doi:10.16597/j.cnki.issn.1002-154x.2024.01.002
基于粒子群算法的高炉热风炉煤气消耗优化方法研究
张剑飞1∗ 刘 潇2
(1. 宝武集团鄂城钢铁有限公司 能源环保部,鄂州 436000; 2. 湖北中平鄂钢联合焦化有限责任公司,鄂州 436000)
Research on Optimization Method for Gas Consumption of Blast Furnace Hot Blast Stove Based on Particle Swarm Optimization
Zhang Jianfei 1∗ Liu Xiao 2
(1. Energy and Environmental Protection Department, Baowu Group Echeng Iron and Steel Co. , LTD. , Ezhou 436000, China; 2. Hubei Zhongping E Steel United Coking Co. , Ltd. , Ezhou 436000, China)
摘要:
高炉煤气管网压力与流量具有不稳定性,导致煤气存在严重放散损失。 该研究基于粒子群算法进行高炉热 风炉煤气消耗优化。 首先,基于高炉热风炉煤气消耗量的预测结果,建立一个以煤气产消平衡和煤气放散量最小为 目标的高炉热风炉煤气消耗优化模型;其次,引入粒子群算法求解模型,获得最佳优化方案。 实验结果表明,经设计 方法优化后的高炉热风炉煤气放散量基本为 0,证实了该方法可以有效降低煤气消耗。
关键词：粒子群算法 高炉 热风炉 煤气消耗 优化方法；
Abstract：
Due to the instability of the pressure and flow rate in the blast furnace gas pipeline network, there is serious gas dissipation loss. Therefore, method based on particle swarm optimization is studied for optimizing the gas consumption of blast furnace hot blast furnaces. Based on the prediction results of gas consumption in blast furnace hot blast furnaces, an optimization model for gas consumption in blast furnace hot blast furnaces is established with the goal of balancing gas production and consumption and minimizing gas emission. Particle swarm optimization algorithm is introduced to solve the model and obtain the optimal optimization scheme. The experimental results show that the optimized design method results in a gas release rate of basically 0 for the blast furnace hot blast stove, confirming that this method can effectively reduce gas consumption.
Keywords：particle swarm optimization; blast furnace; hot air stove; gas consumption; optimization methods;