1. Introduction

In the last decades, artificial neural networks (ANNs) have undergone several transformations and improvements, which allowed their application in different areas of knowledge. Such an approach appreciated by the academic community, ANNs are distributed parallel systems, also known as connectionist systems, inspired by the biology of the human brain [1].

In this context, ANNs are simplified models of the human brain that consist of a large number of processing units (neurons) connected to each other. These units usually calculate mathematical

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functions (non-linear and/or linear) and form a large network of communication, which allows solving high complexity problems [2].

The architectures that implement the connectionist approach are usually conditioned by a training and learning process rather than being explicitly programmed. In this way, the choice of the architecture has an extremely important character for the solution of certain problems [3].

Among the different tasks appropriate to the application of ANNs are:


The variety of ANN applications provide a stimulating scenario for contributions in the field of biofuels, which are defined as renewable fuels derived from biomass that can be used in internal combustion engines or for other types of energy generation. The aim of using biofuels is to reduce external dependence on oil (partial or total replacement of fossil fuels), minimize environmental impacts and develop agricultural production.

The main liquid biofuels produced in the world are ethanol and biodiesel. Ethanol, also known as ethyl alcohol, is a chemical with the molecular formula C2H6O, produced by the fermentation of sugars. Under normal conditions, it is a colorless and volatile liquid with an ethereal odor and pungent taste, miscible in water and in different organic solvents.

According to the U.S. Energy Information Administration (EIA), in 2014, the largest ethanol producers on the worldwide are the United States and Brazil [4]. In the USA, the main raw material used for the production of ethanol is corn, while sugarcane is more prominent in Brazil.

Biodiesel is a fuel composed of alkyl esters of long-chain carboxylic acids, produced by the transesterification and/or esterification of fatty material, fats of vegetable or animal origin, with a short-chain alcohol, such as methanol or ethanol [5]. For the production of biodiesel, a variety of raw materials has been used, including edible and non-edible oils, crude oils, fried oils and animal fats. The main raw materials used are soy, palm, cotton, rapeseed, jatropha and sunflower oils and bovine tallow, although it is possible to use all vegetable oils classified as fixed oils or triglycerides, and animal fats [6–8].

Unlike fuel ethanol, the EIA shows that most of biodiesel production in 2014 is not restricted to America alone but also to the continents of Europe, Asia and Oceania [4].

In general, the two biofuels (ethanol and biodiesel) have attracted international attention and, consequently, have had their production increased in comparison to previous years. Some topics studied and related to both ethanol and biodiesel are:


functions (non-linear and/or linear) and form a large network of communication, which allows

The architectures that implement the connectionist approach are usually conditioned by a training and learning process rather than being explicitly programmed. In this way, the choice of the architecture has an extremely important character for the solution of certain

• Classification and pattern recognition: process by which a received signal (input) is

• Categorization: discovery of well-defined categories or classes in the input data. Unlike

• Prediction: estimation of a numerical response based on input values, also called calibra-

• Noise filtering: extraction of information about a certain response of interest from a noisy

The variety of ANN applications provide a stimulating scenario for contributions in the field of biofuels, which are defined as renewable fuels derived from biomass that can be used in internal combustion engines or for other types of energy generation. The aim of using biofuels is to reduce external dependence on oil (partial or total replacement of fossil fuels), minimize

The main liquid biofuels produced in the world are ethanol and biodiesel. Ethanol, also known as ethyl alcohol, is a chemical with the molecular formula C2H6O, produced by the fermentation of sugars. Under normal conditions, it is a colorless and volatile liquid with an ethereal

According to the U.S. Energy Information Administration (EIA), in 2014, the largest ethanol producers on the worldwide are the United States and Brazil [4]. In the USA, the main raw material used for the production of ethanol is corn, while sugarcane is more prominent in

Biodiesel is a fuel composed of alkyl esters of long-chain carboxylic acids, produced by the transesterification and/or esterification of fatty material, fats of vegetable or animal origin, with a short-chain alcohol, such as methanol or ethanol [5]. For the production of biodiesel, a variety of raw materials has been used, including edible and non-edible oils, crude oils, fried oils and animal fats. The main raw materials used are soy, palm, cotton, rapeseed, jatropha and sunflower oils and bovine tallow, although it is possible to use all vegetable oils classified as

Unlike fuel ethanol, the EIA shows that most of biodiesel production in 2014 is not restricted to

• Optimization: characterized by the minimization or maximization of a cost function;

Among the different tasks appropriate to the application of ANNs are:

assigned to a particular group or category;

classification, classes are not previously known;

environmental impacts and develop agricultural production.

fixed oils or triglycerides, and animal fats [6–8].

odor and pungent taste, miscible in water and in different organic solvents.

America alone but also to the continents of Europe, Asia and Oceania [4].

solving high complexity problems [2].

182 Advanced Applications for Artificial Neural Networks

problems [3].

tion;

dataset.

Brazil.

• Forms of storage, transportation and distribution of biofuels.

However, despite the diversity of topics and works published in the scientific literature, the present research is targeted to the study of the application of ANNs in the quality control of biofuels [9–15]. 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].

It is important to mention that the quality of biofuels is ensured by technical resolutions or standards established by each country which set limit values for contaminants and other parameters [17].
