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Neural modeling and control of a 13C isotope separation process

机译: 13 C同位素分离过程的神经建模和控制

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The paper presents a solution for the C isotope concentration control inside and at the output of a separation column, solution based on the Internal Model Control strategy. The C isotope results from a chemical exchange process carbon dioxide - carbamate, which is a distributed parameter process. In order to model the mentioned process, an original form of the approximating analytical solution which describes the process work in transitory regime is determined. The evolution of the approximating solution depends both on time and on the position from the column height. The reference model of the fixed part of the control structure is implemented using neural networks, representing an original solution due to the fact that a neural model is determined for a distributed parameter process. The controller is, also, implemented using neural networks, its main parameter being adapted in relation to the transducer position change in the separation column. The advantages of using the proposed concentration control strategy consist of: the possibility of controlling the value of the C isotope concentration in any point from the separation column height; the improvement of the system performance regarding the settling time; the possibility to reject the effect of the disturbances.
机译:本文提出了一种基于内部模型控制策略的解决方案,用于在分离柱内部和出口处控制C同位素浓度。 C同位素由二氧化碳-氨基甲酸酯的化学交换过程产生,这是一种分布参数过程。为了对所提到的过程进行建模,确定了描述临时状态下的过程工作的近似分析解决方案的原始形式。近似解的演化取决于时间和列高度的位置。控制结构固定部分的参考模型是使用神经网络实现的,由于为分布参数过程确定了神经模型,因此代表了一种原始解决方案。控制器也使用神经网络实现,其主要参数根据分离柱中换能器的位置变化进行调整。使用建议的浓度控制策略的优点包括:从分离柱高度控制任何点的C同位素浓度的可能性;关于建立时间的系统性能的改善;排除干扰影响的可能性。

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