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首页> 外文会议>Conference on process imaging for automatic control >Direct process estimation from tomographic data using artificial neural systems
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Direct process estimation from tomographic data using artificial neural systems

机译：使用人工神经系统的断层数据直接进程估计

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The paper deals with the goal of component fraction estimation in multi-component flows, a critical measurement in many process systems. Electrical Capacitance Tomography (ECT) is an attractive sensing technique for this task, due to its low-cost, non-intrusion and fast response. However, typical systems, which include practicable real-time reconstruction algorithms have shown to give inaccurate results and the existing approaches to direct component fraction measurement have a performance that is typically flow-regime dependent, and they fail to discriminate fractions in three-component flows. Such systems also depend upon an intermediate image that must be interpreted to yield useful plant data. In the investigation described, an artificial neural network approach has been used to directly estimate the component fractions in gas-oil, gas-water and gas-oil-water flows from ECT measurements. A two-dimensional finite-element electric field model of a 12-electrode ECT sensor has been used to simulate measurements in stratified, annular and bubble-flow conditions. The singular-value decomposition has been used to reduce the raw measurement data to a mutually independent set. Multi-Layer Feed-Forward Neural Networks (MLFFNNs) have been trained with sets of such reduced ECT data with their corresponding component fractions. The trained MLFFNNs have been tested with test patterns consisting of unlearned ECT data. The paper reviews results of the best-trained networks that give a mean absolute error of less than 1percent for the estimation of various multi-component fractions. The MLFFNNs' estimations are also compared with a direct ECT method proposed in one of the previous works. The direct ECT method gives larger mean absolute errors than the MLFFNNs, demonstrating that artificial neural systems provide more accurate component fraction estimations.
机译：本文涉及多组分流量组件分数估计的目标，许多过程系统中的临界测量。由于其低成本，非入侵和快速响应，电容断层扫描（ECT）是该任务的有吸引​​力的传感技术。然而，包括可行的实时重建算法的典型系统已经显示出不准确的结果，并且直接组件分数测量的现有方法具有通常依赖于流动制度的性能，并且它们不能在三个组件流中区分分数。这种系统还取决于必须解释的中间图像以产生有用的植物数据。在所述调查中，人工神经网络方法已被用于直接估计从ECT测量的汽油，天然气和气体流水中的组分分数。二维有限元电场模型用于模拟分层，环形和气泡条件下的测量。奇异值分解已被用于将原始测量数据降低到相互独立的集合。多层前馈神经网络（MLFFNNS）已被培训，其中如此减少的ECT数据，其相应的分量分数。训练有素的MLFFNNS已经用由未解析的ICT数据组成的测试模式进行了测试。本文评论最佳训练网络的结果，其表示估计各种多分量分数的平均绝对误差小于1的绝对误差。将MLFFNNS的估计与前一个工作之一中提出的直接IECT方法进行了比较。直接ECT方法提供比MLFFNN更大的平均绝对误差，证明人工神经系统提供更准确的组件分数估计。

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