3,017 research outputs found

    ANALYSIS OF BOILER TUBE LEAKAGE BY USING ARTIFICIAL NEURAL NETWORK

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    Artificial neural network (ANN) models, developed by training the network with data from an existing plant, are very useful especially for large systems such as Thermal Power Plant. The project is focusing on the ANN modeling development and to examine the relative importance of modeling and processing variables in investigating the unit trip due to steam boiler tube leakage. The modeling and results obtained will be used to overcome the effect of the boiler tube leakage which influenced the boiler to shutdown if the tube leakage continuously producing the mixture of steam and water to escape from the risers into the furnace. The Artificial Intelligent-ANN has been chosen as the system to evaluate the behavior of the boiler because it has the ability to forecast the trips. Hence, the objective of this study has been developed to design an ANN to detect and diagnosis the boiler tube leakage and to simulate the ANN using real data obtained from Thermal Power Plant. The feed-forward with back-propagation, (BP) ANN model will be trained with the real data obtained from the plant. Training and validation of ANN models, using real data from an existing plant, are very useful to minimize or avoid the trip occurrence in the plants. The study will focus on investigating the unit trip due to tube leakage of risers in the boiler furnace and developing the ANN model to forecast the trip

    A review of research on acoustic detection of heat exchanger tube

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    Leakage in heat exchanger tubes can result in unreliable products and dangerous situations, which could cause great economic losses. Along with fast development of modern acoustic detection technology, using acoustic signals to detect leakage in heat exchange tube has been gradually accepted and considered with great potential by both industrial and research societies. In order to further advance the development of acoustic signal detection technology and investigate better methods for leakage detection in heat exchange tube, in this paper, firstly, we conduct a short overview of the theory of acoustic signal detection on heat exchanger tube, which had already been continuously developed for a few decades by researchers worldwide. Thereafter, we further expound the advantages and limitations of acoustic signal detection technology on heat exchanger tube in four aspects: 1) principles of acoustic signal detection, 2) characteristics of sound wave propagation in heat exchanger tube, 3) methods of leakage detection, and 4) leakage localization in heat exchanger tube

    Application of Intelligent Computational Techniques in Power Plants:A Review

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    Growing worldwide demand for energy leads to increasing the levels of challenge in power plants management. These challenges include but are not limited to complex equipment maintenance, power estimation under uncertainty, and energy optimisation. Therefore, efficient power plant management is required to increase the power plant’s operational efficiency. Conventional optimisation tools in power plants are not reliable as it is challenging to monitor, model and analyse individual and combined components within power systems in a plant. However, intelligent computational tools such as artificial neural networks (ANN), nature-inspired computations and meta-heuristics are becoming more reliable, offering a better understanding of the behaviour of the power systems, which eventually leads to better energy efficiency. This paper aims to provide an overview of the development and application of intelligent computational tools such as ANN in managing power plants. Also, to present several applications of intelligent computational tools in power plants operations management. The literature review technique is used to demonstrate intelligent computational tools in various power plants applications. The reviewed literature shows that ANN has the greatest potential to be the most reliable power plant management tool

    An Intelligent Monitoring Interface for a Coal-Fired Power Plant Boiler Trips

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    A power plant monitoring system embedded with artificial intelligence can enhance its effectiveness by reducing the time spent in trip analysis and follow up procedures. Experimental results showed that Multilayered perceptron neural network trained with Levenberg-Marquardt (LM) algorithm achieved the least mean squared error of 0.0223 with the misclassification rate of 7.435% for the 10 simulated trip prediction. The proposed method can identify abnormality of operational parameters at the confident level of ±6.3%

    Miniature mobile sensor platforms for condition monitoring of structures

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    In this paper, a wireless, multisensor inspection system for nondestructive evaluation (NDE) of materials is described. The sensor configuration enables two inspection modes-magnetic (flux leakage and eddy current) and noncontact ultrasound. Each is designed to function in a complementary manner, maximizing the potential for detection of both surface and internal defects. Particular emphasis is placed on the generic architecture of a novel, intelligent sensor platform, and its positioning on the structure under test. The sensor units are capable of wireless communication with a remote host computer, which controls manipulation and data interpretation. Results are presented in the form of automatic scans with different NDE sensors in a series of experiments on thin plate structures. To highlight the advantage of utilizing multiple inspection modalities, data fusion approaches are employed to combine data collected by complementary sensor systems. Fusion of data is shown to demonstrate the potential for improved inspection reliability

