欢迎登录材料期刊网

材料期刊网

高级检索

钢铁企业自备电厂是副产煤气的主要缓冲用户,在消纳富余煤气、减少煤气放散、实现煤气平衡方面发挥着极为重要的作用.充分考虑自备电厂煤气供入量特点,建立了HP-Elman-LSSVM预测模型,并根据自备电厂能源利用的特点,建立拟合模型求解自备电厂锅炉的经济运行负荷,在此基础上对供入自备电厂的煤气进行优化调度.将该模型应用于具体企业,实现了钢铁企业自备电厂煤气预测和优化调度.模型应用表明:所建模型对自备电厂煤气供入量30、45、60个点的预测平均相对误差分别为1.9%、1.4%、1.4%,能有效解决实际生产中自备电厂煤气供入量预测不准问题.并通过煤气优化调度,自备电厂可大幅度提升蒸汽产率,应用企业每年可多产蒸汽约8.1322万t,折合节约标煤9443.955t.

参考文献

[1] Akimoto K;Sannomiya N;Nishikawa Y .An Optimal Gas Supply for a Power Plant Using a Mixed Integer Programrning Model[J].AUTOMATICA,1991,27(03):513.
[2] 韩明荣.优化煤气系统动态平衡[J].钢铁,2001(04):5-7,16.
[3] 张建良,王妤.钢铁企业煤气系统的优化利用模型[J].包头钢铁学院学报,2002(03):280-282.
[4] 李俊岭,温浩,郭占成,许志宏.二甲醚:钢铁厂剩余煤气化工利用的一种较好选择[J].钢铁,2001(12):66-69.
[5] Kim J H;Yi H S;Han C .Plant-Wide Multiperiod Optimal Energy Resource Distribution and Byproduct Gas Holder Level Control in the Iron and Steel Making Process Under Varying Energy Demands[J].Process Systems Engineering,2003,15(02):882.
[6] Kirn J H;Yi H S;Han C .Optimal Byproduct Gas Distribution in the Iron and Steel Making Process Using Mixed Integer Programming[J].International Symposium on Advanced Control of Industrial Processes,2002,581:586.
[7] 刘颖,时飞飞,赵珺,王伟,丛力群,冯为民.基于改进回声状态网络的高炉煤气产耗预测[J].系统仿真学报,2011(10):2184-2189.
[8] Hodrick R J;Prescott E C;Postwar U S .Business Cycles:an Empirical Investigation[J].Journal of Money Credit and Banking,1997,29(01):1.
[9] Packard N H;Crutchfietd J P;Farmer J D et al.Geometry From a Time Series[J].Physical Review Letters,1980,45(09):712.
[10] Takens E .Deterrning Strange Attractors in Turbulence[J].Lecture Notes in Mathematics,1981,898:361.
[11] Cheng Y C;Qi W M;Cai W Y.Dynamic Properties of Elman and Modified Elman Neural Network[A].北京,2002:637.
[12] 李锡杰;师硕;王旭 .基于Elman神经网络的机电信号分类[J].人工智能,2006,22(08):305.
[13] Pham D T;Liu X .Training of Elman Networks and Dynamic System Modeling[J].International Journal of Systems Science,1996,27(02):2212.
[14] 何玉婉 .基于Elman神经网络的高速公路入口匝道预测控制仿真研究[D].西南交通大学,2008.
[15] 任丽娜 .基于Elman神经网络的中期电力负荷预测模型研究[D].兰州理工大学,2007.
[16] 吕飞,沈振中.基于Elman神经网络的面板堆石坝沉降预测模型[J].水电能源科学,2011(12):56-59.
[17] Xiang Li;Guanrong Chen;Zengqiang Chen;Zhuzhi Yuan .Chaotifying linear Elman networks[J].IEEE Transactions on Neural Networks,2002(5):1193-1199.
[18] Cheng-Yuan Liou;Jau-Chi Huang;Wen-Chie Yang .Modeling word perception using the Elman network[J].Neurocomputing,2008(16/18):3150-3157.
[19] Gao X Z;Gao X M;Ovaska S J.Trajectory Control Based on a Modified Elman Neural Network[A].Orlando,1997
[20] Vapnik V N.The Nature of Statistical Learning Theory[M].New York:Springer Verlag,1999
[21] Suykens J A K;Gestel T V;Brabanter J D.Least Squares Support Vector Machines[M].Singapore:World Sci entific Pub Co,2002
上一张 下一张
上一张 下一张
计量
  • 下载量()
  • 访问量()
文章评分
  • 您的评分:
  • 1
    0%
  • 2
    0%
  • 3
    0%
  • 4
    0%
  • 5
    0%