Corrosion rate prediction for metals in biodiesel using artificial neural networks

Rocabruno-Valdés, C.I.; González-Rodrı́guez, J.G.; Díaz-Blanco, Y.; Juantorena, A. U.; Muñoz-Ledo, J.A.; El-Hamzaoui, Y.; Hernández, J.A.

doi:10.1016/j.renene.2019.03.065

Cited by 40 publications

(10 citation statements)

References 44 publications

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“…symmetricmean absolute percentage error (SMAPE), Nash—Sutcliffe efficiencycoefficient (NSE) along with RMSE, MAE & R squared values. The model was also analyzed with regression & slope-intercept tests as proposed by Rocabruno-Valdes et al (2019) [ 23 ].…”

Section: Methodsmentioning

confidence: 99%

Developing a computational toolbased on an artificial neural network for predicting and optimizing propolis oil, an important natural product for drug discovery

et al. 2023

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Propolis is a promising natural product that has been extensively researched and studied for its potential health and medical benefits. The lack of requisite high oil-containing propolis and existing variation in the quality and quantity of essential oil within agro-climatic regions pose a problem in the commercialization of essential oil. As a result, the current study was carried out to optimize and estimate the essential oil yield of propolis. The essential oil data of 62 propolis samples from ten agro-climatic areas of Odisha, as well as an investigation of their soil and environmental parameters, were used to construct an artificial neural network (ANN) based prediction model. The influential predictors were determined using Garson’s algorithm. To understand how the variables interact and to determine the optimum value of each variable for the greatest response, the response surface curves were plotted. The results revealed that the most suited model was multilayer-feed-forward neural networks with an R2 value of 0.93. According to the model, altitude was found to have a very strong influence on response, followed by phosphorous & maximum average temperature. This research shows that using an ANN-based prediction model with a response surface methodology technique to estimate oil yield at a new site and maximize propolis oil yield at a specific site by adjusting variable parameters is a viable commercial option. To our knowledge, this is the first report on the development of a model to optimize and estimate the essential oil yield of propolis.

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Section: Methodsmentioning

confidence: 99%

Developing a computational toolbased on an artificial neural network for predicting and optimizing propolis oil, an important natural product for drug discovery

et al. 2023

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“…Uno de los principales parámetros que influyen en la corrosión metálica es el porcentaje de agua, la cual se establece en la norma ASTM D6751 que debe encontrarse en una cantidad menor a los 500 mg/L (ppm). Un contenido mayor al indicado provoca daños en los componentes del generador de energía debido a la corrosión, por lo que se debe medir de manera rigurosa [42][43][44]. La presencia de otras impurezas como metanol, glicerol libre, ácido graso libre, residuos de catalizador (Na y K) debido a la conversión incompleta o purificación inadecuada también pueden resultar en corrosión metálica [36,37].…”

Section: Corrosión Y Deterioro De Materiales Metálicos Usados En Moto...unclassified

Revisión de las técnicas de caracterización de materiales para estudiar la corrosión en motores a biodiésel

Corrales-Brenes

Quesada-Quirós

Guerrero-Piña

et al. 2022

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El biodiésel es una alternativa biodegradable que permite reemplazar parte del consumo del petrodiésel. Este artículo analiza el efecto corrosivo del biodiésel en materiales metálicos en motores mediante técnicas de caracterización, incluyendo espectroscopia infrarroja por transformada de Fourier (FTIR), espectroscopia de Raman, microscopía de barrido electrónico (SEM) y difracción de rayos X (XRD). Mediante un estudio del estado del arte de dichas técnicas se reúnen recomendaciones para evaluar el desempeño de metales en motores que funcionan a base de biodiésel. Además, se abarcan los mecanismos de corrosión a los que los componentes de los motores están sujetos para mejorar sus procesos de diseño. El estudio finalmente se enfoca en la corrosión por picadura para identificar estrategias viables para mitigar su efecto en este tipo de motores y mejorar sus características de desempeño, para lo cual la ciencia de materiales se demuestra como una herramienta fundamental. El principal resultado de esta revisión fue la identificación de la corrosión por picadura y los compuestos formados como principal mecanismo de falla en motores de biodiesel.

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“…• the use of additives that reduce the activity of fuel methanols [38]; • reduced wear of precision pairs of the fuel system [39]; and • periodic flushing of the fuel system [40,41].…”

Section: Introductionmentioning

confidence: 99%

Modeling of Diesel Engine Fuel Systems Reliability When Operating on Biofuels

et al. 2022

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One of the main trends in the development of modern engine building is the use of biodiesel fuel, which can lead to a decrease in the reliability of engines that are not adapted to it. However, at present there is no general method for determining the reliability of fuel systems of internal combustion engines. In this paper, a reliability model of engine fuel systems when operating on biofuel has been developed. Comprehensive indicators for assessing the reliability of diesel engine fuel systems are the coefficient of readiness and technical use. The availability factor of the fuel system when operating on biodiesel fuel without the replacement of structural materials was 0.66, while with the replacement it was 0.71, and the coefficient of technical utilization without replacement of materials was 0.36, and with the replacement of 0.4. Recommendations are given to improve the reliability of the engine fuel system components. The resulting model allows for complex comparisons of the effectiveness of various ways to improve the reliability of engines running on biodiesel fuel.

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Corrosion rate prediction for metals in biodiesel using artificial neural networks

Cited by 40 publications

References 44 publications

Developing a computational toolbased on an artificial neural network for predicting and optimizing propolis oil, an important natural product for drug discovery

Developing a computational toolbased on an artificial neural network for predicting and optimizing propolis oil, an important natural product for drug discovery

Revisión de las técnicas de caracterización de materiales para estudiar la corrosión en motores a biodiésel

Modeling of Diesel Engine Fuel Systems Reliability When Operating on Biofuels

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