Comparison Between PID and Fuzzy Regulator for Control Evaporator Plants

Hrama, Mykhailo; Sidletskyi, Viktor; Ельперін, Ігор Володимирович

doi:10.1109/elnano.2019.8783428

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…Sugar juice contains glucose (C6H12O6); the aforementioned factors cause a change in its properties. Once the glucose temperature reaches the mark of 160 ° C and it remains unchanged for a long time, there is a splitting off of one of two molecules of water, which is glucose anhydride (C6H10O5) [5]. Creation of crystallized sugar out of it is impossible.…”

Section: Introductionmentioning

confidence: 99%

Justification of the neuro-fuzzy regulation in evaporator plant control system

Hrama¹,

Sidletskyi²,

Ельперін³

2019

Ukr. food j.

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Introduction. 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 neurofuzzy 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.

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Section: Introductionmentioning

confidence: 99%

Justification of the neuro-fuzzy regulation in evaporator plant control system

Hrama¹,

Sidletskyi²,

Ельперін³

2019

Ukr. food j.

Self Cite

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“…ких речовин неможливе [1]. Отже, для запобігання перетримки та перегрівання цукрового сиропу слід забезпечити найкращі параметри контролю якості.…”

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Порівняння Роботи Підсистем Прогнозування В Автоматизованій Системі Керування Випарним Апаратом

Hrama¹,

Sidletskyi²

2022

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The purpose of the presented research is to substantiate the methods of steam pressure regulation in the evaporator with the forecasting subsystem, which will allow predicting the behavior of the system and the study of the influence of the number of time intervals of the forecasting algorithm on the accuracy of the forecast of the evaporating plant operation. Development of a data exchange system between the level of the technological process and the level of production. The operation of the evaporator with the forecasting subsystem of steam pressure regulation is investigated. In the steam pressure control automation scheme, PC-28 pressure transducers are used as sensors. Pneumatic seat valves with a built-in throttle and an electro-pneumatic transducer are used as executive mechanisms. The use of neurofuzzy regulators occurs only in some specific cases of intelligent control of the evaporation process, there are no data comparing the use of intelligent regulators with classical ones, the possibility of combining the operation of several types of intelligent regulators, as well as clear means of predicting their operation. Therefore, in this work, the forecasting method was used to compare the methods of regulating the steam pressure in the apparatus, which made it possible to predict the behavior of the system during the formation of the control action and to display the ready forecast on the operatorʼs screen and, thus, to increase the efficiency of the evaporation station. Statistical data on the behavior of automation system circuits in different operating modes using intelligent and classical regulators were collected, and a forecasting model of the operation of the evaporation plant was built using the local trend method, and the forecasting algorithm was modified. The advantage of this method is its easy and quick implementation, which does not require large economic and energy costs. A model for forecasting the operation of the evaporation plant was built using the local trend method and a forecasting algorithm was developed. The accuracy of the obtained prediction model was also evaluated. The accuracy of the prediction model was 97 % for the PID controller, 97 % for the neurofuzzy controller, and 96,5 % for the neural network when using 9 intervals, which is higher than the accuracy when using 6 intervals. The proposed model for predicting the operation of the evaporation plant is characterized by high accuracy as a whole, but during the occurrence of fluctuations in the transient process, there is an insignificant delay in forecasting these fluctuations. The accuracy of this model directly depends on the increase in the number of time intervals during the development of the forecasting algorithm.

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