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Huang S.,Nanjing Southeast University | Jiang L.,Nanjing Southeast University | Wang T.,Nanjing Nangang Industrial Development Co. | Liu F.,Nanjing Nangang Industrial Development Co. | And 4 more authors.
Dongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Southeast University (Natural Science Edition) | Year: 2012

Combined the neural network with genetic algorithms, a model for a shaft furnace which has tons of gas consumption and NO x emission is built. There are sixteen input parameters in this model, containing mineral aggregate components, moisture content, furnace temperature and so on. Output parameters are the gas consumption and the concentration of NO x emission. Based on the 700 groups of field data, the neural network has been trained. The results show that the prediction error of the gas consumption is less than 3% and the prediction error of NO x emission is less than 5%. Base on this model, real-coded genetic algorithm is applied to linear weight low gas consumption and low NO x emission and switch the model into a function with single variable parameter. Multiple objective functions and operating parameters focusing on different conditions can be discovered under different wight ratios. According to the optimization, the result shows that NO x emission decreases by 20.37% while gas consumption increases by 1.7%.

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