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Grey-box Modeling of Ex-vivo Isolated Perfused Kidney

机译:Ex-Vivo孤立的灌注肾的灰盒建模

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Automatic control of kidney perfusion process in the ex-vivo normthermic condition requires an appropriate mathematical model of the kidney. Two nonlinear mathematical models, a tubuloglomerular feedback model (TGF) and myogenic model, which describe the autoregulation behavior on the nephron level were used as mathematical structures of two Grey-box models. The nominated parameters of each Grey-box model were optimized based on experimental rat data obtained in the ex-vivo normothermic perfused condition. According to the defined matching index, the Grey-box model of the kidney based on myogenic mechanism model (myogenic-based model) fits 80.11% of the measured data, while the Grey-box model of the kidney based on TGF mechanism model (TGF-based model) fits 62.32%. The myogenic-based model was able superbly to model the nonlinear autoregulation of the kidney in the ex-vivo isolated perfused condition, furthermore, it required less computational effort in comparison with the TGF-based model which makes the myogenic-based model more practical to be used in a model-based controller.
机译:肾脏灌注过程中的肾脏灌注过程的自动控制需要适当的肾脏数学模型。两种非线性数学模型,微管间反馈模型(TGF)和肌遗传学模型,描述了肾脏水平上的自动调节行为用作两个灰度箱模型的数学结构。基于在前体内常温灌注条件下获得的实验大鼠数据进行了每个灰度盒模型的指定参数。根据定义的匹配指数,基于肌遗传机制模型的肾脏灰盒模型(基于肌基模型)拟合了80.11%的测量数据,而基于TGF机制模型的肾脏灰盒模型(TGF基于模型)适合62.32%。基于肌遗传学的模型能够卓越地模拟肾脏中的肾脏中的非线性自灌注条件,而且与基于TGF的模型相比,它需要更少的计算努力,这使得基于TGF的模型更加实用用于基于模型的控制器。

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