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The purpose of this paper is to offer a simulation method for predicting the impedance of machine windings at higher frequencies.

A transmission‐line model (TLM) is developed based on parameters calculated on the basis of electroquasistatic and magnetoquasistatic finite‐element (FE) model of the winding cross‐section.

The FE formulations for the low‐ and high‐frequency limits give acceptable results for the respective frequency ranges. An eddy‐current formulation is only accurate on a broader region when the FE mesh is sufficiently fine to resolve the skin depth.

The paper is restricted to frequency‐domain simulations.

The results of the paper improve the understanding of higher‐frequency parasitic effects in electrical drives with long windings.

The paper shows the limitations of the FE methods used for determining the parameters of the TLMs and remedies to avoid these.

The purpose of this paper is to present and apply a parasitic extraction approach for the calculation of DC busbar inductances.

A computational approach based on the finite integration technique and computed magnetic energy is developed to extract parasitic inductances. The finite integration analysis is conducted via the magnetoquasistatic solver of CST EM Studio^® capturing the 3D geometrical effects of the design, as well as the skin and proximity effects.

The method is applied successfully to evaluate the leakage inductances of two printed circuit boards structures; a backplane sample for the verification purpose and a real DC bus employed in a three‐phase pulse width modulation inverter.

The paper demonstrates that the method calculates the loop inductances accurately. It does not, however, verify the used technique to split loop inductances into partial inductances.

The extraction method is easy‐to‐use and able to handle complex geometries within acceptable computation time and accuracy.

The paper introduces a way to compute the parasitic inductances from the results of a numerical electromagnetic field solver.

Modeling Wastewater Treatment Plant (WWTP) constitutes an important tool for controlling the operation of the process and for predicting its performance with substantial influent fluctuations. The purpose of this paper is to apply an artificial neural network (ANN) approach with a feed-forward back-propagation in order to predict the ten-month performance of Touggourt WWTP in terms of effluent Chemical Oxygen Demand (CODeff).

The influent variables such as (pHinf), temperature (TEinf), suspended solid (SSinf), Kjeldahl Nitrogen (KNinf), biochemical oxygen demand (BODinf) and chemical oxygen demand (CODinf) were used as input variables of neural networks. To determine the appropriate architecture of the neural network models, several steps of training were conducted, namely the validation and testing of the models by varying the number of neurons and activation functions in the hidden layer, the activation function in output layer as well as the learning algorithms.

The better results were achieved with an architecture network [6-50-1], hyperbolic tangent sigmoid activation functions at hidden layer, linear activation functions at output layer and a Levenberg – Marquardt method as learning algorithm. The results showed that the ANN model could predict the experimental results with high correlation coefficient 0.89, 0.96 and 0.87 during learning, validation and testing phases, respectively. The overall results indicated that the ANN modeling approach can provide an effective tool for simulating, controlling and predicting the performance of WWTP.

This work is the first of its kind in this region due to the remarkable development in terms of population and agricultural activity in the region, which drove to the increase of water pollutants, so it is necessary to use the modern technologies to modeling and controlling of WWTP.