Статті
Постійне посилання на розділhttps://dspace.nuft.edu.ua/handle/123456789/7372
Переглянути
2 результатів
Результати пошуку
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.Документ Justification of the neuro-fuzzy regulation in evaporator plant control system(2019) Hrama, Mykhailo; Sidletskyi, Victor; Elperin, IgorIntroduction. The purpose of the study is the determining the use of which type of regulation will achieve the best indicators of quality control for the regulation of the evaporator plant. Materials and methods. The system of management of a five-body evaporator plant of a sugar refinery was researched. The method of synthesis of modal control was used for evaluating the results of the research. Results and discussion. A comparison between FID and fuzzy regulator was made. Regulation of such responsible parameters as levels of concentrated juice in evaporator plant bodies, which directly affect the quality and value of manufactured products, was implemented. First, in the space of states in Matlab environment, a mathematical model was developed and the results obtained regarding the variation of the problem with respect to the initial conditions and perturbations were obtained. Due to them one can conclude that the time of the transition process is within the range from 0.8 to 1.2 seconds. However, the deviation levels in the evaporator plant bodies are too high. Secondly, a mathematical model with a PID-regulator was developed and transition processes for control schemes across all control channels were obtained. In this case, the time of transition processes is within the range of 60 seconds along the channel X1 to 145 seconds along the channel X2, but this led to a significant decrease in the deviation of levels in the bodies. Thirdly, a mathematical model with a fuzzy regulator is developed and transition processes for control schemes in all control channels were obtained. In this case, the time of transition processes is within the range from 50 seconds along the channel X1 to 110 seconds along the channel X2, which is the better result if compared to the PID regulator. Compared with the previous study, the levels in the bodies also significantly decreased. Therefore, the use of neuro- fuzzy regulation leads to an increase in qualitative parameters of the process compared with the system with PID-regulators. Conclusions. The scientific substantiation of the feasibility of using neuro-fuzzy regulation during the implementation of optimal control systems is the novelty of the research results.