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Adoption of intelligent computational techniques for steam boilers tube leak trip
No full textFrequent boiler tube trips in coal fired power plants can increase operating cost significantly. An early detection and diagnosis of boiler trips is essential for continuous safe operations in the plant. Several methodologies for the fault diagnosis in a plant have been developed. However these methodologies are difficult to be implemented. In this study, two artificial intelligent monitoring systems specialized in boiler trips have been proposed. The first intelligent monitoring system represents the use of pure artificial neural network system whereas the second intelligent monitoring system represents merging of genetic algorithms and artificial neural networks as a hybrid intelligent system. In the first system using pure artificial neural network, the trip was predicted 5 minutes before the actual trip occurrence. The hybrid intelligent system was able to optimize the selection of the most influencing variables successfully and predict the trip 2 minutes before the actual trip. The first intelligent system performed better than the second one based on the prediction time. The proposed artificial intelligent system could be adopted on-line as a reliable controller of the thermal power plant boiler
DESIGN AND IMPLEMENTATION OF INTELLIGENT MONITORING SYSTEMS FOR THERMAL POWER PLANT BOILER TRIPS
Get PDFSteam 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
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Soot Blowing Operation Optimization Using PSO Method by Studying Behaviour of Operating Parameter in Sub Critical Coal Fired Power Plant
No full textCoal, natural gas and fuel-oil are three major fossil fuels sources are vastly used in electrical power generation sector in Malaysia. In a coal fired power plant, the major byproducts resulting from coal combustion inside boiler is soot, ash and NOx emissions. Boiler fouling and slagging are common problems that leads reduces heat transfer rate in furnace and boiler efficiency. This happens when soot and ash is formed and deposited along the boiler tubes, furnaces and heaters. Hence, soot blowing operation is used to blow off steam in affected areas of boilers as a cleaning mechanism. However, current soot blowing operation is practiced through operator’s visual inspection of slagging or fouling rate in furnace. This leads to inefficient soot blowing operation that effects the plant’s operating and maintenance cost. Thus, by studying behavior of operating parameters, soot blowing operation can be optimized to reduce unnecessary soot blowing operation in power plant
Operating Parameter Optimization using DOE Method to Reduce Unburned Carbon of Fly Ash for Tangential Fired Subcritical Coal Fired Power Plant
No full textAs electric demand increasing due to rapid economic growth, most developing country are sourcing for cheap fuel and low maintenance power plant which coal fired power plant become the more preferable plant. The cheap and abundant coal resources have played a major factor for coal power plant selection compare to other type of power plant. Although this plant type has low maintenance and operating cost but its emission of by product has a great effect on daily plant operation and environment. The one of the major emission was unburned carbon which by product of incomplete combustion where remaining of coal that unburned exits the furnaces with ash. Presence of higher percentage of unburned carbon indicates the low efficiency of furnace combustion and this directly affects financial status of the power plant operators. This condition causes severe damages on the boiler tube by formation of slagging and clinkering which reduces heat transfer and efficiency of the furnace. Current method proved to be more time consuming and plant operator facing difficulty to reduce unburned carbon in real time. As a solution for this problem, a best parameter was predicted to achieve low percentage of unburned carbon
Multidimensional minimization training algorithms for steam boiler high temperature superheater trip using artificial intelligence monitoring system
No full textVariations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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