Enhance protection of electronic appliances through multivariate modelling and optimization of ceramic core materials in varistor devices

Abstract

E-waste comprises discarded low quality protected electronic appliances that annually accumulate a million tons of hazardous materials in the environment.

Porosity Estimation of Mesoporous TiO2‐ZnO Nanocrystalline by Artificial Neural Network Modeling

“…The modeling of the average pore diameter was performed by NeuralPower software version 2.5 which was employed in several types of research [45]. To model the experimentations, D-glucose concentration, PEG concentration, Zn-Ti concentration, and calcination temperature were nominated as the inputs (objective variables) whereas D p (nm) was selected as the output (reliant variable).…”

Scheduling the blended solution as industrial CO2 absorber in separation process by back-propagation artificial neural networks

“…The calculation was repeated 10 times for each node to avoid the random correlation due to the random initialization of the weights [43]. Among the repeated calculation, the structure with minimum RMSE was selected as architecture.…”

The implementation of artificial neural networks for the multivariable optimization of mesoporous NiO nanocrystalline: biodiesel application

“…Therefore, well-known multi-variate semi-empirical approaches such as response surface methodology (RSM) and articial neural networks (ANNs) can be applied to optimize the possible relative condition. [6][7][8][9] The RSM projects the related conditions, ts the detected practical outcomes of the executed model at that point, and then proposes the most suitable model for further authentication. In this method, the conrmed model is typically applied to gain the maximum yield of the nal product.…”

“…21,22 Furthermore, ANN as a non-linear statistical analysis technique has been vastly applied in various nanotechnology applications. 6,[23][24][25][26][27] Generally, it is considered as a promising simulation technique, which hugely diminish the rate of fabrication by predicting the output results with a short level of error in the prediction.…”