Modeling and Optimization of Unburned Carbon in Coal-Fired Boiler Using Artificial Neural Network and Genetic Algorithm

Ilamathi, P.; Selladurai, V.; Balamurugan, K.

doi:10.1115/1.4023328

Cited by 29 publications

(11 citation statements)

References 13 publications

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“…Ilamathi and Balamurugan [30] optimized the unbumed carbon in a coal fired boiler using artificial neural network model and GA. They determined the optimal level process parameters in the artifi cial neural network model in order to reduce the level of the unburned carbon.…”

Section: Introductionmentioning

confidence: 99%

Boilers Optimal Control for Maximum Load Change Rate

Elshafei

Habib

Al-Zaharnah

et al. 2014

Journal of Energy Resources Technology

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In many cogeneration systems, one or more boilers are used in hot standby to meet the plant demand of steam in case of failure or upset in the cogeneration unit. Such boilers need to quickly respond to sudden and large steam load changes. However, fast changes in the firing rate cause transient changes in both the drum-boiler steam pressure and drum level, in addition to the potential of developing of thermal stresses in the walls of steam risers. A genetic algorithm (GA) based optimization scheme is proposed for tuning the conventional boiler control loops to maximize the ability of the boiler to respond to large steam demand while keeping the fluctuations in pressure, drum level, and feed rate within acceptable operation limits. A nonlinear model for an actual boiler is first built, validated, and then, it is used to demonstrate the performance of the boiler with the proposed control loop optimization.

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Section: Introductionmentioning

confidence: 99%

Boilers Optimal Control for Maximum Load Change Rate

Elshafei

Habib

Al-Zaharnah

et al. 2014

Journal of Energy Resources Technology

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“…When evolution algebra T = 3, fitness function, that is, the mean square error of the test sample reaches the minimum, the values of MSE=4.485×10-3, at this point, the optimal SPREAD constant of GRNN is 0.23, the predicted value curve and the real value curve of NOx production were tested on GA-GRNN network with test samples, as shown in Fig2, the average relative error of the predicted results is 0.038, the predicted value curve and the real value curve of coal consumption are shown in Fig3 and the average relative error of the predicted results is 0.075 [9] . Based on the principle of minimizing power supply coal consumption and NOx production, the objective function of multi-objective optimization for boiler combustion is constructed by combining the constraint conditions, as shown in equation (1) [10] .…”

Section: Ga-grnn Establishment and Forecast Of Network Modelmentioning

confidence: 99%

Modeling and optimization of boiler combustion system in power station

Zhang

Wang

2018

MATEC Web Conf.

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This paper studies on researching the method of reducing NOx production and coal consumption of coal-fired power station boiler. It takes a power plant 600MW subcritical boiler as the research object, from the power plant Supervisory Information System (SIS) it gets the historical operation data as experimental data. Based on GA-GRNN (generalized regression neural network based on genetic optimization), a predictive model of boiler combustion system with 39 variables such as inlet and output of coal consumption and NOx production was established. Finally, coal consumption and NOx production were optimized based on the neural network model of boiler combustion system. In this paper, 29 adjustable thermal parameters of boiler combustion system model input are selected as optimization variables and the improved NSGA-II (non-dominated sorting genetic algorithm) is used to optimize multiple objective variables. The optimization study was carried out under the actual operating condition of 349.21 MW. After optimization, the coal consumption of power supply was reduced by 5.67% and the NOx production was reduced by 50%. Therefore, the optimization results provide guidance for adjusting the combustion of utility boilers.

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“…Boilers are the key equipment for energy conversion. The energy consumption management of a power plant mainly focuses on the modeling of a boiler combustion system based on big data, (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13) the optimization of a combustion coal mixture strategy, (14)(15)(16) and boiler unit equipment improvement. (17) References 1-5 indicate the use of the neural network method to model the key parameters of boiler combustion optimization and the selection of input parameters that is only based on manual operation experience.…”

Section: Introductionmentioning

confidence: 99%

Research on Energy Management Strategy of Captive Power Plant Based on Hybrid Method

Liu¹,

Huang²,

Liu³

et al. 2019

Sensors and Materials

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Compared with a regular power plant, the load and fuel composition of a captive power plant vary greatly, resulting in complex and variable working conditions, and for specific operating conditions, there is a lack of data. Considering the irregularity and incompleteness of the data, a data-mining-based boiler energy management analysis method was proposed in this paper. This study adopted the random forest and recursive feature elimination (RF-RFE) method to select the features of distributed control system (DCS) monitoring data. According to different load intervals, some data with better running conditions were selected for clustering. According to the clustering center, adjustable parameters were optimized, and the obtained optimization results were input to the support vector machine model for energy consumption comparison. The results show that the method used in this study can reduce the unit energy consumption by 3-5% by adjusting the controllable parameters.

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Modeling and Optimization of Unburned Carbon in Coal-Fired Boiler Using Artificial Neural Network and Genetic Algorithm

Cited by 29 publications

References 13 publications

Boilers Optimal Control for Maximum Load Change Rate

Boilers Optimal Control for Maximum Load Change Rate

Modeling and optimization of boiler combustion system in power station

Research on Energy Management Strategy of Captive Power Plant Based on Hybrid Method

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