scholarly journals Application of Methods of Fuzzy Logic and Neural Networks for Automation of Technological Processes in Oil and Gas Engineering and Increasing the Efficiency of Oil Production

2021 ◽  
Vol 19 (2) ◽  
pp. 83
Author(s):  
A M Sagdatullin
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Oil And Gas ◽  
Oil Production ◽  
Technological Processes

В данной работе рассматривается вопрос повышения эффективности функционирования насосных систем, как наиболее энергоемкой части нефтегазового месторождения. Обоснована актуальность темы исследования для нефтегазового машиностроения, сформулирована основная цель исследования, заключающаяся в цифровизации рассматриваемых процессов и создании отечественных систем автоматического управления с применением алгоритмов нечеткой логики и нейронных сетей. Рассмотрены методы построения современных систем управления, проанализированы их достоинства и недостатки. Рассмотрены особенности различных подходов к построению систем автоматического управления технологическими объектами при добыче и транспортировке нефти. Наиболее распространенными являются метод прямого цифрового управления или метод с применением обратных связей. Представлено классическое описание объектов автоматизации и телемеханизации на основе параметров систем. Приведены основные характеристики рассматриваемых технологических процессов, таких как добыча, подготовка и транспортировка нефти, не позволяющие добиться максимальной эффективности в существующем подходе. Выявлены наиболее важные факторы для эффективных систем автоматического управления данными объектами. Полученные экспериментальные данные показали, что параметры технологического процесса варьируются в значительных пределах от номинальных значений, что приводит к невысокому качеству работы регуляторов. Точность моделей идентификации системы на основе линейных авторегресинных методов составляет не более 30 %. Сделан вывод о необходимости применения для управления нелинейными объектами с присущими неопределенностями на основе нейронечетких и нечетких регуляторов с дискретными термами.

Neuro-Fuzzy Approaches for FRP Oil and Gas Pipeline Condition Assessment

2004 ◽  
Author(s):  
S. Kumar ◽  
F. Taheri
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Expert System ◽  
Oil And Gas ◽  
Condition Assessment ◽  
Image Feature ◽  
Gas Pipelines ◽  
Multiple Neural Networks ◽  
Oil And Gas Pipelines ◽  
Interpretation System

Recent advances in ultrasonic, optical and piezoelectric sensors, and computing technologies have led to the development of inspection systems for underground and off-shore structures such as water lines, oil and gas pipes, and telecommunication conduits. It is now possible to use inspection technologies that require no human intervention (i.e., having had to go underground or off-shore); moreover, the inspection process can be fully automated, from data acquisition to data analysis, and eventually to condition assessment and repair. This paper describes the development of an automated data interpretation system for fiber-reinforced polymer composites (FRP) oil and gas pipelines, which would also be applicable to metallic pipes. The interpretation system obtains C-scan image data from so-called “smart pigs” and maps data using Geographic Information System (GIS) and Global Positioning System (GPS). Assessment of health of pipelines using neural networks is then performed to identify the high-risk locations in each pipeline or pipeline network, thus allowing the inspection to be properly targeted. The proposed system utilizes artificial neural networks and genetic algorithm to recognize various types of defects in FRP oil and gas pipelines. Image processing and wavelets techniques are used to find the detail of the damage geometry. An expert system is also developed, using fuzzy Logic, to perform damage condition assessment and suggest an optimum repair protocol. The framework of the developed system, thus includes GIS, risk map, modification of digital images for preprocessing, image feature segmentation, utilization of multiple neural networks for feature pattern recognition, the fusion of multiple neural networks via the use of fuzzy logic systems, and the proposed expert system for suggested repair.

