Attesting compliance of biodiesel quality using composition data and classification methods

Lopes, Marcus Vinicius; Filho, Alex Oliveira Barradas; Barros, Allan Kardec; Viegas, Isabelle Moraes Amorim; Silva, Luis Claudio O.; Marques, Edmar P.; Marques, Aldaléa Lopes Brandes

doi:10.1007/s00521-017-3087-4

Cited by 6 publications

(4 citation statements)

References 37 publications

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“…In each iteration, we leave a fold to test and train the model in the remaining k-1 folds [52]. Based on Lopes et al [53], Afzal et al [54], Song et al [50], and Chang et al [55], our dataset was randomly divided into five subsets (k = 5). Thus, from the 772 adolescents, 618 were randomly chosen to compose the training set and 154 were used as the test set (i.e., a proportion of 80% of the data for training and 20% for testing).…”

Section: Methodsmentioning

confidence: 99%

Development of a Computational Model to Predict Excess Body Fat in Adolescents through Low Cost Variables

Sousa

Santana

Lopes

et al. 2019

IJERPH

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Background: Excess body fat has been growing alarmingly among adolescents, especially in low income and middle income countries where access to health services is scarce. Currently, the main method for assessing overweight in adolescents is the body mass index, but its use is criticized for its low sensitivity and high specificity, which may lead to a late diagnosis of comorbidities associated with excess body fat, such as cardiovascular diseases. Thus, the aim of this study was to develop a computational model using linear regression to predict obesity in adolescents and compare it with commonly used anthropometric methods. To improve the performance of our model, we estimated the percentage of fat and then classified the nutritional status of these adolescents. Methods: The model was developed using easily measurable socio-demographic and clinical variables from a database of 772 adolescents of both genders, aged 10–19 years. The predictive performance was evaluated by the following metrics: accuracy, sensitivity, specificity, and area under ROC curve. The performance of the method was compared to the anthropometric parameters: body mass index and waist-to-height ratio. Results: Our model showed a high correlation (R = 0.80) with the body fat percentage value obtained through bioimpedance. In addition, regarding discrimination, our model obtained better results compared to BMI and WHtR: AUROC = 0.80, 0.64, and 0.55, respectively. It also presented a high sensitivity of 92% and low false negative rate (6%), while BMI and WHtR showed low sensitivity (27% and 9.9%) and a high false negative rate (65% and 53%), respectively. Conclusions: The computational model of this study obtained a better performance in the evaluation of excess body fat in adolescents, compared to the usual anthropometric indicators presenting itself as a low cost alternative for screening obesity in adolescents living in Brazilian regions where financial resources are scarce.

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Section: Methodsmentioning

confidence: 99%

Development of a Computational Model to Predict Excess Body Fat in Adolescents through Low Cost Variables

Sousa

Santana

Lopes

et al. 2019

IJERPH

Self Cite

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“…These quality parameters are established by standards in each country or region, such as the standards EN 14214 (Europe), ASTM D6751 (USA) and RANP 45/2014 (Brazil) [27].…”

Section: Case Studymentioning

confidence: 99%

“…This work aimed to obtain an alternative method with more accuracy when compared to other alternative methods [27].…”

mentioning

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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“…With the never ending increase in world fuel demand, biodiesel has emerged in recent decades as one of the main alternatives to petroleum diesel [1]. Biodiesel can be used in conventional diesel engines.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Biodiesel Production from Waste Cooking Oil Using Genetic Algorithm-Based Support Vector Machines

et al. 2018

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The ever increasing fuel demands and the limitations of oil reserves have motivated research of renewable and sustainable energy resources to replace, even partially, fossil fuels, which are having a serious environmental impact on global warming and climate change, excessive greenhouse emissions and deforestation. For this reason, an alternative, renewable and biodegradable combustible like biodiesel is necessary. For this purpose, waste cooking oil is a potential replacement for vegetable oils in the production of biodiesel. Direct transesterification of vegetable oils was undertaken to synthesize the biodiesel. Several variables controlled the process. The alkaline catalyst that is used, typically sodium hydroxide (NaOH) or potassium hydroxide (KOH), increases the solubility and speeds up the reaction. Therefore, the methodology that this study suggests for improving the biodiesel production is based on computing techniques for prediction and optimization of these process dimensions. The method builds and selects a group of regression models that predict several properties of biodiesel samples (viscosity turbidity, density, high heating value and yield) based on various attributes of the transesterification process (dosage of catalyst, molar ratio, mixing speed, mixing time, temperature, humidity and impurities). In order to develop it, a Box-Behnken type of Design of Experiment (DoE) was designed that considered the variables that were previously mentioned. Then, using this DoE, biodiesel production features were decided by conducting lab experiments to complete a dataset with real production properties. Subsequently, using this dataset, a group of regression models—linear regression and support vector machines (using linear kernel, polynomial kernel and radial basic function kernel)—were constructed to predict the studied properties of biodiesel and to obtain a better understanding of the process. Finally, several biodiesel optimization scenarios were reached through the application of genetic algorithms to the regression models obtained with greater precision. In this way, it was possible to identify the best combinations of variables, both independent and dependent. These scenarios were based mainly on a desire to improve the biodiesel yield by obtaining a higher heating value, while decreasing the viscosity, density and turbidity. These conditions were achieved when the dosage of catalyst was approximately 1 wt %.

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Attesting compliance of biodiesel quality using composition data and classification methods

Cited by 6 publications

References 37 publications

Development of a Computational Model to Predict Excess Body Fat in Adolescents through Low Cost Variables

Development of a Computational Model to Predict Excess Body Fat in Adolescents through Low Cost Variables

Applications of Artificial Neural Networks in Biofuels

Optimizing Biodiesel Production from Waste Cooking Oil Using Genetic Algorithm-Based Support Vector Machines

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