钢铁企业罩式炉装炉优化及煤气柜位预测问题研究与应用
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摘要
近几年随着钢铁工业综合自动化技术及节能技术的普及,制造执行系统和能源管理系统已在各大型钢铁企业成功应用,作为系统核心的生产计划编制及能源平衡调度水平直接影响到企业的用能效果。本文依托国家“863计划”课题,分析了钢铁企业资源和能源优化的主要问题及特点,对钢铁生产过程中的罩式炉装炉优化和煤气系统柜位在线预测问题开展了深入系统的研究工作。主要内容如下：
研究了冷轧过程中罩式炉装炉优化问题。首先将钢卷进行拼卷,接着对待装炉钢卷进行有效组合。在优化拼卷阶段：建立了多目标多背包拼卷模型,利用系统聚类法对钢卷分类来确定模型的背包中心,并构造了一种自适应量子遗传算法快速获得最优的拼卷结果。在最优装炉组合阶段：建立了多炉型、不确定炉数下的装炉组合多背包模型。利用拉格朗日松弛启发式算法求得装炉数的上界,并构造了一种新型单亲遗传算法进而求解,得到耗能最少的装炉优化组合。
研究了炼焦过程焦炉煤气系统的柜位预测问题,通过分析焦炉煤气的产消及柜位变化特点,建立了基于最小二乘支持向量机的柜位预测模型。考虑参数和样本对模型精度的影响,构造了梯度网格搜索算法优选模型参数和大样本筛选方法选取训练样本。
研究了炼铁过程高炉煤气系统的柜位在线预测问题,考虑实际高炉煤气柜位经常频繁大幅度变化,将其归结为一般非平坦变化的非线性回归问题。提出了基于简约梯度法的多核学习最小二乘支持向量机回归建模方法,该方法能较快建立基于丰富混合特征空间的柜位预测模型,充分预测各种柜位变化。
研究了炼钢过程转炉煤气系统的多柜位预测问题,提出了一种一致T型灰色关联分析方法,确定柜位的主要影响用户。根据多个煤气柜同时并网运行的情况,提出了多输出最小二乘支持向量机回归算法,构建柜位预测模型。通过将其转换为求解线性方程组,快速得到模型的权系数和偏置公式表示,从而准确预测各转炉煤气柜位的变化。
基于上述问题的研究,结合软件工程方法,开发了罩式炉装炉优化及煤气柜位预测系统,通过在上海宝钢股份有限公司冷轧薄板厂和能源中心的运行情况表明：该系统可以提高钢铁企业的资源和能源利用水平,达到了降低生产成本、节约能源、减轻环境污染的效果。
With the popularization of integrated automation and energy technology in steel industry, the manufacturing executive system and energy management system have been successfully applied in steel industry. As the core function of MES and EMS, the ability of production planning and energy balance scheduling make a direct influence on the utilization effect of enterprise's resource and energy. Based on the projects of National High-Tech Research and Development Programme, the main problems and characteristics of resource and energy optimization in steel plant are analyzed in this dissertation, in which the optimal charging for bell-type annealing furnace and the on-line prediction for gas holder level in steel production process are studied in detail. The dissertation has mainly carried on the following researches.
The optimal charging for batch-type annealing furnace in cold rolling process is studied. Firstly, the coils are merged, and then the charging coils are effectively combinated. At the stage of coils merging, a multi-objective and multi-knapsack model is established, based on which, a clusters method acquires the classification of coils and the knapsack model's center. And an adaptive quantum genetic algorithm is developed to obtain the optimal coils merging results rapidly. At the stage of optimal charging combination, a multi-knapsack model with multi-furnace type and uncertain furnace numbers is suggested, where the furnace numbers upper limit is obtained by lagrangian relaxation heuristic algorithm and a new partheno genetic algorithm is proposed for solving the model. The charging results with the least energy consumption are obtained.
The prediction problem for coke oven gas holder level in coke-making process is studied. A prediction model based on the least square support vector machine is established through analyzing the change characteristics of gas production-consumption and holder level. Considering the influence of model's parameters and samples on the prediction precision, a gradient grid search algorithm is proposed to opitimize the model's parameters, and an effective big samples selection method is suggested to build the training samples.
The on-line prediction problem for blast furnace gas holder level in steel-making process is studied. Considering the practical level with frequent and great fluctuation, this problem is regarded as a general nonlinear regression problem with non-flat variation. A new multiple kernel learning based least square support vector regression is constructed based on a reduced gradient algorithm, which can rapidly give the resulting regressor under the optimal linear combination of kernels for predicting the various change trend of holder level.
The multi-holder level prediction problem for linz donaniz gas in iron-making process is studied. A consistent T's grey relation analysis is developed to determine the main influencing users of holder level. According to the parallel running mode of several holders in practice, a multi-output least square support vector machine regression algorithm is proposed to establish the prediction model, in which the model's weighted values and bias are fast given by solving a series of linear equation system. The developed model can accurately predict the level trend of each LDG holder.
