Soft-computing models for soot-blowing optimization in coal-fired utility boilers

Peña, Begoña; Teruel, Enrique; Díez, Luis I.

doi:10.1016/j.asoc.2010.04.023

Cited by 48 publications

(32 citation statements)

References 30 publications

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“…B. Pena et al suggested a probabilistic model to predict the effectiveness of soot-blowing using ANN and Adaptive Nuero-Fuzzy Inference Systems (ANFIS) [12]. H.B.…”

Section: Brief Review On Past Workmentioning

confidence: 99%

Intelligent Prediction of Clinker Formation Condition for Steam Boiler Tubes Using Artificial Neural Network

2019

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Ash, as one of the by-product of combustion either accumulates onto boiler tubes as slag or is collected by electrostatic precipitators attached to the backend of the boiler. Flue gas will transport these ash particles either to the inner surfaces of the boiler or to the dust collecting facilities at the backend of the boiler. Sintered ash deposits formed in the radiant section of the boiler are known as clinkers and they contribute to a wide variety of problems to the boiler. Preventative measures to combat clinker formation is in dire need to the energy sector. In this study, a prediction model using real plan data was developed for detection of clinker formation conditions. Several variations of Artificial Neural Networks were tried and test, with emphasis given on the feed-forward neural network, cascade neural network and recurrent neural network. In addition, sensitivity analysis was also conducted to determine the influence of random input variables to their respective response variables. The Tornado Diagram is selected as the method to determine the most influential parameter for clinker formation. It is expected that the Recurrent Neural Network prediction model and the identified most influential input parameter for clinker formation will assist operators in decision making for the maintenance of boilers.

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“…B. Pena et al suggested a probabilistic model to predict the effectiveness of soot-blowing using ANN and Adaptive Nuero-Fuzzy Inference Systems (ANFIS) [12]. H.B.…”

Section: Brief Review On Past Workmentioning

confidence: 99%

Intelligent Prediction of Clinker Formation Condition for Steam Boiler Tubes Using Artificial Neural Network

2019

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“…The particular power plant instrumentation, such as heat flux meters [16,17] and acoustic pyrometry [18], can easily show the status of the furnace's heat absorption by providing continuous signals, while inducing a meaningful increase in cost of installation, maintenance, and the sensor. The fuzzy models and artificial neural network are well applied in the monitoring of ash fouling of boilers [7,[19][20][21], but it is difficult for real time monitoring because of great sum of calculations. A statistical model of ash deposition was proposed by Zheng et al in a pulverized-coal boiler by an exact simulation Monte Carlo approach [22,23].…”

Section: Introductionmentioning

confidence: 99%

An Optimization Study on Soot-Blowing of Air Preheaters in Coal-Fired Power Plant Boilers

et al. 2019

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This paper presents a comprehensive approach for optimization of soot-blowing of air preheaters in a coal-fired power plant boiler. In the method, modeling of the cleanliness factor is firstly proposed to monitor the ash deposition status of the air preheaters. Then, the statistical fitting of the ash fouling status is subsequently obtained to analyze the ash fouling dynamics and assessment of optimized soot-blowing strategies. Soot-blowing strategies are finally developed to optimize the steam consumption and heat transfer efficiency. Our methods can achieve the fouling monitoring and soot-blowing optimization of air preheater (APH) by using the existing monitoring data, not requiring additional special instruments and complex computing systems. The methodology is validated with the actual operating data of a 300 MW coal-fired power plant boiler. The results show the effectiveness of the proposed method. It can be used for the soot-blowing optimization in most coal-fired power plant boiler with air preheaters.

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“…It has been estimated to be an important source of losses of availability and energy efficiency in thermal power plants, that may amount to 1% under normal operating conditions [1]. A large number of case studies can be found in recent decades [3][4][5], indicating a continuous worldwide interest in this problem. The collected data showed 7% of units suffering from frequent fouling and 40% reporting occasional problems, out of a total of 91 pulverized-coal utility boilers in the USA.…”

Section: Introductionmentioning

confidence: 99%

Ash fouling monitoring and key variables analysis for coal fired power plant boiler

Shi

Wang

2015

THERM SCI

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Ash deposition on heat transfer surfaces is still a significant problem in coalfired power plant utility boilers. The effective ways to deal with this problem are accurate on-line monitoring of ash fouling and soot-blowing. In this paper, an online ash fouling monitoring model based on dynamic mass and energy balance method is developed and key variables analysis technique is introduced to study the internal behavior of soot-blowing system. In this process, artificial neural networks are used to optimize the boiler soot-blowing model and mean impact values method is utilized to determine a set of key variables. The validity of the models has been illustrated in a real case-study boiler, a 300 MW Chinese power station. The results on same real plant data show that both models have good prediction accuracy, while the artificial neural networks model II has less input parameters. This work will be the basis of a future development in order to control and optimize the soot-blowing of the coal-fired power plant utility boilers.tion. Therefore, optimization for soot blowing system according to the actual cleaning need becomes more and more important. At first, an appropriate assessment for the ash fouling level of each heat transfer surface of the boiler is needed. Monitoring techniques become more and more important for the study of boiler behaviors and ash fouling influence in coal-fired power plant boilers and have attracted extensive research effort recently [6][7][8].Ash fouling monitoring can be accomplished by means of on-line calculations and special power plant instrumentation or a combination of both techniques. On-line calculations always need to calculate the heat absorption of heat transfer surfaces. The conventional method of calculating the heat absorption rate is the log-mean-temperature-difference approach [6]. However, it is a static balance method which cannot show the dynamic behaviors of heat transfer surfaces. While special power plant instrumentation (i. e. heat fluxes meters [7]) can well reflect the status of heat absorption of heat transfer surfaces by providing a continuous signal, they induce significant increase of cost on sensors, installation, and maintenance. There are some commercial (ash fouling) boiler monitoring tools [8], but the internal behaviors of these applications are unclear.Key variables analysis is a tool that is often used to study the behavior of a system, or a model, and to attain the dependency of outputs on each or some of input parameters [9]. Artificial neural network (ANN) has recently proved its availability to tackle with thermal engineering problems [10,11]. ANN has also been used in system modeling, identification, control, forecasting, power systems and optimization [12][13][14]. ANN has also been proposed to deal with ash fouling [15], but it is not completely used for tackle the problem of variables influence. Some examples are important revisions of the ANN applications and references made in the field of energy [16].In this paper, the cleanliness factors (C...

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Soft-computing models for soot-blowing optimization in coal-fired utility boilers

Cited by 48 publications

References 30 publications

Intelligent Prediction of Clinker Formation Condition for Steam Boiler Tubes Using Artificial Neural Network

Intelligent Prediction of Clinker Formation Condition for Steam Boiler Tubes Using Artificial Neural Network

An Optimization Study on Soot-Blowing of Air Preheaters in Coal-Fired Power Plant Boilers

Ash fouling monitoring and key variables analysis for coal fired power plant boiler

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