Application of hybrid genetic algorithm to optimization of pulverizing system

作者：Liao, Yan-Fen^*; Ma, Xiao-Qian

来源：Journal of South China University of Technology(Natural Science Edition), 2009, 37(2): 152-157.

• Combustion condition
• Furnace combustion
• Hybrid genetic algorithms
• Outlet temperature
• Pulverized coals
• Pulverizing systems
• Radial basis function neural networks
• Simulated results

This paper proposes a hybrid genetic algorithm (GA) to optimize the operation of pulverizing system in admixing combustion conditions. In this algorithm, a fuzzy neural network is adopted to evaluate the slag property of admixed coal, and a Radial Basis Function neural network is used to predict the fineness of pulverized coal and the outlet temperature of the mill. Then, the pulverizing system is optimized, with the mill safety and the furnace combustion stability as the restraints, and with the lowest blended coal price and pulverizing unit cost as the targets. Moreover, a kind of hierarchical gene is designed to fasten the searching process. Simulated results indicate that the proposed algorithm is practicable and feasible in optimizing the operation of pulverizing system in admixing combustion conditions.

• 出版日期2009-2
• 单位华南理工大学