    Depth estimation of inner wall defects by means of infrared thermography

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    There two common methods dealing with interpreting data from infrared thermography: qualitatively and quantitatively. On a certain condition, the first method would be sufficient, but for an accurate interpretation, one should undergo the second one. This report proposes a method to estimate the defect depth quantitatively at an inner wall of petrochemical furnace wall. Finite element method (FEM) is used to model multilayer walls and to simulate temperature distribution due to the existence of the defect. Five informative parameters are proposed for depth estimation purpose. These parameters are the maximum temperature over the defect area (Tmax-def), the average temperature at the right edge of the defect (Tavg-right), the average temperature at the left edge of the defect (Tavg-left), the average temperature at the top edge of the defect (Tavg-top), and the average temperature over the sound area (Tavg-so). Artificial Neural Network (ANN) was trained with these parameters for estimating the defect depth. Two ANN architectures, Multi Layer Perceptron (MLP) and Radial Basis Function (RBF) network were trained for various defect depths. ANNs were used to estimate the controlled and testing data. The result shows that 100% accuracy of depth estimation was achieved for the controlled data. For the testing data, the accuracy was above 90% for the MLP network and above 80% for the RBF network. The results showed that the proposed informative parameters are useful for the estimation of defect depth and it is also clear that ANN can be used for quantitative interpretation of thermography data

    Application of Nondestructive Testing in Inspection of Boiler and Pressure Vessel and Pressure Piping

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    Non-destructive testing technology has been widely used in all occupations. The testing of equipment is called non-destructive testing technology. During the testing process, there is no need to pre-process the equipment to test, nor add other testing substances, which is especially suitable for the testing of boiler equipment in industrial production. Boiler and pressure vessel equipment is an important part of industrial production. The boiler pressure vessel has been in an operating environment with relatively high temperature for a long time, and it is common for the boiler pressure vessel to rupture. At this time, non-destructive testing technology came into being. It can detect damage to the boiler structure without damaging it, which plays a vital role for boilers, especially pressure pipes. This article analyzes the advantages, disadvantages and principles of pressure pipeline non-destructive testing technology, and discusses its application in boiler flaw detection to provide guidance for future work

    On-Line Monitoring of Environment-Assisted Cracking in Nuclear Piping Using Array Probe Direct Current Potential Drop

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    A direct current potential drop method utilizing array probes with measurement ends maintaining an equalized potential designated as equi-potential switching array probe direct current potential drop (ESAP-DCPD) technique has been developed earlier at Seoul National University. This paper validates ESAP-DCPD technique by showing consistency among experimental measurements, analytical solution and numerical predictions using finite element analysis (FEA) of electric field changes with crack growth in metals. In order to examine its viability as an on-line monitoring of environment assisted crack growth at the inner surface of piping welds, artificial inner surface cracks were introduced in a full-scale weldment mockup pipe and stainless steel metal mockup pipe. The weldment was joined by low alloy steel and stainless steel pipes. The pipes were monitored by using ESAP-DCPD in laboratory environments. Optimization of electrical wiring configuration has produced results with significantly reduced noise for adequately long period of time. Then optimized experimental results were compared with the FEA prediction results for the mockup to show a good agreement. Also a round-robin measurement has been made at three laboratories. It has been found that the developed ESAP-DCPD can detect circumferential cracks with a depth of 40 % of wall thickness in stainless steel with a good detectability for further growth behaviors. For axial cracks, however, the measurements showed poor detectability. Hence the developed ESAP-DCPD system can be used to monitor large circumferential cracks that existing non-destructive examination techniques often fail to detect until leakage takes place.Korea (South). Ministry of Trade, Industry and Energy. Korea Institute of Energy Technology Evaluation and Plannin
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