2. State of art: ANN, quality parameters and biofuels

In this section, some papers published in scientific journals, which discuss applications of ANNs to the quality of fuel ethanol (pure or blends) and biodiesel, were selected and discussed. Articles were extracted from the Web of Science database. Table 1 groups different articles by type of biofuel (ethanol or biodiesel).

In the first article of Table 1, Najafi et al., in the paper named "Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network", proposed an experimental analysis of the performance and pollutant emissions of a four-stroke SI engine operating with mixtures of ethanol and gasoline (0, 5, 10, 15 and 20%), with the aid of ANNs [18]. Analyzes of the fuel ethanol quality parameters were performed based on the standards of the American Society for Testing and Materials (ASTM). The authors showed that blends with ethanol and gasoline provided an increase in engine power and torque output. It was also found that for ethanol blends, specific brake fuel consumption decreases while thermal brake efficiency and volumetric efficiency increased [18].

Concerning to the use of ANNs, the work of Najafi et al. used the backpropagation algorithm and multilayer perceptron (MLP) architecture for non-linear mapping between the inputs (gasoline-ethanol mixtures and engine speed) and the output parameters (engine performance and exhaust emissions). The evaluation of the results was based on three criteria: correlation


Table 1. ANN works applied to biofuel quality.

coefficient (r), root mean squared error (RMSE) and mean relative error (MRE). Thus, the work proves the feasibility of using the ANN approach to predict motor performance (brake power, torque output, brake thermal efficiency, volumetric efficiency, brake specific fuel consumption and equivalence fuel-air ratio) and the emissions (CO, CO2, HC and NOx) [18].

In 2012, the work titled "Ultrasonic determination of water concentration in ethanol fuel using artificial neural networks", published by Liu and Koc, it was determined the concentration of water in ethanol by measurements of ultrasonic velocity and liquid temperature [19]. The aim of the research is to propose an alternative method to contribute to the inspection against the adulteration of fuels, which impairs the vehicle performance and can cause damages to the engine [19].

In the development of an alternative method, the authors Liu and Koc used an ANN based on the MLP architecture. A database was elaborated with 651 samples for the training and validation steps of ANNs. The activation functions, varied for each hidden layer, were the functions logistic sigmoid (logsig), tangent sigmoid (tansig) and linear (purelin), and the results were based on the mean square error (MSE) and on the determination coefficient (R<sup>2</sup> ). Thus, the research concluded that the results obtained by ANNs were far better when compared with other models [19].

In the paper "Prediction of ethanol concentration in biofuel production using artificial neural networks", the authors Ahmadian-Moghadam et al. carried out, in 2013, an economic bioprocess to supply ethanol from sugar cane molasses. That research aims to contribute to the reduction of biofuel production prices and to have it as a more competitive resource in the market [20].

Ahmadian-Moghadam et al. applied ANNs to estimate the concentration of ethanol based on the sugar concentration and live and dead yeast cells. To do so, a database with 61 samples was divided as follows: 60% for training, 15% for validation and 25% for test [20]. The performance of ANN models was evaluated by the mean absolute deviation (MAD), mean absolute percentage error (MAPE) and MSE. Authors concluded that the results showed the viability of the application of the ANN model to determine the final ethanol concentration in the biofuels production process in a large scale [20].

Bendu et al. pointed out, in the paper "Application of GRNN for the prediction of performance and exhaust emissions in HCCI engine using ethanol" published in 2016, the importance of the evaluation of performance and emission parameters of an ethanol-fueled homogeneous charge compression ignition (HCCI) engine. In addition, the authors identified the nature of the parameters as a non-linear problem, which indicated the need for more robust tools [21].

For this purpose, Bendu et al. used a generalized regression neural network (GRNN) consisting of four layers (input layer, radial layer, regression layer and output layer). The input parameters were the charge temperature and the engine load, while the performance and emission values were set as output parameters.

The engine performance parameters were brake thermal efficiency (BTE), exhaust gas temperature (EGT) and the exhaust emission parameters were NO, CO, smoke and unburned hydrocarbon emission (UHC). Summing up, the authors showed the viability of the method and pointed out that the GRNN model can also be used for the control and testing of the HCCI engine [21].

coefficient (r), root mean squared error (RMSE) and mean relative error (MRE). Thus, the work proves the feasibility of using the ANN approach to predict motor performance (brake power, torque output, brake thermal efficiency, volumetric efficiency, brake specific fuel consumption

Biofuel Title of publication Year

Application of GRNN for the prediction of performance and exhaust emissions in HCCI engine using

Artificial neural network prediction of diesel engine performance and emission fueled with diesel-

Biodiesel Application of artificial neural network to predict properties of diesel-biodiesel blends 2010

Application of artificial neural networks to predict viscosity, iodine value and induction period of

Ultrasonic determination of water concentration in ethanol fuel using artificial neural networks 2012 Prediction of ethanol concentration in biofuel production using artificial neural networks 2013

Inference of the biodiesel cetane number by multivariate techniques 2013 Neural network prediction of biodiesel kinematic viscosity at 313 K 2014

