Coal blending is a process of optimization under all constraints. The influences of different constraints on the objective function are different. To analyze the effects of several constraints including coal calorific value, volatile, moisture, ash and sulfur content on the optimal price of coal blending, an optimizing model was established by using particle swarm optimization algorithm in this paper. Moreover, the effects of adjusting the constraint range of calorific value and volatile content of the coal on coal blending economy were analyzed in detail. The results show that, the influence of each constraint on the blending coal's price meets the piecewise linear function, the economy of range constraint is determined by the slope of the function, the smaller absolute value of the slope has the higher economy. Setting reasonable constraints for coal blending can further improve the economy of blending coal and satisfy the requirement of coal property. The research methods in this paper can provide reference for constraints design of coal blending, it is helpful for thermal power plants to obtain more economical solution of coal blending.%电厂配煤是一个满足所有约束条件的寻优过程,而每种约束条件对目标函数的影响均不相同.本文利用粒子群优化算法建立配煤寻优模型,分析发热量、挥发分、水分、灰分与硫分等约束条件对配煤最优价格的影响趋势,并具体分析了配煤过程中调整发热量与挥发分约束范围对配煤经济性的影响.研究表明:各个约束条件对混煤价格的影响趋势基本符合分段线性函数;约束范围经济性由函数的斜率决定,斜率绝对值越小经济性越优,越大则经济性越差;合理制定调整配煤约束条件,可进一步提高配煤的经济效应,并获得较高的煤质参数指标.本文研究方法对电厂工作人员制定配煤约束条件有很好的指导作用,有利于电厂获得更加经济的配煤掺烧方案.
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