摘要
针对钢铁生产流程中能耗预测模型建立困难、预测精度低等问题,提出了一种基于蚁群优化的小波神经网络的钢铁生产物流能耗预测模型,首先对钢铁生产过程以及影响生产能耗的因素进行分析,确定输入参数构成特征空间,然后利用小波变换重构特征空间,接着利用神经网络模型建立能耗预测模型,最后采用蚁群算法对预测模型参数进行优化.在炼铁、炼钢以及轧钢工序的能耗预测实验表明,提出的方法具有较好的普适性,提高了预测精度,为钢铁企业提前了解能耗需求提供了指导.
To overcome the difficulties of establishing a prediction model of energy consumption and to improve the predicting accuracy,a prediction model of material and energy consumption in steel production based on ant colony optimization wavelet neural network is proposed in this paper.The steel production process and the factors which impact energy consumption are firstly analyzed.The input parameters are then determined to constitute fea-ture space,which is reconstructed by wavelet transform.A prediction model of energy consumption with neural network is constructed thereafter.Finally the solving process is optimized by using ant colony algorithm.The ener-gy consumption prediction experiments in the processes of iron -making,steel -making and steel -rolling show that the proposed method has better universality,and at the same time it improves the prediction accuracy and provides guidance for steel enterprises to understand energy needs in advance.
出处
《昆明理工大学学报（自然科学版）》
CAS
2015年第2期80-87,共8页
Journal of Kunming University of Science and Technology(Natural Science)
基金
国家自然科学基金资助项目(61175068
61163004)
关键词
钢铁生产过程
能耗分析
能耗预测
蚁群优化算法
小波神经网络
steel production process
energy consumption analysis
energy prediction
ant colony optimization
wavelet neural network
