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

被引:1
|
作者
So, Min Seop [1 ]
Kibet, Duncan [1 ]
Woo, Tae Kyeong [1 ]
Kim, Seong-Joon [1 ]
Shin, Jong-Ho [1 ]
机构
[1] Chosun Univ, Dept Ind Engn, Gwangju 61452, South Korea
基金
新加坡国家研究基金会;
关键词
coal-fired power plants; pollutant emission; deep neural network; feature importance; coal mixture; OPTIMIZATION; MODEL;
D O I
10.3390/machines11121042
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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.
引用
收藏
页数:14
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