of neural networks to the quality of biofuels are proven.

show abstract

“…The iodine value quantifies the unsaturations present in fatty acids from the determination of the amount of iodine that reacts directly with the double and triple bonds between carbon atoms in the fatty acid chains (Barradas Filho et al, 2015). The characteristic Newtonian behaviour of vegetable oils is influenced by their major components, such as mono-and polyunsaturated fatty acids.…”

Section: Physicochemical Characteristicsmentioning

confidence: 99%

Common components and specific weights analysis for the discrimination and evaluation of vegetable oil quality

Ferreira

Santos

Rampazzo

et al. 2017

Int J of Food Sci Tech

View full text Add to dashboard Cite

Summary The aim of this study was to assess the quality of different vegetable oils by applying common components and specific weights analysis as a tool for the evaluation and discrimination of chromatography, spectral and physicochemical data. This multiblock method of data analysis divided the data into three common components, corresponding to 56.44%, 34.74% and 8.77% of variance, and it was influenced mostly by chromatography, physicochemical and spectral data, respectively. Gas chromatography, which was used for discrimination of the botanical origin of oil and the groups of saturated, monounsaturated and polyunsaturated fatty acids, was situated in the first common dimension; physicochemical analysis, which was applied to evaluate quality parameters such as acid and saponification value and determine the stability of the product, was situated in the second common dimension. FTIR analysis, by exerting a minor influence on the common dimensions, was considered dispensable in evaluating the quality of vegetable oils by common components and specific weights analysis. Therefore, multiblock analysis could efficiently discriminate vegetable oils.

show abstract

Application of artificial neural networks to predict viscosity, iodine value and induction period of biodiesel focused on the study of oxidative stability

Cited by 52 publications

References 34 publications

Artificial Neural Networks (ANNs) for Density and Viscosity Predictions of CO2 Loaded Alkanolamine + H2O Mixtures

Artificial Neural Networks (ANNs) for Density and Viscosity Predictions of CO2 Loaded Alkanolamine + H2O Mixtures

Applications of Artificial Neural Networks in Biofuels

Common components and specific weights analysis for the discrimination and evaluation of vegetable oil quality

Contact Info

Product

Resources

About