Modeling and Optimization of NOX Emission in a Coal-fired Power Plant using Advanced Machine Learning Methods

Tan, Peng; Ji, Xia; Cheng, Zhang; Fang, Qingyan; Chen, Gang

doi:10.1016/j.egypro.2014.11.1129

Cited by 20 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The results showed that adjusting control parameters of the combustion system might improve both combustion efficiency and NO x emissions. Tan et al [17] developed a methodology combining the advanced extreme learning machine (ELM) with harmony search (HS) and optimized the operational parameters of a boiler for the control of NO x emissions in a 700 MW pulverized-coal-fired power plant. The results achieved an MRE of 1.13% which is better than those of the ANN and SVR.…”

Section: Methodsmentioning

confidence: 99%

Prediction of SOx-NOx Emission in Coal-Fired Power Plant Using Deep Neural Network

So,

Kibet,

Woo

et al. 2023

Machines

View full text Add to dashboard Cite

Coal has been used as the most commonly energy source for power plants since it is relatively cheap and readily available. Thanks to these benefits, many countries operate coal-fired power plants. However, the combustion of coal in the coal-fired power plant emits pollutants such as sulfur oxides (SOx) and nitrogen oxides (NOx) which are suspected to cause damage to the environment and also be harmful to humans. For this reason, most countries have been strengthening regulations on coal-consuming industries. Therefore, the coal-fired power plant should also follow these regulations. This study focuses on the prediction of harmful emissions when the coal is mixed with high-quality and low-quality coals during combustion in the coal-fired power plant. The emission of SOx and NOx is affected by the mixture ratio between high-quality and low-quality coals so it is very important to decide on the mixture ratio of coals. To decide the coal mixture, it is a prerequisite to predict the amount of SOx and NOx emission during combustion. To do this, this paper develops a deep neural network (DNN) model which can predict SOx and NOx emissions associated with coal properties when coals are mixed. The field data from a coal-fired power plant is used to train the model and it gives mean absolute percentage error (MAPE) of 7.1% and 5.68% for SOx and NOx prediction, respectively.

show abstract

Section: Methodsmentioning

confidence: 99%

Prediction of SOx-NOx Emission in Coal-Fired Power Plant Using Deep Neural Network

So,

Kibet,

Woo

et al. 2023

Machines

View full text Add to dashboard Cite

show abstract

“…Como ilustra a Figura 3, o neurônio artificial simula o comportamento do neurônio biológico, possuindo diversas entradas e uma saída [5]. A figura abaixo ilustra o modo de funcionamento de um neurônio artificial e o seu funcionamento de forma integrada, formando uma rede com camadas definidas: Em Seu estudo, [6] relatou que algumas técnicas de IA que foram utilizadas em diferentes trabalhos para reduzir emissões de NOx em caldeiras industriais: genetic algorithms (GA), particle swarm optimization (PSO) e ant colony optimization (ACO). Além desta abordagem, [A] aplicou em seu estudo técnicas de Support Vector Regression (SVR), Artificial Neural Network (ANN), Extreme Learning Machine (ELM) e Harmony Search (HS) onde demonstrou os benefícios na aplicação de monitoramento deste tipo de equipamento de equipamentos de combustão.…”

Section: Algumas Técnicas De Ia Aplicadas Ao Controle E Monitoramentounclassified

Aplicação De Técnicas De Inteligência Artificial Em Controle De Processos Em Batelada

SIlva¹,

Lepikson²

2019

Blucher Engineering Proceedings

View full text Add to dashboard Cite

Resumo: O objetivo deste trabalho é realizar uma revisão da literatura sobre o uso de inteligência artificial (IA) no processo em batelada, caracterizando o seu benefício no tratamento da não linearidade e transiência de variáveis de processos em batelada. Através de uma revisão sistemática da literatura, foram estabelecidos critérios para busca e seleção dos artigos, de forma a retratar a evolução de estudos na área nos últimos vinte anos. Após seleção, as principais aplicações de IA para controle de bateladas foram descritos. Foi possível concluir que a base Science Direct apresentou o maior número de artigos na área e que de modo geral variações de processo foram tratadas através de redes neurais artificiais e lógicas fuzzy demonstraram capacidade de auxiliar no controle de variáveis não lineares.

show abstract

“…Considering the quality improvement of optimization results, the operating parameters in coal-fired utility boiler were optimized by three different algorithms and simulated annealing genetic algorithm (SAGA) showed a superior optimization performance [16]. The relationship between operating parameters and NOx emission was studied with extreme learning machine, which showed that it had a stronger generalization ability, and harmony search was also proved to be more powerful in optimizing operating parameters [17]. Particle swarm optimization (PSO) was applied to optimize the air distribution scheme to reach best combustion based on the boiler combustion prediction model [18].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Thermal Efficiency and Unburned Carbon in Fly Ash of Coal-Fired Utility Boiler via Grey Wolf Optimizer Algorithm

Zhao

et al. 2019

IEEE Access

View full text Add to dashboard Cite

This paper focuses on improving thermal efficiency and reducing unburned carbon in fly ash by optimizing operating parameters via a novel high-efficient swarm intelligence optimization algorithm (grey wolf optimizer algorithm, GWO) for coal-fired boiler. Mathematical models for thermal efficiency and unburned carbon in fly ash of the discussed boiler are established by artificial neural network (ANN). Based on the ANN models, the grey wolf optimizer algorithm is used to obtain higher thermal efficiency and lower unburned carbon by optimizing the operating parameters. Meanwhile, the comparisons between GWO and particle swarm optimization (PSO) and genetic algorithm (GA) show that GWO has superior performance to GA and PSO regarding the boiler combustion optimization. The proposed method can accurately optimize the boiler combustion performance, and its validity and feasibility have been experimentally validated. Additionally, a run of optimization takes a less time period, which is suitable for the real-time optimization. INDEX TERMS Coal-fired utility boiler, grey wolf optimizer, thermal efficiency, unburned carbon in fly ash.

show abstract

Modeling and Optimization of NOX Emission in a Coal-fired Power Plant using Advanced Machine Learning Methods

Cited by 20 publications

References 9 publications

Prediction of SOx-NOx Emission in Coal-Fired Power Plant Using Deep Neural Network

Prediction of SOx-NOx Emission in Coal-Fired Power Plant Using Deep Neural Network

Aplicação De Técnicas De Inteligência Artificial Em Controle De Processos Em Batelada

Optimization of Thermal Efficiency and Unburned Carbon in Fly Ash of Coal-Fired Utility Boiler via Grey Wolf Optimizer Algorithm

Contact Info

Product

Resources

About