Verbally defined processes controlled by fuzzy controllers with input/output parameters represented by set of precise terms

Kayashev, A. I.; Muravyova, E. A.; Sharipov, M. I.; Emekeev, Alexander; Sagdatullin, Artur

doi:10.1109/meacs.2014.6986847

Cited by 11 publications

(9 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Feed-forward backprop is one of the simple types of neural networks. This network is widely used for machine learning due to an increase in the number of perseptrons, however, the reverse side of such ability -increased production power is required [1].…”

Section: Choice Of Neural Network Type and Architecturementioning

confidence: 99%

“…Cascade-forward backprop (cascade neural network) is similar to the feed-forward backprop network, differs only in that the input data is supplied to each subsequent layer directly, the outputs of previous layers are also supplied to subsequent ones. This network is widely used in the field of optimization, however, when using a large number of neurons (more than 50) and layers (more than 10), it requires a large productive power [1].…”

Section: Choice Of Neural Network Type and Architecturementioning

confidence: 99%

“…Adjusting the parameters of the controller manually is a rather complex process, since the ratio between the coefficients of the controller plays a large role, as well as the values of the parameters themselves. With the help of neural networks, it is possible to create a controller that will function on the basis of real process parameters and will be applied to the already existing PID controller, adjusting its coefficients to obtain the established parameter [1].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Development of neural network controller for control of debutanization process in rectification column of gas-fractionating plant

Muravyova

Sharipov

Zubaydullina

2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

In process systems, stabilizing parameters and bringing the process to technological mode is a rather difficult task. Control and control of parameters in manual mode does not produce effective results, and for this reason the introduction of an improved control system is required to eliminate human factor errors. Currently, the best efficiency is shown by neural networks, which are used for an advanced control and prediction system. The purpose of the study: in this project, a neural network controller is being developed to control the debutanization process of the gas fractionation plant in the Matlab Simulink environment. Cascade neural networks consisting of a certain number of layers and neurons are used to develop the project. During the work, a cascade neural network was selected, in which the parameters of the technological system were trained.

show abstract

Section: Choice Of Neural Network Type and Architecturementioning

confidence: 99%

Section: Choice Of Neural Network Type and Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Development of neural network controller for control of debutanization process in rectification column of gas-fractionating plant

Muravyova

Sharipov

Zubaydullina

2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…The parametric uncertainty of an asynchronous motor is a consequence of a change in its temperature and dynamic load. Existing adaptive controllers may be suitable for those mechanisms whose parameters either remain constant or change very slowly [9][10]. Therefore, the main objective of this work is to develop an adaptive neural network control system for the coordinates of an asynchronous motor.…”

Section: Problem Statement and Main Taskmentioning

confidence: 99%

Improving the energy efficiency of oil production using identification and prediction of operating modes of production wells based on data analysis methods, machine learning and neural networks

Sagdatullin

2019

E3S Web Conf.

View full text Add to dashboard Cite

Currently, there is a need to improve the systems and control of pumping equipment in the oil and gas production and oil and gas transport industries. Therefore, an adaptive neural network control system for an electric drive of a production well was developed. The task of expanding the functional capabilities of asynchronous electric motors control of the oil and gas production system using the methods of neural networks is solved. We have developed software modules of the well drive control system based on the neural network, an identification system, and a scheme to adapt the control processes to changing load parameters, that is, to dynamic load, to implement the entire system for real-time control of the highspeed process. In this paper, based on a model of an identification block that includes a multilayered neural network of direct propagation, the control of the well system was implemented. The neural network of the proposed system was trained on the basis of the error back-propagation algorithm, and the identification unit works as a forecaster of system operation modes based on the error prediction. In the initial stage of the model adaptation, some fluctuations of the torque are observed at the output of the neural network, which is associated with new operating conditions and underestimated level of learning. However, the identification object and control system is able to maintain an error at minimum values and adapt the control system to a new conditions, which confirms the reliability of the proposed scheme.

show abstract

“…This is due to the fact that the regulation of the pressure and flow by opening and closing the control valves has a low economic efficiency, power and pressure losses, breakthroughs in the main pipelines and others negative effects. The booster pump station of the transportation and treatment system consists of two highvoltage asynchronous motor of the pumps (H-3,4), a reservoir (E-3), valves (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18), check valves (GC-1,2,3 and 4), filters (F-1, 2 and 3), oil metering system and measuring the quantity and quality of crude oil (SIKNS), as well as low-voltage frequency converter, step-up and step-down transformers and built-in controller for high voltage induction motors (HVAD) of the pumps (H-1,2). Oil from the oilfield production wells goes to BPS, where processed emulsifier, which is supplied from a reagent block and then the oil-emulsion goes to the separator-slug catcher and in a three-phase oil-gas separator.…”

Section: Introductionmentioning

confidence: 99%

Multidimensional Fuzzy Control System Development of Oil Transportation and Treatment Technological Processes Based on the Input/Output Parameters Model in the Precise Terms Set Form

Sagdatullin

2015

AMM

View full text Add to dashboard Cite

As pumps and pumping stations of oil collecting, maintaining reservoir pressure, transportation and treatment processes account for over 50 % of oil and gas enterprises estimated total cost of electricity, relevant issue is the development and application of intelligent control systems for energy efficiency of these processes. For the booster pump station as a multivariable nonlinear control object are proposed: a structure in which the function of regulating valves transferred to the pumps with frequency speed control; conceptual model with seven inputs and five outputs (adjustable) variables, which is the basis for constructing three-dimensional fuzzy controller with not-fuzzy terms to stabilize and improve the energy efficiency of oil transportation and treatment processes and minimize energy losses. In this paper we propose a control system of pumping station on the basis of multidimensional fuzzy logic controller with compensation of the mutual influence of control loops in the form of an additional system of production rules that helped to stabilize the liquid level at 2.5 m given level with an absolute error of ± 0.2 m and thus improve quality oil emulsion on the installation of oil and gas refining central collection point.

show abstract

Verbally defined processes controlled by fuzzy controllers with input/output parameters represented by set of precise terms

Cited by 11 publications

References 0 publications

Development of neural network controller for control of debutanization process in rectification column of gas-fractionating plant

Development of neural network controller for control of debutanization process in rectification column of gas-fractionating plant

Improving the energy efficiency of oil production using identification and prediction of operating modes of production wells based on data analysis methods, machine learning and neural networks

Multidimensional Fuzzy Control System Development of Oil Transportation and Treatment Technological Processes Based on the Input/Output Parameters Model in the Precise Terms Set Form

Contact Info

Product

Resources

About