Fuel oil leak detection in power plant with recurrent neural network and execute in programmable logic controller

Mohammadi, Mahmoud; Nikbakht, Abbas; Bavalishoar, Aliakbar

doi:10.1109/kbei.2015.7436168

Cited by 4 publications

(3 citation statements)

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“…The proposed neuroidentifier continuously determines the current values of the object's parameters based on its input and output signals, which are subsequently used by the adaptive device to recalculate the controller's parameters (Kondratenko, 2011). In addition, the intelligent system for monitoring fuel oil leaks at power stations uses the fuel oil leak identifier based on a recurrent neural network (Mohammadi, 2015).…”

Section: Fig 2 Hierarchical Levels Of Control Of Complex Dynamic Proc...mentioning

confidence: 99%

Contemporary Technologies and Society: Innovations, Artificial Intelligence, and Challenges

2023

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The collective monograph elucidates the trends in the formation and development of the digital society in the 21st century, and the role of information and communication technologies therein. Presently, information and innovative technologies are shaping more efficient management mechanisms, expanding access to healthcare, education, science, culture, and banking, enhancing the quality of public services, and broadening communication and information access capabilities.The research findings presented in the monograph underscore the significance and necessity of digital technologies for the welfare of the population and economic development. Today, information and innovative technologies are integrated into business processes across virtually all spheres of activity, as they enhance decision-making efficiency. Information technologies enable any activity to be conducted online, facilitating solutions to social issues by easing access to education through remote learning, local government services through e-governance, electronic registration systems, medical services via digital health systems, and the provision of electronic financial and banking services.The first chapter of the monograph analyses the current state and prospects of using information and innovative technologies in education, presents research results on the application of artificial intelligence, machine learning, nanotechnologies, and virtual reality in higher education training. It also highlights the nuances of developing digital competencies among schoolchildren and students.The second chapter is dedicated to the application of information and innovative technologies in economics and management, examining their impact on the economy of the information society. The digital economy is grounded on information and communication technologies that facilitate ecommerce through the operation of electronic digital devices, tools, and automated systems.The third chapter contemplates the theoretical concepts of employing information and innovative technologies in society at large, considering their influence in fields such as ecology, social issues, psychology, and professional adaptation to digital existence.The fourth chapter is devoted to the utilization of artificial intelligence, studying its societal impact and evaluating potential opportunities and risks. Particular attention is given to the status and prospects of artificial intelligence in education, manufacturing, medical fields, and the development of communicative competencies.The fifth chapter of the monograph focuses on contemporary technologies in physical education, sports, and physical rehabilitation, which are paramount in the context of ensuring effective approaches to physical development, rehabilitation, and fostering a healthy lifestyle approach.

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Section: Fig 2 Hierarchical Levels Of Control Of Complex Dynamic Proc...mentioning

confidence: 99%

Contemporary Technologies and Society: Innovations, Artificial Intelligence, and Challenges

2023

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“…Internally based systems use flow, pressure as well as fluid temperature for monitoring internal parameters of the pipeline, whereas, externally based systems apply local dedicated sensors. However, recurrent neural networks can also be used for nonlinear system identification for fuel oil leak detection [33]. In order to train the network, data can be taken from inlet and outlet flow parameters of the pipeline.…”

Section: B Recurrent Neural Networkmentioning

confidence: 99%

Deep Learning for Pipeline Damage Detection: an Overview of the Concepts and a Survey of the State-of-the-Art

Jafari

Razvarz

Gegov

et al. 2020

2020 IEEE 10th International Conference on Intelligent Systems (IS)

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Pipelines have been extensively implemented to transfer oil as well as gas products at wide distances as they are safe, and suitable. However, numerous sorts of damages may happen to the pipeline, for instance erosion, cracks, and dent. Hence, if these faults are not properly refit will result in the pipeline demolitions having leak or segregation which leads to tremendously environment risks. Deep learning methods aid operators to recognize the earliest phases of threats to the pipeline, supplying them time and information in order to handle the problem efficiently. This paper illustrates fundamental implications of deep learning comprising convolutional neural networks. Furthermore the usages of deep learning approaches for hampering pipeline detriment through the earliest diagnosis of threats are introduced.

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“…Analysis of the current research in this area shows, that neural controllers are most often used in automation systems of complex nonlinear multidimensional plants operating under conditions of uncertain disturbances, and for which there is no sufficient manual control experience gained by their operators [22][23][24]. This is confirmed by many examples of their successful application presented in a number of works, in particular, in control systems of different types of industrial and mobile robots [25,26], DC and synchronous motors [27,28], power and heating plants [29][30][31], ships [32] and others [33,34].…”

Section: Introductionmentioning

confidence: 97%

Neuroevolutionary Approach to Control of Complex Multicoordinate Interrelated Plants

Kondratenko¹,

Kozlov²,

Gerasin³

2019

IJC

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This paper presents the developed by the authors step-by-step neuroevolution based approach to designing control systems for complex multicoordinate interrelated plants (MIP). The proposed approach allows us to build the structure of the automatic control system (ACS) for the MIP on the basis of the single complex neural controller (NC) with multiple inputs and outputs as well as to implement the effective training of its multilayer neural network (NN) by means of the evolutionary based algorithm, taking into account the mutual influence of all variables of the MIP in an optimal way. In order to study and validate the efficiency of the presented approach the design of the ACS for the spatial motion of caterpillar mobile robot (MR) able to move on inclined and vertical ferromagnetic surfaces is carried out in this work. The developed ACS based on the NC with optimal structure allows us to achieve high quality indicators of spatial motion control, taking into account the mutual influence of control channels of the MR’s speed and angle that confirms the high efficiency of the proposed approach.

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Fuel oil leak detection in power plant with recurrent neural network and execute in programmable logic controller

Cited by 4 publications

References 1 publication

Contemporary Technologies and Society: Innovations, Artificial Intelligence, and Challenges

Contemporary Technologies and Society: Innovations, Artificial Intelligence, and Challenges

Deep Learning for Pipeline Damage Detection: an Overview of the Concepts and a Survey of the State-of-the-Art

Neuroevolutionary Approach to Control of Complex Multicoordinate Interrelated Plants

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