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基于粒子群算法的火电厂优化配煤研究 被引量:8

Investigation of Coal Blending Optimization for Power Plants Based on Particle Swarm Optimization Algorithm
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摘要 在火电厂的配煤过程中,需要综合考虑锅炉运行的稳定性与经济性。利用粒子群算法建立火电厂经济性配煤优化模型,主要以配煤经济性作为目标函数,并以单煤的价格、发热量、灰分、挥发分、水分以及硫分等6项指标值作为约束条件。基于内蒙古某电厂的来煤条件,采用本模型进行配煤优化计算。仿真实验表明:带惯性权重的粒子群算法(标准PSO)具有较好的全局搜索能力,能够快速、准确地搜索到最佳的优劣质煤配比关系和最经济的配煤价格。 In the process of coal blending for power plants, the stability and economics of boiler must be consider synthetically. The coal blending model of power plant was established by using particle swarm optimization algorithm, where the economics of coal blending was acted as an object function, moreover, six industrial and economical target of coal acted as constraint conditions, concluding price and calorific value and volatile matter and ash constituent and water constituent and sulfur content. Based on coal obtaining condition of a power plant in Inner Mongolia, the coal optimization blending proportion was investigated by using the model. The simulation result showed that the method that applying a modified particle swarm optimization algorithm to optimize blending coal system is feasible, which has a better global search capability. Consequently, it can quickly search the best ratio between high quality coal and bad one as well as the best economical price of blending coal.
出处 《锅炉技术》 北大核心 2012年第5期18-24,共7页 Boiler Technology
关键词 配煤优化 火电厂经济性 粒子群优化算法 约束条件 惯性权重因子 适应度函数 coal blending optimization economics of power plant particle swarm optimization algorithm constraint condition inertial weight factor fitness function
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