ANALYSIS OF BOILER TUBE LEAKAGE BY USING ARTIFICIAL NEURAL NETWORK

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

DESIGN AND IMPLEMENTATION OF INTELLIGENT MONITORING SYSTEMS FOR THERMAL POWER PLANT BOILER TRIPS

Steam boilers represent the main equipment in the power plant. Some boiler trips may lead to an entire shutdown of the plant, which is economically burdensome. An early detection and diagnosis of the boiler trips is crucial to maintain normal and safe operational conditions of the plant. Numbers of methodologies have been proposed in the literature for fault diagnosis of power plants. However, rapid deployment of these methodologies is difficult to be achieved due to certain inherent limitations such as system inability to learn or a dynamically improve the system performance and the brittleness of the system beyond its domain of expertise. As a potential solution to these problems, two artificial intelligent monitoring systems specialized in boiler trips have been proposed and coded within the MA TLAB environment in the present work. The training and validation of the two systems have been performed using real operational data which was captured from the plant integrated acquisition system of JANAMANJUNG coal-fired power plant. An integrated plant data preparation framework for seven boiler trips with related operational variables, has been proposed for the training and validation of the proposed artificial intelligent systems. The feedforward neural network methodology has been adopted as a major computational intelligent tool in both systems. The root mean square error has been widely used as a performance indicator of the proposed systems. The first intelligent monitoring system represents the use of the pure artificial neural network system for boiler trip detection. The final architecture for this system has been explored after investigation of various main neural network topology combinations which include one and two hidden layers, one to ten neurons for each hidden layer, three types of activation function, and four types of multidimensional minimization training algorithms. It has been found that there was no general neural network topology combination that can be applied for all boiler trips. All seven boiler trips under consideration had been detected by the proposed systems before or at the same time as the plant control system. The second intelligent monitoring system represents mergmg of genetic algorithms and artificial neural networks as a hybrid intelligent system. For this hybrid intelligent system, the selection of appropriate variables from hundreds of boiler operation variables with optimal neural network topology combinations to monitor boiler trips was a major concern. The encoding and optimization process using genetic algorithms has been applied successfully. A slightly lower root mean square error was observed in the second system which reveals that the hybrid intelligent system performed better than the pure neural network system. Also, the optimal selection of the most influencing variables was performed successfully by the hybrid intelligent system. The proposed artificial intelligent systems could be adopted on-line as a reliable controller of the thermal power plant boiler

ANALYSIS OF BOILER TUBE LEAKAGE BY USING ARTIFICIAL NEURAL NETWORK

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

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

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%

ECOS 2012

The 8-volume set contains the Proceedings of the 25th ECOS 2012 International Conference, Perugia, Italy, June 26th to June 29th, 2012. ECOS is an acronym for Efficiency, Cost, Optimization and Simulation (of energy conversion systems and processes), summarizing the topics covered in ECOS: Thermodynamics, Heat and Mass Transfer, Exergy and Second Law Analysis, Process Integration and Heat Exchanger Networks, Fluid Dynamics and Power Plant Components, Fuel Cells, Simulation of Energy Conversion Systems, Renewable Energies, Thermo-Economic Analysis and Optimisation, Combustion, Chemical Reactors, Carbon Capture and Sequestration, Building/Urban/Complex Energy Systems, Water Desalination and Use of Water Resources, Energy Systems- Environmental and Sustainability Issues, System Operation/ Control/Diagnosis and Prognosis, Industrial Ecology

Flame stability and burner condition monitoring through optical sensing and digital imaging

This thesis describes the design, implementation and experimental evaluation of a prototype instrumentation system for flame stability and burner condition monitoring on fossil-fuel-fired furnaces. A review of methodologies and technologies for the monitoring of flame stability and burner condition is given, together with the discussions of existing problems and technical requirements in their applications. A technical strategy, incorporating optical sensing, digital imaging, digital signal/image processing and soft computing techniques, is proposed. Based on this strategy, a prototype flame imaging system is developed. The system consists of a rigid optical probe, an optical-bearn-splitting unit, an embedded photodetector and signal-processing board, a digital camera, and a mini-motherboard with associated application software. Detailed system design, implementation, calibration and evaluation are reported. A number of flame characteristic parameters are extracted from flame images and radiation signals. Power spectral density, oscillation frequency, and a proposed universal flame stability index are used for the assessment of flame stability. Kernel-based soft computing techniques are employed for burner condition monitoring. Specifically, kernel principal components analysis is used for the detection of abnormal conditions in a combustion process, whilst support vector machines are used for the prediction of NO x emission and the identification of flame state. Extensive experimental work was conducted on a 9MW th heavy-oil-fired combustion test facility to evaluate the performance of the prototype system and developed algorithms. Further tests were carried out on a 660MWth heavy-oil-fired boiler to investigate the cause of the boiler vibration from a flame stability point of view. Results Obtained from the tests are presented and discussed

