Recent Developments on Statistical and Neural Network Tools Focusing on Biodiesel Quality

Marques, Delano Brandes; Filho, Alex O. Barradas; Romariz, Alexandre Ricardo Soares; Viegas, Isabelle Moraes Amorim; Luz, Djavania Azevêdo da; Barros, Allan Kardec; Labidi, Sofiane; Ferraudo, Antônio Sérgio

doi:10.14355/ijcsa.2014.0303.01

Cited by 7 publications

(6 citation statements)

References 76 publications

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“…Its movements will produce forces in the axial direction called thrust force, and in the rotational direction called torque then these forces will affect the hole roundness and surface roughness which can influence the quality of the product [7,8]. To predict the values of these parameters, it is adequate to apply neural networks, especially the one namely Backpropagation Neural Network (BPNN) [9][10][11][12]. After obtaining the best network to gain the parameters, it is necessary to find the optimum values of these parameters.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective Optimization Using Neural Network, Differential Evolution, and Teaching Learning Based Optimization in Drilling Process of Glass Fiber Reinforced Polymer

Fatika

Effendi

2021

JMES Int. Journal of Mech. Eng. Sc.

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Section: Introductionmentioning

confidence: 99%

Multi-objective Optimization Using Neural Network, Differential Evolution, and Teaching Learning Based Optimization in Drilling Process of Glass Fiber Reinforced Polymer

Fatika

Effendi

2021

JMES Int. Journal of Mech. Eng. Sc.

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“…Typically, studies related to the quality control of biofuels have the goal to search efficient methods that monitor the fuels produced and commercialized avoiding damages to the environment and consumer injury [9,16].…”

Section: Introductionmentioning

confidence: 99%

Applications of Artificial Neural Networks in Biofuels

Filho¹,

Viegas²

2018

Advanced Applications for Artificial Neural Networks

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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.

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“…Pasadakis and co-authors performed a distillation prediction profile and cold properties of diesel fuels using mid-infrared spectroscopy and neural networks. Other works found in the literature apply ANNs in predicting biodiesel and diesel properties. − Between different types of ANNs, the feed-forward networks of multilayer perceptron (MLP) appear as frequently used models which consist of an input layer, one or more hidden layers, and an output layer …”

Section: Introductionmentioning

confidence: 99%

Predicting Cetane Index, Flash Point, and Content Sulfur of Diesel–Biodiesel Blend Using an Artificial Neural Network Model

et al. 2017

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Artificial neural networks (ANNs) were used to predict, not simultaneously, flash point, cetane index, and sulfur content (S1800) of diesel blends (7% (v/v) biodiesel) using distillation curves (ASTM D86), specific gravity at 20 °C (ASTM D405), cetane index (ASTM D4737), flash point (ASTM D93), and sulfur content (ASTM D4294). The low error values obtained compared with other chemometric based models described in literature, and high correlation coefficients between reference and predicted values showed that ANNs were efficient in determining flash point, cetane index/cetane number, and sulfur content (S1800). The constructed model contains diesel samples of different compositions (50, 500, and 1800 mg kg −1 ), thus revealing the variety of fuel in the Brazilian market. Furthermore, the proposed method has advantages such as low cost and easy implementation, as it applies the results of the routine test to evaluate the quality control of diesel.

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Recent Developments on Statistical and Neural Network Tools Focusing on Biodiesel Quality

Cited by 7 publications

References 76 publications

Multi-objective Optimization Using Neural Network, Differential Evolution, and Teaching Learning Based Optimization in Drilling Process of Glass Fiber Reinforced Polymer

Multi-objective Optimization Using Neural Network, Differential Evolution, and Teaching Learning Based Optimization in Drilling Process of Glass Fiber Reinforced Polymer

Applications of Artificial Neural Networks in Biofuels

Predicting Cetane Index, Flash Point, and Content Sulfur of Diesel–Biodiesel Blend Using an Artificial Neural Network Model

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