数据驱动的填料塔液泛气速预测模型与实时监测研究
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摘要
填料塔的液泛气速是塔的工作气速的上限。研究填料塔的液泛现象和预测填料塔的液泛气速,使填料塔在尽可能高的通量下平稳运行,避免发生由于对液泛失察或判断延误,导致溶液跑损、停产,形成巨额的经济损失。该研究工作对提高塔器的安全操作、经济效率提升以及节能减排都具有重要的意义,并有着广泛的工业应用前景。
在总结了传统的液泛气速经验模型以及液泛实时监测方法的基础上,本文详细介绍了新型的基于数据驱动方法,建立统一的填料液泛气速预测模型的算法及实现步骤；提出了通过监听填料床因液泛发生而形成的声波改变,实现液泛实时监测的的理论及方法。并通过相应的实验,对这些新方法的可行性进行了验证。
本文的创新性研究工作包括：
(1)对国内近几十年来发表的散堆填料实验的文献数据进行了全面的归纳总结,初步建立了相应的实验数据库。基于径向基神经网络推导出了液泛气速预测模型。探索采用支持向量机回归方法,建立了填料塔液泛气速预测模型。根据得到的填料塔的液泛气速预测模型,在MATLAB下开发了一个辅助预测软件,可预先估计填料的液泛气速。
(2)通过对填料塔两相对流时的声波发射进行实时监测实验,分析正常及液泛状态下,填料层上部的声波信号波动特性,分析得到了实验塔液泛状态的特征频率段位于180-200Hz区域。
(3)探索采用阵列麦克风采集填料层上部的声波信号变化,然后采用近场声全息方法,进行了数据计算并绘制了重建后的源面声压变化图。该方法克服了单麦克风采集所得仅为液泛区域声波变化平均值的缺点,能够更好地体现液泛区域各部位的声压变化,可以用于塔内局部区域液泛的研究。
最后总结了本文的主要研究成果,提出了需进一步探索和改进的研究方向。
The flooding gas velocity of packed column is the upper limit of its operating gas velocity. Research on the flooding of the packed column and forecasting its flooding gas velocity makes it possible that packed column can be operated steadily in the high capacity to avoid the loss and production halts of liquor due to the oversight or delayed judgment on flooding, which may lead to the tremendous economic loss. This research not only has great significance on the enhancement in safe operation of column, improvement in the economical efficiency, together with energy saving and emission reduction, but also has a wide prospect of industrial applications.
Based on the summarization concerning the traditional experience model of flooding gas velocity and the method of online monitoring of flooding, this paper specifically introduces the original approach based on data-driven and builds an algorithm regarding the gas velocity prediction model of flooding, and the corresponding steps. Moreover, it also puts forward monitoring the acoustic wave change which formed due to flooding in the packed bed to realize the real-time monitor, which is a new theory as well as a method. Furthermore, these new methods have been verified via corresponding experiments.
The encompassed innovative research of this paper is as follows:
1. It makes compressive summary on the domestic literature data which have been published in recent decades with regard to experiment of random packing, so the corresponding data base have initially been established. In view of fact that the RBF (Radical Basis Function) network has launched the prediction model on the flooding gas velocity, the method of SVM (Support Vector Machine) regression is applied to this exploration in order to build the flooding gas velocity model of packed column. According to the available flooding gas velocity model of packed column, a prediction software under MATLAB is developed, which can estimate the flooding gas velocity of packed column in advance.
2. Through the real-time monitoring experiment on the acoustic emission of the two-phase convection flow in packed columns and the analysis of the feature of acoustic wave signal above of the packed bed under the normal or flooding condition, it is found that the characteristic frequency of experimental column under flooding state is located at the range of180-200Hz.
3. The exploration adopts the microphone array to collect the change of acoustic wave signal which is above the packing bed. Afterwards, NAH (Nearfield Acoustic Holography) is used for computing and then, the rebuilt diagram about change of sound pressure has been drawn. This method overcomes the flaw that average amount of acoustic wave in flooding area is the only thing the single microphone collected, while this method can better reflect the change of sound pressure in any positions within the area of flooding, and it can used to research the flooding in the certain part of packed column.
