The uneven load of the electric drive of sucker-rod pumping units causes an increase in the consumption of reactive power, which requires compensation. This article discusses the issue of calculating the processes in sucker-rod pumping units in the case of individual compensation of the reactive power. Mathematical models for calculating the process of changing the capacity of cosine capacitors for reactive power compensation in the starting and steady-state modes of the electric drive of the sucker-rod pumping unit, which are characterized by the periodically varying load and moment of inertia, have been developed. The calculation is based on a mathematical model of the induction motor that takes into account the saturation of the magnetic path and the skin effect in the bars of the deep-bar rotor. The created mathematical model can be used to regulate the capacitance of the capacitors connected to the induction motor in order to reduce the reactive power consumption.

This study aims to identify an effective neural network architecture for the task of semantic segmentation of the surface of beer wort at the stage of primary fermentation, using deep learning methodologies. Four contemporary architectures were evaluated and contrasted. The following networks are presented in both baseline and optimized forms: U-Net, DeepLabV3+, Fast-SCNN, and SegNet. The models were trained on a dataset of images depicting real beer surfaces at the primary fermentation stage. This was followed by the validation of the models using key metrics, including pixel classification accuracy, Mean Intersection over Union (mIoU), Dice Coefficient, inference time per image, and Graphics Processing Unit (GPU) resource utilization. Results indicate that the optimized U-Net achieved the optimal balance between performance and efficiency, attaining a validation accuracy of 88.85%, mIoU of 76.72%, and a Dice score of 86.71%. With an inference time of 49.5 milliseconds per image, coupled with minimal GPU utilization (18%), the model proves suitable for real-time deployment in production environments. Conversely, complex architectures, such as DeepLabV3+, did not yield the anticipated benefits, thereby underscoring the viability of utilizing compact models for highly specialized industrial tasks. This study establishes a novel quantitative metric for the assessment of fermentation. This is based on the characteristics of the foam surface and thus offers an objective alternative to traditional subjective inspections. The findings emphasize the potential of adapting optimized deep learning architectures to quality control tasks within the food industry, particularly in the brewing sector, and they pave the way for further integration into automated computer vision systems.

МЕТОД РОЗРАХУНКУ МЕХАНІЧНИХ ХАРАКТЕРИСТИК АСИНХРОННИХ ДВИГУНІВ З КОРОТКОЗАМКНЕНИМ РОТОРОМ

Wydawnictwo SIGMA-NOT wydaje czasopisma fachowe informuj