Improved artificial bee colony-based optimization of boiler combustion considering NO  emissions, heat rate and fly ash recycling for on-line applications

Song, Jingge; Romero, Carlos E.; Yao, Zheng; He, Boshu

doi:10.1016/j.fuel.2015.12.065

Cited by 63 publications

(10 citation statements)

References 29 publications

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“…After initialization, the employed bees try to detect new food sources with the nearest areas. They saved in memory until creating new food sources to following the equation (13) where j = 1, 2, . .…”

Section: Artificial Bee Colonymentioning

confidence: 99%

“…Delgarm et al [12] determined the performance of energy building using the multi-objective artificial bee colony optimization approach. In addition, Song et al [13] used an improved ABC algorithm to optimize the combustion of a boiler to consider NO x emission. Ebrahim et al [14] used the ABC algorithm for proportional integral derivative (PID) controller parameters optimization for speed control of an AC driver.…”

Section: Introductionmentioning

confidence: 99%

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Effectiveness of Nature-Inspired Algorithms using ANFIS for Blade Design Optimization and Wind Turbine Efficiency

et al. 2019

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Blade design of the horizontal axis wind turbine (HAWT) is an important parameter that determines the reliability and efficiency of a wind turbine. It is important to optimize the capture of the energy in the wind that can be correlated to the power coefficient ( C p ) of HAWT system. In this paper, nature-inspired algorithms, e.g., ant colony optimization (ACO), artificial bee colony (ABC), and particle swarm optimization (PSO) are used to search for the blade parameters that can give the maximum value of C p for HAWT. The parameters are tip speed ratio, blade radius, lift to drag ratio, solidity ratio, and chord length. The performance of these three algorithms in obtaining the optimal blade design based on the C p are investigated and compared. In addition, an adaptive neuro-fuzzy interface (ANFIS) approach is implemented to predict the C p of wind turbine blades for investigation of algorithm performance based on the coefficient determination (R2) and root mean square error (RMSE). The optimized blade design parameters are validated with experimental results from the National Renewable Energy Laboratory (NREL). It was found that the optimized blade design parameters were obtained using an ABC algorithm with the maximum value power coefficient higher than ACO and PSO. The predicted C p using ANFIS-ABC also outperformed the ANFIS-ACO and ANFIS-PSO. The difference between optimized and predicted is very small which implies the effectiveness of nature-inspired algorithms in this application. In addition, the value of RMSE and R2 of the ABC-ANFIS algorithm were lower (indicating that the result obtained is more accurate) than the ACO and PSO algorithms.

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Section: Artificial Bee Colonymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effectiveness of Nature-Inspired Algorithms using ANFIS for Blade Design Optimization and Wind Turbine Efficiency

et al. 2019

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“…An improved flower pollination algorithm (IFPA) [22] was used to optimize hyper-parameters. An artificial bee colony (ABC) algorithm [23,24] was also used for modeling and optimization of coal-fired boilers.…”

Section: Introductionmentioning

confidence: 99%

Lowering Nitrogen Oxide Emissions in a Coal‐Powered 1000‐MW Boiler

2021

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Burning of coal in power plants produces excessive nitrogen oxide (NOx) emissions, which endanger people’s health. Proven and effective methods are highly needed to reduce NOx emissions. This paper constructs an echo state network (ESN) model of the interaction between NOx emissions and the operational parameters in terms of real historical data. The grey wolf optimization (GWO) algorithm is employed to improve the ESN model accuracy. The operational parameters are subsequently optimized via the GWO algorithm to finally cut down the NOx emissions. The experimental results show that the ESN model of the NOx emissions is more accurate than both of the LSTM and ELM models. The simulation results show NOx emission reduction in three selected cases by 16.5%, 15.6%, and 10.2%, respectively.

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“…The optimization activities are carried out to improve performance of coal and biomass cofiring process [7], and decrease the carbon content in ash [8]. Many investigations were devoted to the modeling and prediction of NOx emission [9][10][11] as well as to optimization methods for NOx reduction [12][13][14]. Current research on optimization methods are based on data from available measurement systems.…”

Section: Introductionmentioning

confidence: 99%

A Combustion Process Optimization and Numerical Analysis for the Low Emission Operation of Pulverized Coal-Fired Boiler

Madejski¹,

Janda²,

Modliński³

et al. 2016

Developments in Combustion Technology

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The paper presents experimental and numerical investigation of pulverized coal combustion process analysis and optimization. The research was conducted on the frontfired pulverized coal boiler with dedicated low-NOx furnace installation. In order to find optimal boiler operating conditions the acoustic gas temperature measurement system and mass flow rate of pulverized coal measurement system was applied. The uniform temperature distribution as a result of uniform coal and air flow provides the optimal combustion process with low level of NOx emission and total organic carbon content in ash. Experimental results confirm that the monitoring and control of fuel and air flow distribution allows to optimize combustion process by increasing thermal efficiency of the boiler. In the numerical part of investigation, the complex CFD model of pulverized coal boiler was made. The calculations of turbulent, reactive, and thermal flow processes were performed at different boiler operating conditions retrieved from power plant online monitoring system. The results of numerical simulations enable to identify the optimal boiler operating conditions.

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Improved artificial bee colony-based optimization of boiler combustion considering NO emissions, heat rate and fly ash recycling for on-line applications

Cited by 63 publications

References 29 publications

Effectiveness of Nature-Inspired Algorithms using ANFIS for Blade Design Optimization and Wind Turbine Efficiency

Effectiveness of Nature-Inspired Algorithms using ANFIS for Blade Design Optimization and Wind Turbine Efficiency

Lowering Nitrogen Oxide Emissions in a Coal‐Powered 1000‐MW Boiler

A Combustion Process Optimization and Numerical Analysis for the Low Emission Operation of Pulverized Coal-Fired Boiler

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