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In-furnace Temperature Information Included Combustion Optimization of a Utility Boiler

Author: LiuJianJun
Tutor: FangQingYanï¼›ZhouHuaiChun
School: Huazhong University of Science and Technology
Course: Thermal Power Engineering
Keywords: Combustion detector BP neural network Genetic Algorithms Particle swarm optimization Numerical Simulation
CLC: TK227.1
Type: Master's thesis
Year: 2011
Downloads: 37
Quote: 0
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Abstract


Decades in the foreseeable future , coal will continue to be our main energy . A coal - based energy pattern , a dominant position in China 's power industry , coal-fired thermal power . Conventional boiler combustion optimization and adjustment is often given specific operating point optimization strategy for the important control parameters , the lack of general applicability . Combustion optimization system based on the model prediction and multi-objective optimization techniques can achieve closed-loop control system to achieve multiple economic and environmental optimization target a wide range of applications . In this paper, a furnace combustion temperature field detection system to carry out a combustion test boiler multi- condition detection cross-section along the height of the furnace temperature distribution , combustion efficiency and NOx emissions data . Combustion test test results based on multiple loading conditions , adding the section temperature information of the furnace , combustion efficiency and NOx emissions BP neural network model . Combustion efficiency and NOx emissions of the model predictions with the experimental values ??relative error of less than 1% and 5% respectively . Prediction model based on established , respectively, using a genetic algorithm and particle swarm optimization the boiler high combustion efficiency and low NOx emissions target and multi- target combustion optimization . Multi-objective optimization results show that , join the furnace temperature prediction model optimization results more in line with the actual situation ; in 134MW , 154MW , 172MW , genetic algorithm optimized combustion efficiency increased by 0.84% ??, 1.37% and 2.61% , NOx emissions decreased by 18% , 6% , 18% . Finally, the numerical simulation method to optimize the process and results . Sectional average temperature numerical simulation results with the experimental error of less than 8% , indicating that the numerical simulation can accurately simulate the combustion process of the boiler . Numerical Simulation of NOx emissions and combustion efficiency value is in good agreement with the optimization results trend also shows that the optimization result is reasonable . This study shows that : based furnace combustion temperature field visualization system detects furnace combustion temperature information to establish combustion neural network prediction model to more accurately predict the boiler combustion process of pollutant emissions ; genetic algorithm optimization results closer to the actual situation , the results guidance boiler optimization burning online for the establishment of boiler combustion optimization guidance system to lay the foundation .

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CLC: > Industrial Technology > Energy and Power Engineering > Steam Power Engineering > Steam boiler > Run > Combustion and adjustments
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