Attesting compliance of biodiesel quality using composition data and classification methods 2017

Etanol Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using

In 2012, the work titled "Ultrasonic determination of water concentration in ethanol fuel using artificial neural networks", published by Liu and Koc, it was determined the concentration of water in ethanol by measurements of ultrasonic velocity and liquid temperature [19]. The aim of the research is to propose an alternative method to contribute to the inspection against the adulteration of fuels, which impairs the vehicle performance and can cause damages to the

In the development of an alternative method, the authors Liu and Koc used an ANN based on the MLP architecture. A database was elaborated with 651 samples for the training and validation steps of ANNs. The activation functions, varied for each hidden layer, were the functions logistic sigmoid (logsig), tangent sigmoid (tansig) and linear (purelin), and the results were based on the mean square error (MSE) and on the determination coefficient (R<sup>2</sup>

Thus, the research concluded that the results obtained by ANNs were far better when com-

In the paper "Prediction of ethanol concentration in biofuel production using artificial neural networks", the authors Ahmadian-Moghadam et al. carried out, in 2013, an economic bioprocess to supply ethanol from sugar cane molasses. That research aims to contribute to the reduction of biofuel production prices and to have it as a more competitive resource in the market [20].

Ahmadian-Moghadam et al. applied ANNs to estimate the concentration of ethanol based on the sugar concentration and live and dead yeast cells. To do so, a database with 61 samples

).

2009

2016

2017

2015

and equivalence fuel-air ratio) and the emissions (CO, CO2, HC and NOx) [18].

engine [19].

pared with other models [19].

artificial neural network

184 Advanced Applications for Artificial Neural Networks

Table 1. ANN works applied to biofuel quality.

kerosene-ethanol blends: a Fuzzy-based optimization

biodiesel focused on the study of oxidative stability

ethanol

In 2017, Bhowmik et al. performed a study titled "Artificial neural network prediction of diesel engine performance and emission fueled with diesel–kerosene–ethanol blends: a fuzzy-based optimization" to explore the impact on performance and emission characteristics of a single cylinder indirect injection (IDI) engine fueled with blends of diesel and kerosene [22]. In this research, the authors indicated that the addition of ethanol to the mixtures of diesel and kerosene significantly improved the BTE, brake specific energy consumption (BSEC) and the emissions of NOX, CO and total hydrocarbon (THC) of the engine [22].

Therefore, Bhowmik et al. built an ANN model to map the inputs (load, kerosene volume percentage and ethanol volume percentage) with respect to the outputs (BTE, BSEC, NOX, THC and CO). The best topology found had a structure with five hidden neurons and presented satisfactory results for the problem addressed. The criteria for evaluation of the developed ANNs were based on MSE, MAPE and r [22].

In 2010, Kumar and Bansal published the paper "Application of artificial neural network to predict properties of diesel – biodiesel blends" whose aim was to evaluate tools for the determination of physical-chemical properties of diesel-biodiesel mixtures. Choosing an appropriate and efficient alternative method could help to avoid some overly time-consuming and costly experiments [23].

Also in the Kumar and Bansal paper, traditional linear regression (principle of least squares) and ANN were applied and compared. The ANNs optimization process was carried out by varying the architectures and training algorithms. The authors concluded that the best results were obtained by the ANN method [23].

In the work of Nadai et al., entitled "Inference of the biodiesel cetane number by multivariate techniques", a method consisting of successive application of principal components analysis (PCA), fuzzy clustering and ANN in a dataset composed by structural information from proton nuclear magnetic resonance (1 H NMR) of biodiesel fatty esters was implemented [24]. The aim of that work was to obtain the cetane number of different types of complex mixtures from data of pure substances (esters). The authors pointed out two main characteristics that affect the cetane number values: the number of carbon-carbon double bonds and the structure of the alcohol moiety in each fatty ester [24].

In 2014, with the research "Neural network prediction of biodiesel kinematic viscosity at 313 K" Meng et al. performed the prediction of the kinematic viscosity property of biodiesel by artificial neural networks. The authors used 105 samples of biodiesel collected from the literature and 19 types of fatty acid methyl esters (FAMEs) were set as inputs. The results obtained suggested ANNs as an option in predicting kinematic viscosity with a correlation coefficient of 0.9774 [25].

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].

Also in the work of Barradas Filho et al., the ANNs optimization occurred by varying the activation functions, the number of neurons in the hidden layers and the convergence methods. The evaluation criteria of the models were MSE, RMSE, MAPE and r and R<sup>2</sup> coefficients. After optimization, the ANN results were compared to other models and obtained the best performance [26].

In 2017, the work "Attesting compliance of biodiesel quality using composition data and classification methods" of Lopes et al. compared four classification methods (decision tree classifier, Knearest neighbors, support vector machine and artificial neural networks) to evaluate the compliance of biodiesel samples concerning some quality parameters. This work aimed to obtain an alternative method with more accuracy when compared to other alternative methods [27].