Comparative analysis of intelligent planning methods

2012 ◽  
Vol 9 (2) ◽  
pp. 53-57 ◽  
Author(s):  
O.V. Darintsev ◽  
A.B. Migranov
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Comparative Analysis ◽  
Mobile Robots ◽  
Sensory Information ◽  
Working Environment ◽  
Planning Methods ◽  
Intelligent Planning

The main stages of solving the problem of planning movements by mobile robots in a non-stationary working environment based on neural networks, genetic algorithms and fuzzy logic are considered. The features common to the considered intellectual algorithms are singled out and their comparative analysis is carried out. Recommendations are given on the use of this or that method depending on the type of problem being solved and the requirements for the speed of the algorithm, the quality of the trajectory, the availability (volume) of sensory information, etc.

Withdrawn: Diabetes Mellitus Prognosis Using Fuzzy Logic and Neural Networks Case Study: Alexandria Vascular Center (AVC)

2019 ◽  
Vol 13 ◽  
Author(s):  
Abeer A. Amer ◽  
Soha M. Ismail
Keyword(s):  
Diabetes Mellitus ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Computer Science ◽  
Recent Advances ◽  
Vascular Center ◽  
Copyright Permission ◽  
Legal Right

The following article has been withdrawn on the request of the author of the journal Recent Advances in Computer Science and Communications (Recent Patents on Computer Science): Title: Diabetes Mellitus Prognosis Using Fuzzy Logic and Neural Networks Case Study: Alexandria Vascular Center (AVC) Authors: Abeer A. Amer and Soha M. Ismail* Bentham Science apologizes to the readers of the journal for any inconvenience this may cause BENTHAM SCIENCE DISCLAIMER: It is a condition of publication that manuscripts submitted to this journal have not been published and will not be simultaneously submitted or published elsewhere. Furthermore, any data, illustration, structure or table that has been published elsewhere must be reported, and copyright permission for reproduction must be obtained. Plagiarism is strictly forbidden, and by submitting the article for publication the authors agree that the publishers have the legal right to take appropriate action against the authors, if plagiarism or fabricated information is discovered. By submitting a manuscript, the authors agree that the copyright of their article is transferred to the publishers if and when the article is accepted for publication.

Artificial potential field neuro-fuzzy controller for autonomous navigation of mobile robots

2020 ◽  
pp. 095965182097483
Author(s):  
Mahamat Loutfi Imrane ◽  
Achille Melingui ◽  
Joseph Jean Baptiste Mvogo Ahanda ◽  
Fredéric Biya Motto ◽  
Rochdi Merzouki
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Mobile Robots ◽  
Potential Field ◽  
Autonomous Navigation ◽  
Fuzzy Logic Controller ◽  
Artificial Potential Field ◽  
Type Reduction ◽  
Interval Type

Some autonomous navigation methods, when implemented alone, can lead to poor performance, whereas their combinations, when well thought out, can yield exceptional performances. We have demonstrated this by combining the artificial potential field and fuzzy logic methods in the framework of mobile robots’ autonomous navigation. In this article, we investigate a possible combination of three methods widely used in the autonomous navigation of mobile robots, and whose individual implementation still does not yield the expected performances. These are as follows: the artificial potential field, which is quick and easy to implement but faces local minima and robustness problems. Fuzzy logic is robust but computationally intensive. Finally, neural networks have an exceptional generalization capacity, but face data collection problems for the learning base and robustness. This article aims to exploit the advantages offered by each of these approaches to design a robust, intelligent, and computationally efficient controller. The combination of the artificial potential field and interval type-2 fuzzy logic resulted in an interval type-2 fuzzy logic controller whose advantage over the classical interval type-2 fuzzy logic controller was the small size of the rule base. However, it kept all the classical interval type-2 fuzzy logic controller characteristics, with the major disadvantage that type-reduction remains the main cause of high computation time. In this article, the type-reduction process is replaced with two layers of neural networks. The resulting controller is an interval type-2 fuzzy neural network controller with the artificial potential field controller’s outputs as auxiliary inputs. The results obtained by performing a series of experiments on a mobile platform demonstrate the proposed navigation system’s efficiency.

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