Extraction method is still the most waste-free in oil recovery technologies, and in the case of low-soybean seeds, the most convenient. Therefore, with the impulse intensification of this process, it is possible not only to increase its efficiency, but also to achieve compactness of equipment, to reduce energy costs and to improve the quality of extraction oils. The methods of intensification of the extraction process from plant raw materials, which can be divided into mechanical, thermal, biochemical and electrophysical, are analyzed, generalized and classified. The hypothesis is confirmed according to which the intensification of the extraction process of oil with an increased content of tocopherols occurs due to the use of a pressure diffusion flux from the capillary-porous structure of plant raw materials under the action of a microwave field. It is proved that the proposed number of energy effects successfully correlates the effect of the pulsed microwave field on the mass-transfer rate when extracting oil from rapeseed and soybean seeds. Using the developed mathematical model of the extraction process with pulse intensification, it is possible to deduce the dependences of the mass transfer coefficient on the number of energy effects, the dependence of the mass transfer coefficient on the microwave power and other dependences of the dimensionless criterial complexes characterizing the investigated process with means of its intensification. A determining effect on the mass transfer coefficient microwave power is defined. Burdo (Bu) vaporization number, showing the ratio of the microwave power and power needed to convert the liquid into vapor, corrects and coordinates the experimental data with an error of 8-16.5%.
The paper proposes using a fuzzy controller in telecommunication networks for improving the scheduling process. A structure of the fuzzy controller was developed. Linguistic variables, terms and membership functions for input and output values were defined. A rules base was developed. An Adaptive Neuro-fuzzy Inference System (ANFIS) on the base of the fuzzy-controller was developed. A genetic algorithm to improve the rule base was proposed. The operation of ANFIS was simulated and trained.