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Документ Automated methods of controlling the flow of syrup in the evaporation station with subsystems of decision support and forecasting(2022) Hrama, Mykhailo; Sidletskyi, VictorIntroduction. The purpose of the presented study is to substantiate the methods of regulating the consumption of syrup in the evaporation station with a forecasting subsystem, which will allow to predict the behavior of the system and the decision-making subsystem, which will reduce the influence of the human factor on the course of the evaporation process. Materials and methods. The work of the evaporation station with the subsystem of forecasting and decision support when regulating the consumption of syrup was researched. In the automation scheme for regulating the flow rate of syrup, induction flow meters are used as a sensor. Pneumatic saddle valves with a built-in throttle and an electro-pneumatic converter were used as actuators. Results and discussion. The use of neural sensors occurs only in certain specific cases of intelligent control of the evaporation process, there is no data comparing the use of intelligent regulators with classical ones, the possibility of combining the work of several types of intelligent regulators, as well as clear means of predicting their work and supporting decision-making. Therefore, in this paper, a decision-making subsystem has been justified, which made it possible to assess the priorities of user requests when using a human- machine interface. The highest priority was given to the request to display information on possible changes to the adjustment parameters of other control circuits. The forecasting method was also used to compare the methods of regulating the flow rate of syrup in the apparatus, which made it possible to predict the behavior of the system during the formation of the control action and display the finished forecast on the operator's screen and, thus, increase the efficiency of the evaporation station. Statistical data on the behavior of the contours of the automation system in different modes of operation using intelligent and classical regulators were collected, a model for predicting the operation of an evaporation station by the method of local tendency was built and a forecasting algorithm was developed. The accuracy of the obtained forecasting model is also evaluated. The accuracy of the forecasting model was 98% for the PID controller, 95% for the neural fuzzy regulator and 96% for the neural network. Conclusions. The model for predicting the operation of the evaporation station is characterized by high accuracy in general, but during the occurrence of oscillations in the transition process, there is an insignificant delay in predicting these fluctuations. The most important in the output of information by the decision-making subsystem is the function of displaying information about the possible changes to the parameters of regulation of other control circuits.Документ Intelligent automatic control of sugar factory evaporator operation using behavior prediction subsystem(2022) Hrama, Mykhailo; Sidletskyi, Victor; Elperin, IgorIntroduction. The aim of the presented research was to substantiate the intelligent automatic control of the sugar juice evaporation with the subsystem for behavior prediction, which allows to determine the behavior of the automatic system. Materials and methods. The operation of the evaporator unit with system behavior prediction to regulate the sugar juice level was investigated. Capacitive level gauges were used as a sensor in the automation scheme of sugar juice level control. Pneumatic seat valves with a built-in throttle and an electro¬pneumatic converter were used as actuators. Results and discussion. The use of neuro-fuzzy regulators occurs only in some specific cases of intelligent control of the evaporation process. There is no data comparing the use of intelligent regulators with classical ones and the possibility of combining several types of intelligent regulators, as well as clear means of predicting their work. Therefore, in the present study, a prediction method was used to compare methods to regulate the level of sugar juice in the evaporator. This made it possible to predict the behavior of the system during the formation of the control action and display the finished forecast on the operator's screen, which made it possible to increase the efficiency of the evaporative station. Statistical data on the behavior of the automation system contours in various operating modes were collected using intelligent and classical controllers, and a model was built to determine the operation of the evaporator using the local trend method and the modified algorithm of prediction. The advantage of this method is its easy and fast implementation, which does not require large economic and energy costs. The accuracy of the prediction model was 98% for the PID-controller, 95% for the fuzzy-controller and 96% for the neural network. The obtained model of the system prediction is stable because the absolute error does not change when dividing the time series into intervals. Conclusions. The proposed system of intelligent automated control of the evaporation of sugar juice with a modified prediction method based on local trends has an insignificant delay, while prediction is performed with high accuracy and stability.