Ultimately, this paper sums up the research findings, and presents the direction of research which needs to be explored and improved further.
在总结了传统的液泛气速经验模型以及液泛实时监测方法的基础上,本文详细介绍了新型的基于数据驱动方法,建立统一的填料液泛气速预测模型的算法及实现步骤；提出了通过监听填料床因液泛发生而形成的声波改变,实现液泛实时监测的的理论及方法。并通过相应的实验,对这些新方法的可行性进行了验证。
本文的创新性研究工作包括：
(1)对国内近几十年来发表的散堆填料实验的文献数据进行了全面的归纳总结,初步建立了相应的实验数据库。基于径向基神经网络推导出了液泛气速预测模型。探索采用支持向量机回归方法,建立了填料塔液泛气速预测模型。根据得到的填料塔的液泛气速预测模型,在MATLAB下开发了一个辅助预测软件,可预先估计填料的液泛气速。
(2)通过对填料塔两相对流时的声波发射进行实时监测实验,分析正常及液泛状态下,填料层上部的声波信号波动特性,分析得到了实验塔液泛状态的特征频率段位于180-200Hz区域。
(3)探索采用阵列麦克风采集填料层上部的声波信号变化,然后采用近场声全息方法,进行了数据计算并绘制了重建后的源面声压变化图。该方法克服了单麦克风采集所得仅为液泛区域声波变化平均值的缺点,能够更好地体现液泛区域各部位的声压变化,可以用于塔内局部区域液泛的研究。
最后总结了本文的主要研究成果,提出了需进一步探索和改进的研究方向。
The flooding gas velocity of packed column is the upper limit of its operating gas velocity. Research on the flooding of the packed column and forecasting its flooding gas velocity makes it possible that packed column can be operated steadily in the high capacity to avoid the loss and production halts of liquor due to the oversight or delayed judgment on flooding, which may lead to the tremendous economic loss. This research not only has great significance on the enhancement in safe operation of column, improvement in the economical efficiency, together with energy saving and emission reduction, but also has a wide prospect of industrial applications.
Based on the summarization concerning the traditional experience model of flooding gas velocity and the method of online monitoring of flooding, this paper specifically introduces the original approach based on data-driven and builds an algorithm regarding the gas velocity prediction model of flooding, and the corresponding steps. Moreover, it also puts forward monitoring the acoustic wave change which formed due to flooding in the packed bed to realize the real-time monitor, which is a new theory as well as a method. Furthermore, these new methods have been verified via corresponding experiments.
The encompassed innovative research of this paper is as follows:
1. It makes compressive summary on the domestic literature data which have been published in recent decades with regard to experiment of random packing, so the corresponding data base have initially been established. In view of fact that the RBF (Radical Basis Function) network has launched the prediction model on the flooding gas velocity, the method of SVM (Support Vector Machine) regression is applied to this exploration in order to build the flooding gas velocity model of packed column. According to the available flooding gas velocity model of packed column, a prediction software under MATLAB is developed, which can estimate the flooding gas velocity of packed column in advance.
2. Through the real-time monitoring experiment on the acoustic emission of the two-phase convection flow in packed columns and the analysis of the feature of acoustic wave signal above of the packed bed under the normal or flooding condition, it is found that the characteristic frequency of experimental column under flooding state is located at the range of180-200Hz.
3. The exploration adopts the microphone array to collect the change of acoustic wave signal which is above the packing bed. Afterwards, NAH (Nearfield Acoustic Holography) is used for computing and then, the rebuilt diagram about change of sound pressure has been drawn. This method overcomes the flaw that average amount of acoustic wave in flooding area is the only thing the single microphone collected, while this method can better reflect the change of sound pressure in any positions within the area of flooding, and it can used to research the flooding in the certain part of packed column.
Ultimately, this paper sums up the research findings, and presents the direction of research which needs to be explored and improved further.
引文
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