Genetic algorithm based artificial neural network and partial least squares regression methods to predict of breakdown voltage for transformer oils samples in power industry using ATR-FTIR spectroscopy.

The current study proposes a novel analytical method for calculating the breakdown voltage (BV) of transformer oil samples considered as a significant method to assess the safe operation of power industry. Transformer oil samples can be analyzed using the Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy combined with multivariate calibration methods. The partial least squares regression (PLSR) back propagation-artificial neural network (BP-ANN) methods and a genetic algorithm (GA) for variable selection are used to predict and assess breakdown voltage in transformer oil samples from various Iranian transformer oils.

Determination of acidity in metal incorporated zeolites by infrared spectrometry using artificial neural network as chemometric approach.

The NH-TPD analysis is a costly and tedious method to determine zeolites acidity. Thus, to do so, FTIR spectroscopy was quantitatively used as a fast and cost-effectively method. Back-propagation artificial neural network (BP-ANN) was used for the analysis of multivariate base on the characteristic absorbance of 11 zeolite samples after metal substitution in the ~3612 cm region.