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

Portugal SB13: contribution of sustainable building to meet EU 20-20-20 targets

Proceedings of the International Conference Portugal SB13: contribution of sustainable building to meet EU 20-20-20 targetsThe international conference Portugal SB13 is organized by the University of Minho, the Technical University of Lisbon and the Portuguese Chapter of the International Initiative for a Sustainable Built Environment in Guimarães, Portugal, from the 30th of October till the 1st of November 2013. This conference is included in the Sustainable Building Conference Series 2013-2014 (SB13-14) that are being organized all over the world. The event is supported by high prestige partners, such as the International Council for Research and Innovation in Building and Construction (CIB), the United Nations Environment Programme (UNEP), the International Federation of Consulting Engineers (FIDIC) and the International Initiative for a Sustainable Built Environment (iiSBE). Portugal SB13 is focused on the theme â Sustainable Building Contribution to Achieve the European Union 20-20-20 Targetsâ . These targets, known as the â EU 20-20-20â targets, set three key objectives for 2020: - A 20% reduction in EU greenhouse gas emissions from 1990 levels; - Raising the share of EU energy consumption produced from renewable resources to 20%; - A 20% improvement in the EU's energy efficiency. Building sector uses about 40% of global energy, 25% of global water, 40% of global resources and emit approximately 1/3 of the global greenhouse gas emissions (the largest contributor). Residential and commercial buildings consume approximately 60% of the worldâ s electricity. Existing buildings represent significant energy saving opportunities because their performance level is frequently far below the current efficiency potentials. Energy consumption in buildings can be reduced by 30 to 80% using proven and commercially available technologies. Investment in building energy efficiency is accompanied by significant direct and indirect savings, which help offset incremental costs, providing a short return on investment period. Therefore, buildings offer the greatest potential for achieving significant greenhouse gas emission reductions, at least cost, in developed and developing countries. On the other hand, there are many more issues related to the sustainability of the built environment than energy. The building sector is responsible for creating, modifying and improving the living environment of the humanity. Construction and buildings have considerable environmental impacts, consuming a significant proportion of limited resources of the planet including raw material, water, land and, of course, energy. The building sector is estimated to be worth 10% of global GDP (5.5 trillion EUR) and employs 111 million people. In developing countries, new sustainable construction opens enormous opportunities because of the population growth and the increasing prosperity, which stimulate the urbanization and the construction activities representing up to 40% of GDP. Therefore, building sustainably will result in healthier and more productive environments. The sustainability of the built environment, the construction industry and the related activities are a pressing issue facing all stakeholders in order to promote the Sustainable Development. The Portugal SB13 conference topics cover a wide range of up-to-date issues and the contributions received from the delegates reflect critical research and the best available practices in the Sustainable Building field. The issues presented include: - Nearly Zero Energy Buildings - Policies for Sustainable Construction - High Performance Sustainable Building Solutions - Design and Technologies for Energy Efficiency - Innovative Construction Systems - Building Sustainability Assessment Tools - Renovation and Retrofitting - Eco-Efficient Materials and Technologies - Urban Regeneration - Design for Life Cycle and Reuse - LCA of sustainable materials and technologies All the articles selected for presentation at the conference and published in these Proceedings, went through a refereed review process and were evaluated by, at least, two reviewers. The Organizers want to thank all the authors who have contributed with papers for publication in the proceedings and to all reviewers, whose efforts and hard work secured the high quality of all contributions to this conference. A special gratitude is also addressed to Eng. José Amarílio Barbosa and to Eng. Catarina Araújo that coordinated the Secretariat of the Conference. Finally, Portugal SB13 wants to address a special thank to CIB, UNEP, FIDIC and iiSBE for their support and wish great success for all the other SB13 events that are taking place all over the world