Application of genetic algorithm-back propagation for prediction of mercury speciation in combustion flue gas
- 125 Downloads
Coal combustion is one of the main sources of mercury emission. Studies using artificial neural networks (ANNs) to predict mercury emission have shown the feasibility of ANN method. Such analyses aimed to provide guidance for mercury emission control in coal combustion. A mercury emission prediction model was developed by modifying the traditional back propagation (BP) neural networks, and a genetic algorithm (GA) based on global search was used, so called the GA-BP neural networks. In total, six main factors were evaluated and selected as the characteristics parameters. Totally, 20 coal-fired boilers were used as training samples, and three different types of mercury including elemental mercury, oxidized mercury, and particulate mercury were used as outputs. The accuracy of prediction results was analyzed, and source of error was discussed. Results show that correlation efficiency for the training samples was as high as 0.895. Three additional samples were studied to test the predictive model. Results of training and predicting were highly correlated with actual measurement results. It is shown that GA-BP is a promising model for mercury speciation prediction.
KeywordsFlue gas Predicting model Mercury speciation
This work is supported by the grant of 2012AA06A113 of National High-tech R&D Program of China and Grants of 201009048 and 200909025 of the Ministry of Environmental Protection of China.
- Kawashima M (1994) Artificial neural network back propagation model with three-phase annealing developed for the building energy predictor shootout. ASHRAE Trans 100:1096–1118Google Scholar
- Qeethara AS, Ghaleb ER (2013) Predicting the effects of medical waste in the environment using artificial neural networks: a case study. Int J Comput Sci Issues 10:258–261Google Scholar
- Yi JQ, Wang Q, Zhao DB, Wen JT (2007) BP neural network prediction-based variable-period sampling approach for networked control systems. Appl Math Comput 185:976–988Google Scholar
- Zhou JM, Luo ZY, Zhu YQ, Fang MX (2014) Mercury emission and its control in Chinese coal-fired power plants. Zhejiang University Press, BerlinGoogle Scholar