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Modeling nonlinear stochastic kinetic system and stochastic optimal control of microbial bioconversion process in batch culture

机译:批量培养中微生物转化过程的非线性随机动力学系统建模和随机最优控制

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In this paper, we analyze a stochastic model representing batch fermentation in the process of glycerol bio-dissimilation to 1,3-propanediol by klebsiella pneumoniae. The stochasticity in the model is introduced by parameter perturbation which is a standard technique in stochastic population modelling. Thus, based on the nonlinear deterministic dynamical system of glycerol bioconversion to 1,3-propanediol in batch culture, we present the stochastic version of the batch fermentation process driven by a five-dimensional Brownian motion and Lipschitz coefficients, which is suitable for the factual fermentation. Subsequently, we study the existence and uniqueness of solutions for the stochastic system as well as the boundedness and Markov property of solutions. Moveover a stochastic optimal control model is constructed and the sufficient and necessary conditions for optimality are proved via dynamic programming principle. Finally we present computer simulation for the stochastic system by using Stochastic Euler–Maruyama scheme. Compared with the results from the deterministic system, numerical results reveal the peculiar role of stochasticity in the dynamical responses of the batch culture.
机译:在本文中,我们分析了代表随机发酵的模型,该模型代表了肺炎克雷伯菌对甘油生物异化为1,3-丙二醇过程中的分批发酵。通过参数扰动引入模型中的随机性,参数扰动是随机总体建模的一种标准技术。因此,基于分批培养中甘油生物转化为1,3-丙二醇的非线性确定性动力学系统,我们提出了由五维布朗运动和Lipschitz系数驱动的分批发酵过程的随机版本,这适用于实际情况。发酵。随后,我们研究了随机系统解的存在性和唯一性,以及解的有界性和马尔可夫性质。建立了随机最优控制模型,并通过动态规划原理证明了最优性的充要条件。最后,我们通过使用随机欧拉-丸山方法对随机系统进行计算机仿真。与确定性系统的结果相比,数值结果揭示了随机性在分批培养的动力响应中的特殊作用。

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