TQ171.622 99021273计算机集散控制技术在光学玻璃精密退火炉群温度控制中的应用=Applications of computer centralizedand scattered technique in the temperaturecontrol of furnace group for precision annealingof optical glass...TQ171.622 99021273计算机集散控制技术在光学玻璃精密退火炉群温度控制中的应用=Applications of computer centralizedand scattered technique in the temperaturecontrol of furnace group for precision annealingof optical glass[刊,中]/瞿宁,郑金奎,雷力鸣,裴涛(中国兵器工业部第58所.四川,绵阳(621000))//兵工自动化,1998,(1).—25-28介绍了计算机集散控制技术应用于光学玻璃精密退火炉群温度控制系统的组成。展开更多
To improve the thermal efficiency and reduce nitrogen oxides （NOx ） emissions in a power plant for energy conservation and environment protection, based on the reconstructed section temperature field and other relat...To improve the thermal efficiency and reduce nitrogen oxides （NOx ） emissions in a power plant for energy conservation and environment protection, based on the reconstructed section temperature field and other related parameters, dynamic radial basis function （RBF） artificial neural network （ANN） models for forecasting unburned carbon in fly ash and NO, emissions in flue gas ware developed in this paper, together with a multi-objective optimization system utilizing particle swarm optimization and Powell （PSO-Powell） algorithm. To validate the proposed approach, a series of field tests were conducted in a 350 MW power plant. The results indicate that PSO-Powell algorithm can improve the capability to search optimization solution of PSO algorithm, and the effectiveness of system. Its prospective application in the optimization of a pulverized coal （ PC ） fired boiler is presented as well.展开更多
文摘TQ171.622 99021273计算机集散控制技术在光学玻璃精密退火炉群温度控制中的应用=Applications of computer centralizedand scattered technique in the temperaturecontrol of furnace group for precision annealingof optical glass[刊,中]/瞿宁,郑金奎,雷力鸣,裴涛(中国兵器工业部第58所.四川,绵阳(621000))//兵工自动化,1998,(1).—25-28介绍了计算机集散控制技术应用于光学玻璃精密退火炉群温度控制系统的组成。
文摘To improve the thermal efficiency and reduce nitrogen oxides （NOx ） emissions in a power plant for energy conservation and environment protection, based on the reconstructed section temperature field and other related parameters, dynamic radial basis function （RBF） artificial neural network （ANN） models for forecasting unburned carbon in fly ash and NO, emissions in flue gas ware developed in this paper, together with a multi-objective optimization system utilizing particle swarm optimization and Powell （PSO-Powell） algorithm. To validate the proposed approach, a series of field tests were conducted in a 350 MW power plant. The results indicate that PSO-Powell algorithm can improve the capability to search optimization solution of PSO algorithm, and the effectiveness of system. Its prospective application in the optimization of a pulverized coal （ PC ） fired boiler is presented as well.