Based on the studies mentioned above, two software systems for bell-type annealing charging optimization and gas holder level prediction are developed in combination with software engineering technology. The application in cold rolling plant and energy cernter of Shanghai Baosteel Co. Ltd. show that the system can improve the level of the utilization of resource and energy in steel enterprise, decrease the production cost and reduce the enviorenment pollution.
研究了冷轧过程中罩式炉装炉优化问题。首先将钢卷进行拼卷,接着对待装炉钢卷进行有效组合。在优化拼卷阶段：建立了多目标多背包拼卷模型,利用系统聚类法对钢卷分类来确定模型的背包中心,并构造了一种自适应量子遗传算法快速获得最优的拼卷结果。在最优装炉组合阶段：建立了多炉型、不确定炉数下的装炉组合多背包模型。利用拉格朗日松弛启发式算法求得装炉数的上界,并构造了一种新型单亲遗传算法进而求解,得到耗能最少的装炉优化组合。
研究了炼焦过程焦炉煤气系统的柜位预测问题,通过分析焦炉煤气的产消及柜位变化特点,建立了基于最小二乘支持向量机的柜位预测模型。考虑参数和样本对模型精度的影响,构造了梯度网格搜索算法优选模型参数和大样本筛选方法选取训练样本。
研究了炼铁过程高炉煤气系统的柜位在线预测问题,考虑实际高炉煤气柜位经常频繁大幅度变化,将其归结为一般非平坦变化的非线性回归问题。提出了基于简约梯度法的多核学习最小二乘支持向量机回归建模方法,该方法能较快建立基于丰富混合特征空间的柜位预测模型,充分预测各种柜位变化。
研究了炼钢过程转炉煤气系统的多柜位预测问题,提出了一种一致T型灰色关联分析方法,确定柜位的主要影响用户。根据多个煤气柜同时并网运行的情况,提出了多输出最小二乘支持向量机回归算法,构建柜位预测模型。通过将其转换为求解线性方程组,快速得到模型的权系数和偏置公式表示,从而准确预测各转炉煤气柜位的变化。
基于上述问题的研究,结合软件工程方法,开发了罩式炉装炉优化及煤气柜位预测系统,通过在上海宝钢股份有限公司冷轧薄板厂和能源中心的运行情况表明：该系统可以提高钢铁企业的资源和能源利用水平,达到了降低生产成本、节约能源、减轻环境污染的效果。
With the popularization of integrated automation and energy technology in steel industry, the manufacturing executive system and energy management system have been successfully applied in steel industry. As the core function of MES and EMS, the ability of production planning and energy balance scheduling make a direct influence on the utilization effect of enterprise's resource and energy. Based on the projects of National High-Tech Research and Development Programme, the main problems and characteristics of resource and energy optimization in steel plant are analyzed in this dissertation, in which the optimal charging for bell-type annealing furnace and the on-line prediction for gas holder level in steel production process are studied in detail. The dissertation has mainly carried on the following researches.
The optimal charging for batch-type annealing furnace in cold rolling process is studied. Firstly, the coils are merged, and then the charging coils are effectively combinated. At the stage of coils merging, a multi-objective and multi-knapsack model is established, based on which, a clusters method acquires the classification of coils and the knapsack model's center. And an adaptive quantum genetic algorithm is developed to obtain the optimal coils merging results rapidly. At the stage of optimal charging combination, a multi-knapsack model with multi-furnace type and uncertain furnace numbers is suggested, where the furnace numbers upper limit is obtained by lagrangian relaxation heuristic algorithm and a new partheno genetic algorithm is proposed for solving the model. The charging results with the least energy consumption are obtained.
The prediction problem for coke oven gas holder level in coke-making process is studied. A prediction model based on the least square support vector machine is established through analyzing the change characteristics of gas production-consumption and holder level. Considering the influence of model's parameters and samples on the prediction precision, a gradient grid search algorithm is proposed to opitimize the model's parameters, and an effective big samples selection method is suggested to build the training samples.
The on-line prediction problem for blast furnace gas holder level in steel-making process is studied. Considering the practical level with frequent and great fluctuation, this problem is regarded as a general nonlinear regression problem with non-flat variation. A new multiple kernel learning based least square support vector regression is constructed based on a reduced gradient algorithm, which can rapidly give the resulting regressor under the optimal linear combination of kernels for predicting the various change trend of holder level.
The multi-holder level prediction problem for linz donaniz gas in iron-making process is studied. A consistent T's grey relation analysis is developed to determine the main influencing users of holder level. According to the parallel running mode of several holders in practice, a multi-output least square support vector machine regression algorithm is proposed to establish the prediction model, in which the model's weighted values and bias are fast given by solving a series of linear equation system. The developed model can accurately predict the level trend of each LDG holder.
Based on the studies mentioned above, two software systems for bell-type annealing charging optimization and gas holder level prediction are developed in combination with software engineering technology. The application in cold rolling plant and energy cernter of Shanghai Baosteel Co. Ltd. show that the system can improve the level of the utilization of resource and energy in steel enterprise, decrease the production cost and reduce the enviorenment pollution.
引文
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