IMPROVEMENT OF CAR WASH CONTROL USING NEURAL NETWORK TECHNOLOGIES

In recent years, neural network technologies and models have been used in various industries, at agroindustrial enterprises and in car service. In order to improve the operation and management of portal car wash process at agro-industrial enterprises, the authors proposed neural network technologies. When constructing a car wash mathematical model based on neural network, the following output parameters were selected: geometrical dimensions of a vehicle and its model, as well as the dirtiness of the vehicle and its type. Water and detergent consumption, washing time and washing cycle are dependent on the output parameters. It is found that a neural network algorithm recognizing the dirtiness level and vehicle type can be applied to control the water supply in a portal wash, which should reduce water consumption and generally increase the energy efficiency of the wash. The parameters of a portal car wash are substantiated. Neural network algorithm techniques in a car wash are identified. The authors created an algorithm determining vehicle class and dirtiness. The study proves the effectiveness of automatic portal car washing for vehicle cleaning with high pressure water jetting, as well as the need to improve the portal wash control, based on new control algorithms to save water, electricity and time consumption when cleaning vehicles of different makes.