| 引用本文: | 董晓旭,何安瑞,孙文权,汪净,李正涛.应用熵权-TOPSIS法的加热炉炉温在线设定模型[J].哈尔滨工业大学学报,2017,49(7):119.DOI:10.11918/j.issn.0367-6234.201602050 |
| DONG Xiaoxu,HE Anrui,SUN Wenquan,WANG Jing,LI Zhengtao.On-line temperature setup model of reheating furnace based on entropy weight-topsis method[J].Journal of Harbin Institute of Technology,2017,49(7):119.DOI:10.11918/j.issn.0367-6234.201602050 |
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| 摘要: |
| 为解决加热炉中同时存在多块状态不同的板坯而导致加热策略不同的问题，针对每个板坯的实时情况，以单个炉区为研究对象，结合熵权法和TOPSIS法的中间过程，提出一种熵权-TOPSIS法.引入特殊钢种等级概念，对不同的钢种进行量化处理，并将其与板坯温差、当前位置和板坯厚度共同作为评价指标；结合熵权法对评价指标差异性的要求和TOPSIS法对样本方案的加权方法，把客观熵权作为TOPSIS法计算贴近度的权值，归一化后得到最终的板坯综合权重，利用此权重对控制段炉温进行最终设定.分别数值模拟了固定数值模型与熵权-TOPSIS模型，对比结果表明：与固定权值模型相比，使用本模型后，板坯平均温差下降了5.05 ℃，最大温差下降了7.77 ℃.炉温平均波动值减小了8.98 ℃.
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| 关键词: 钢坯加热炉 板坯 炉温设定 炉温优化 熵权-TOPSIS法 |
| DOI：10.11918/j.issn.0367-6234.201602050 |
| 分类号:TF31 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(51404021)；北京市自然科学基金(3154035)；中央高校基本科研业务费(FRF-TP-15-060A3) |
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| On-line temperature setup model of reheating furnace based on entropy weight-topsis method |
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DONG Xiaoxu1,HE Anrui1,SUN Wenquan1,WANG Jing2,LI Zhengtao2
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(1.National Engineering Research Center of Advanced Rolling (University of Science and Technology Beijing), Beijing 100083, China;2. 2250 Hot Strip Mill, HunanValin LY Steel Co., Ltd., Loudi 417009, Hunan, China)
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| Abstract: |
| To deal with the difference of heating strategy resulted from the different conditions of the slabs, a kind of entropy weight-TOPSIS method is proposed, which takes a single furnace zone as the research object and combines with the intermediate process of entropy weight and TOPSIS. A concept of special steel grade level is introduced to quantify steel grade, which is taken as the evaluation indexes with the slab temperature difference, slab location and thickness. This model combines the requirements for the difference of evaluation index in the entropy weight method and the weighted method of TOPSIS. The objective entropy weight is used as the closeness degree of the TOPSIS method, and by normalizing it the final weight of the slab is obtained and the temperature of control zone can be set. The simulation results show that, compared with the fixed weight model, the average and maximum temperature difference are decreased by 5.05 ℃ and 7.77 ℃, and the fluctuation value of furnace temperature is reduced by 8.98 ℃.
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| Key words: reheating furnace slabs furnace temperature setup furnace temperature optimization entropy weight-TOPSIS method |
