Rocabruno-Valdes, C. I. published the artcileCorrosion rate prediction for metals in biodiesel using artificial neural networks, COA of Formula: C23H46O2, the main research area is biodiesel corrosion rate prediction artificial neural network.
The objective of this research was to develop a direct artificial neural network with the ability to predict a corrosion rate of metals in different biodiesel. Exptl. values were obtained by the electrochem. noise technique, EN, as well as, information reported in the literature. A backpropagation model was proposed with three layers; metal and biodiesel composition, blend biodiesel/diesel, total acid number (TAN), temperature and exposure time were considered as input variables in the model. The best fitting training data were acquired with 24:4:1, considering a Levenberg -Marquardt learning algorithm, a hyperbolic tangent and linear transfer functions in the hidden and output layer resp. Exptl. and simulated data were compared satisfactorily through the linear regression model with a correlation coefficient of 0.9885 and a mean square error, MSE, of 2.15 × 10-4 in the validation stage. Furthermore, the model agreed the requirements of the slope and the intercept statistical test with a 99% confidence. The obtained results indicated that the ANN model could be attractive as corrosion rate estimator.
Renewable Energy published new progress about Acid number. 929-77-1 belongs to class esters-buliding-blocks, name is Methyl docosanoate, and the molecular formula is C23H46O2, COA of Formula: C23H46O2.
Referemce:
Ester – Wikipedia,
Ester – an overview | ScienceDirect Topics