| 引用本文: | 张琦,李鸿亮,赵晓宇,贾辉.高炉煤气产生量与消耗量动态预测模型及应用[J].哈尔滨工业大学学报,2016,48(1):101.DOI:10.11918/j.issn.0367-6234.2016.01.015 |
| ZHANG Qi,LI Hongliang,ZHAO Xiaoyu,JIA Hui.Dynamic prediction model of blast furnace gas generation and consumption and its application[J].Journal of Harbin Institute of Technology,2016,48(1):101.DOI:10.11918/j.issn.0367-6234.2016.01.015 |
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| 摘要: |
| 针对钢铁企业高炉煤气产生量和消耗量波动频繁，难以有效预测的问题，应用小波分析方法将高炉煤气产生量和消耗量历史数据经剔除"噪声"后分为趋势数据和波动数据，并结合高炉实际运行工况，建立一种具有时序更新和自我修正功能的最小二乘支持向量机（Lssvm）高炉煤气动态预测模型.以一座容积为3 200 m3高炉的煤气产生量和相应的热风炉煤气消耗量作为样本数据，对8 h内的煤气产生量与消耗量进行了动态预测.结果表明：采用小波分析后的Lssvm预测模型绝对平均误差降低到2.77%，Update_Lssvm模型预测高炉煤气产量精度达到1.55%，热风炉高炉煤气消耗量精度达到4.23%，解决了变工况下高炉煤气产生量和消耗量预测随机性问题.与其他预测模型相比，Update_Lssvm模型预测精度明显提升.该模型不仅具有泛化能力，也为高炉煤气优化调度提供了理论依据.
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| 关键词: 钢铁企业 高炉煤气 动态预测 小波分析 节能 |
| DOI：10.11918/j.issn.0367-6234.2016.01.015 |
| 分类号:TF05 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(51274065); 教育部中央高校基本科研业务经费(N130402008, N110702001). |
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| Dynamic prediction model of blast furnace gas generation and consumption and its application |
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ZHANG Qi1, LI Hongliang1, ZHAO Xiaoyu1, JIA Hui2
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（1.State Environmental Protection Key Laboratory of Eco-Industry（Northeastern University）, 110819 Shenyang, China; 2. Northeastern University Science & Technology Industry Co.,LTD., 110819 Shenyang, China）
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| Abstract: |
| Due to the frequent fluctuations of generation and consumption of blast furnace gas and difficulties to be predicted effectively in iron and steel works, the dynamic Lssvm prediction model with a timing update and self-correction function is established. The model is based on decomposed volatility and trend data of the generation and consumption after excluding the data "noise" by wavelet analysis, which combined with the actual blast furnace operating conditions. The amount of blast furnace gas generation of 3 200 m3 blast furnace and the consumption of the hot blast stove are taken as sample data to predict the future data in eight hours. The results show that the mean absolute error of the Lssvm prediction model with wavelet analysis has declined to 2.77%, the Update_Lssvm model is established to predict the accuracy of blast furnace gas generation date is 1. 55%, and blast furnace gas consumption of hot stove is 4.23%. The predict randomness problem of generation and consumption of blast furnace gas under the variable condition has been settled. Compared with other forecasting models, the prediction accuracy of the Update Lssvm model has been enhanced. The model not only has the generalization ability, but also provides a theoretical basis for optimal operation of blast furnace gas.
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| Key words: iron and steel work blast furnace gas dynamic prediction wavelet analysis energy saving |
