References
- Lixia, Q. and Guanchun, L., 2015, The current situations and development trends in Chinese cement trade. Industrial Building Supplement II, 45, 156–159.
- Xinchun, W., Rujin, H., Wei, S., et al., 2013, Suggested policy to promote the greenhouse gases of cement industry. China Cement Year Book, 6, 111–113.
- Qiuyun, S., 2014, Research on Typical Working Conditions of Cement Calcining Process, (Jinan: Jinan University).
- Chunlai, J. and Jintian, Y., 2012, Tendency analysis and countermeausures for total emission control of NOx in cement industry during “the twelfth five-year” period. Environment and Sustainble Development, 6, 26–32.
- Dongxiong, L. and Changhe, C., 2001, Development of coal combustion and pollution control technology. Journal of Electric Power, 1, 27–31.
- Yonglong, J., 2004, Analyses About The Producing Mechanism of NOx During Sintering Process. Sintering and Pelletizing, 5, 6–8.
- Lvqing, C., 2011, The Study on the NO Emission in Cement Precalciner. (Guangzhou: South China University of Technology).
- Peng, W., 2014, Analysis of the current situation of denitration in the cement industry. Energy and Energy Conservation, 1, 89–90.
- Ilamathi, P., Selladurai, V., Balamurugan, K., et al., 2013, ANN–GA approach for predictive modeling and optimization of NOx emission in a tangentially fired boiler. Clean Technologies and Environmental Policy, 15(1), 125–131.10.1007/s10098-012-0490-5
- Jian, M., Hongdong, Z. and Jingjing, Y., 2011, Application and prospect of artificial neural network. Electronic Design Engineering, 24, 62–65.
- Vaidya, R., Vyas, P. and Chhatpar, H.S., 2003, Statistical optimization of medium components for the production of chitinase by Alcaligenes xylosoxydans. Enzyme and Microbial Technology, 33(1), 92–96.10.1016/S0141-0229(03)00100-5
- Shixiong, R. and Dan, M., 2010, Application of the Genitic algorithm and artificial neural network to the prediction of the urban wasterwater discharge. Shanxi Architecture, 9, 175–176.
- Hao, W., 2012, Applications and Research on Atmospheric Quality Evaluation and Forecast by the genetic algorithms and BP Neural Network. (Nanjing University of Information Science and Technology).
- Haider, M.A., Pakshirajan, K., Singh, A., et al., 2008, Artificial neural network-genetic algorithm approach to optimize media constituents for enhancing lipase production by a soil microorganism. Applied Biochemistry and Biotechnology, 144(3), 225–235.10.1007/s12010-007-8017-y
- Abu Qdais, H., Bani Hani, K. and Shatnawi, N., 2010, Modeling and optimization of biogas production from a waste digester using artificial neural network and genetic algorithm. Resources, Conservation and Recycling, 54(6), 359–363.
- Feng, Y., Zhang, W., Sun, D., et al., 2011, Ozone concentration forecast method based on genetic algorithm optimized back propagation neural networks and support vector machine data classification. Atmospheric Environment, 45(11), 1979–1985.10.1016/j.atmosenv.2011.01.022
- Ozgur, T., Tuccar, G., Ozcanli, M., et al., 2011, Prediction of emissions of a diesel engine fueled with soybean biodiesel using artificial neural networks. Energy Education Science and Technology Part A-Energy Science and Research, 27(2), 301–312.
- Thomsen, K., Jensen, L.S. and Schomburg, F., 1998, FLS-FuIler ILC-lowNO(x) calciner commissioning and operation at Lone Star St. Cruz in California. ZKG International, 51(10), 542–550.
- Zhijuan, H., 2004, The Study on Mechanism of Nitrogen Oxides Transformation and Control Technology for Precalciner. (Wuhan: Huazhong University of Science and Technology).