摘要
针对解决火电厂目前电煤供应紧张、煤价成本居高不下的现状。文章通过对电厂的实际用煤展开最优炉前配煤研究,方案通过建立以掺烧煤成本最低为目标函数和机组对混煤的工业成分要求作为约束条件的配煤数学模型,利用粒子群算法的局部快速收敛特性优化遗传算法进行模型求解。其单混煤煤质工业成分间的非线性映射关系通过建立GA-BP神经网络预测模型进行预测。通过算例及误差结果证明该方法在煤质预测和求解配煤成本最低的可靠性,可对电厂实际配煤进行指导。
Aiming at the current coal supply shortages in thermal power plant,and the high coal prices,in this paper,the optimal coal blending for pre-fired coal is studied through the actual use of coal in the power plant. A coal blending mathematical model is established with minimum coal blending cost as the objective function and the unit's requirements for the industrial components of the blended coal as a constraint to use the particle swarm. The local fast convergence feature of the algorithm is optimized by genetic algorithm to solve the model. The non-linear mapping relationship between industrial components of the single coal blend coal quality is predicted by establishing a GA-BP neural network prediction model.Through the analysis of examples and error results,it is proved that this method can predict and solve the reliability of coal blending with the lowest cost. The research can guide the actual coal blending of power plants.
作者
付轩熠
茅大钧
印琪民
FU Xuan-yi;MAO Da-jun;YIN Qi-min(School of Automation Engineering,Shanghai University of Electric Power,Shanghai 200090,China;Huaneng Shanghai Shidongkou First Power Plant,Shanghai 200090,China)
出处
《煤炭工程》
北大核心
2018年第9期150-154,共5页
Coal Engineering
关键词
配煤优化
煤质预测
数学模型
目标函数
约束条件
混煤价格
coal blending optimization
coal quality prediction
mathematical model
objective function
constraintconditions
mixed coal price