On the application of self-organizing neural networks in gas-liquid and gas-solid flow regime identification
On the application of self-organizing neural networks in gas-liquid and gas-solid flow regime identification
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DOI：
10.1590/s1678-58782010000100003
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发表时间：
2010-03-01
影响因子：
2.2
通讯作者：
Seleghim Jr., Paulo
中科院分区：
文献类型：
--
作者：
Barbosa, P. R.;Crivelaro, K. C. O.;Seleghim Jr., Paulo
与多相流的传输和操纵相关的主要问题之一是流态的存在，它对重要的运行参数有很大的影响。这种情况的一个例子发生在气-液化学反应器中，其中可以通过保持分散气泡流动状态以最大化总界面面积来获得最大反应系数。另一个例子是固体的气力输送，其中的制度与安全和能源消耗相关。因此，自动识别流态的能力非常重要，特别是为了维持多相系统根据设计条件运行。这项工作评估了自组织图（神经网络）的使用，该图适用于水平两相流中的流态识别问题。为了获得广泛的结果，考虑了两种不同类型的两相流：气-固和气-液。使用在圣卡洛斯圣保罗大学热与流体工程实验室的实验设施中收集的数据进行了测试，以验证神经网络模型的 pet：性能。结果表明，神经网络能够正确识别政权。当分析具有与用作训练数据的流量不同的流量的相同状态时，错误百分比更大，这强调了训练信号选择的重要性。
One of the main problems associated with the transport and manipulation of multiphase flow is the existence of flow regimes, which have a strong influence on important parameters of operation. An example of this occurs in gas-liquid chemical reactors in which maximum coefficients of reaction can he attained by keeping a dispersed-bubbly flow regime to maximize the total interfacial area. Another example is the pneumatic conveying of solids in which the regimes are associated with safety and energy consumption. Thus, the ability to identify flow regimes automatically is very important, specially to maintain multiphase systems operating according to design conditions. This work assesses the use of a self-organizing map (neural network) adapted to the problem of regime identification in horizontal two-phase flows. In order to achieve extensive results, two different types of two-phase flows were considered: gas-solid and gas-liquid. Tests were made to verify the pet:performance of the neural network model, using data collected at the experimental facilities of the Thermal and Fluid Engineering Laboratory of the University of Sao Paulo at Sao Carlos. Results show that the neural network is capable of correctly identifying the regimes. The error percentage is bigger when analyzing the same regime with flow rates different from the one used as training data emphasizing the importance of training signals choice.