FORECASTING PETROL EVAPORATION WHILE STORING DURING TRANSITION TO A VIRTUAL CLOUD AUTOMATION SCHEME
In agriculture, the automation process requires significant investment in sensors, building software systems, and providing personnel for technical support of devices. In this case, the location of a number of objects in the field makes it difficult or impossible to automate them. The application of the concept of a virtually cloud-based automation system frees the automation object from participation in the processes of receiving, converting, transmitting and using information about it without installing sensors. This makes it possible to predict the loss of gasoline from evaporation during storage with the use of neural networks. The paper solved the problem of building the correct neural network architecture based on the graph of the interdependence of the parameters of the entire model of a fuel storage system in a tank. To calculate the monthly remaining fuel in the reservoir volume, a neural network with three inputs, 7 neurons in a hidden layer was selected. The hyperbolic tangent is selected as the function of neuron activation as the function of neuron activation. The accuracy of the prediction of the remaining fuel was 95%. A neural network model based on a virtual-cloud tank automation scheme was also obtained, which is capable of describing the process of gasoline evaporation from a horizontal cylindrical tank with high accuracy.