Application of genetic algorithm-back propagation for prediction of mercury speciation in combustion flue gas

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.