Open AccessApplication of feed forward neural network model to predict the limiting current of tin magneto electrodeposition
Author(s) -
Sudibyo,
N. Aziz,
Anggi Hadi Wijaya,
R S Fathona
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1011/1/012005
Subject(s) - tin , limiting current , artificial neural network , limiting , nonlinear system , current (fluid) , feedforward neural network , control theory (sociology) , algorithm , computer science , materials science , electrode , biological system , chemistry , physics , thermodynamics , engineering , artificial intelligence , metallurgy , electrochemistry , mechanical engineering , control (management) , quantum mechanics , biology
Predicting the value of Tin Magneto electrodeposition (MED) is very important since the optimum mass transport occurred at the limiting current. The MED limiting current able to detect using electroanalytical chemistry, but this method is expensive; it needs some method, which able to predict the limiting current of tin MED. However, predicting the limiting current under magnetic field effect is more complicated due to the highly nonlinear characteristic and complicated of its multiple inputs single-output (MISO) system. The nonlinear model that able to predict the limiting current of tin MED is Artificial Neural Networks (ANNs). One of the ANNs which able to simulate the Multiple-Input-Single-Output (MISO) model is the Feed Forward Neural Network (FFNN). In this work, MISO FFNN will model a matrix data set with six variable inputs and one output. The data was obtained from the results of the experiments using electroanalytical chemistry. The output of this model is the limiting current of tin MED, meanwhile, the inputs are by the concentration of tin (Sn 2+ ) in the electrolyte (C), viscosity(v), diffusion coefficient (D), area of the electrode (A), the number of electroactive species (n) and magnetic field strength (B). To get the best model, the performance of FFNN was tested with three variations of the algorithm (Levenberg-Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient) and ten variations of the number of neurons (10, 15, 20, 25, 30, 35, 40, 45 and 50). The best model obtained for this MISO FFNN model is which uses the Levenberg-Marquardt algorithm and the highest number of neurons (50 neurons).