基于LSTM神经网络的燃煤锅炉热效率预测方法
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摘要
锅炉燃烧过程属于持续性工艺流程,当前运行工况参数会受到前N个周期的工况叠加影响。本文收集锅炉负荷、省煤器出口氧量、各二次风挡板开度、燃尽风挡板开度、各磨煤机给煤量、炉膛与风箱差压、一次风总风压、锅炉运行中排出的煤灰和煤渣的含碳量等参数,形成时间序列样本集,构建LSTM神经网络模型,用于预测燃煤锅炉热效率。该方法能够挖掘并记忆锅炉连续运行过程中参数自身变化与热效率影响的客观规律,克服锅炉持续性燃烧调整的工况叠加带来的预测误差,提高学习效率,提升预测精度。
The boiler combustion is a continuous technological process,and the current operating conditions parameters will be affected by the superposition of the first N cycles. This paper collected boiler parameters such as boiler load,economizer outlet oxygen,secondary air baffle opening,burnout baffle opening,coal supply to each coal mill,furnace and bellows differential pressure,primary wind total pressure,carbon content of coal ash and cinder discharged during boiler operation.Then a time series sample set was formed and an LSTM neural network model was constructed to predict the thermal efficiency of the coal-fired boiler. The method could excavate and memorize the objective law of the influence of parameters itself and the thermal efficiency during the continuous operation of the boiler. It also overcome the prediction error brought by the superposition of the continuous combustion adjustment of the boiler,improved the learning efficiency and the prediction accuracy.
The boiler combustion is a continuous technological process,and the current operating conditions parameters will be affected by the superposition of the first N cycles. This paper collected boiler parameters such as boiler load,economizer outlet oxygen,secondary air baffle opening,burnout baffle opening,coal supply to each coal mill,furnace and bellows differential pressure,primary wind total pressure,carbon content of coal ash and cinder discharged during boiler operation.Then a time series sample set was formed and an LSTM neural network model was constructed to predict the thermal efficiency of the coal-fired boiler. The method could excavate and memorize the objective law of the influence of parameters itself and the thermal efficiency during the continuous operation of the boiler. It also overcome the prediction error brought by the superposition of the continuous combustion adjustment of the boiler,improved the learning efficiency and the prediction accuracy.
引文
[1]BG/T 10180-2003,工业锅炉热工性能试验规程[S].
[2]吴从容,黎华,李茂东等.燃煤工业锅炉热效率快速测试方法分析[J].能源与环境,2012(4):23-24.
[3]关晓光,蒋贺,宋吉明.燃煤工业锅炉能效测试方法的改进研究[J].东北电力大学学报,2011,31(2):14-17.
[4]王宏志,陈帅,侍洪波.基于最小二乘支持向量机和PSO算法的电厂烟气含氧量软测量[J].热力发电,2008,37(3):35-38.
[5]高芳,翟永杰,卓越等.基于共享最小二乘支持向量机模型的电站锅炉燃烧系统的优化[J].动力工程学报,2012,32(12):928-933.
[6]高明明,刘吉臻,高明帅等.基于补偿模糊神经网络的灰系统控制研究[J].动力工程学报,2012,32(7):532-537.
[7]牛玉广,沙超,康俊杰.基于数值模拟与试验运行数据的电站锅炉燃烧系统复合建模[J].动力工程学报,2014,34(10):765-770.
[8]牛培峰,刘永超,张先臣等.基于改进人工蜂群算法的锅炉NOx排放预测优化[J].热能动力工程,2014,29(4):427-433.
[9]李越胜,江政纬,甘云华等.基于PSO-LSSVM算法的燃油工业锅炉效率软测量[J].自动化技术与应用,2017,36(10):41-45.
[10]徐齐胜,罗胜琪,陶欣等.基于神经网络遗传算法的锅炉燃烧优化系统[J].自动化与仪表,2014,29(6):30-32.
[2]吴从容,黎华,李茂东等.燃煤工业锅炉热效率快速测试方法分析[J].能源与环境,2012(4):23-24.
[3]关晓光,蒋贺,宋吉明.燃煤工业锅炉能效测试方法的改进研究[J].东北电力大学学报,2011,31(2):14-17.
[4]王宏志,陈帅,侍洪波.基于最小二乘支持向量机和PSO算法的电厂烟气含氧量软测量[J].热力发电,2008,37(3):35-38.
[5]高芳,翟永杰,卓越等.基于共享最小二乘支持向量机模型的电站锅炉燃烧系统的优化[J].动力工程学报,2012,32(12):928-933.
[6]高明明,刘吉臻,高明帅等.基于补偿模糊神经网络的灰系统控制研究[J].动力工程学报,2012,32(7):532-537.
[7]牛玉广,沙超,康俊杰.基于数值模拟与试验运行数据的电站锅炉燃烧系统复合建模[J].动力工程学报,2014,34(10):765-770.
[8]牛培峰,刘永超,张先臣等.基于改进人工蜂群算法的锅炉NOx排放预测优化[J].热能动力工程,2014,29(4):427-433.
[9]李越胜,江政纬,甘云华等.基于PSO-LSSVM算法的燃油工业锅炉效率软测量[J].自动化技术与应用,2017,36(10):41-45.
[10]徐齐胜,罗胜琪,陶欣等.基于神经网络遗传算法的锅炉燃烧优化系统[J].自动化与仪表,2014,29(6):30-32.