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A simple Markovian model for the initial stage of the formation of carbon molecular sieves by carbon deposition

机译:通过碳沉积形成碳分子筛初始阶段的简单马尔可夫模型

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One type of molecular sieves is carbon molecular sieves (CMSs). Activated carbons, with pores of relatively uniform pore-size distribution, which can be produced from various sources including biomass, are suitable as substrates to manufacture CMSs. This is accomplished by narrowing the pores' mouths of such activated carbons through deposition of fine particles of carbon onto them. These carbon particles are generated by decomposing a gaseous carbon source. The formation of CMSs proceeds stochastically, which is mainly attributable to mesoscopic size and complex motion of the depositing carbon particles and random distribution of the pores on the activated carbons. Herein, CMS formation is modeled as a Markovian process, specifically, a pure-birth process with a constant intensity of transition. Such a pure-birth process can possibly lead to the simplest stochastic model for the kinetics of CMS formation, especially at the initial stage. In spite of its simplicity, this model is useful for preliminary exploration of stochastic characteristics of CMS formation. The model gives rise to the master equation of the pure-birth process as well as the governing equations for the mean and variance of the random variable characterizing CMS formation. To validate the model, the analytical solutions of these equations have been compared with available experimental data; the model is in excellent accord with the data. Meanwhile, the analysis of these available data through stochastic modeling has revealed that they are highly accurate.
机译:一种类型的分子筛是碳分子筛(CMS)。可以从包括生物质在内的各种来源生产的具有相对均匀的孔径分布的孔的活性炭适合作为制造CMS的基质。这是通过使细小的碳颗粒沉积在活性炭的孔口上而使其狭窄而实现的。这些碳颗粒是通过分解气态碳源而产生的。 CMS的形成是随机进行的,这主要归因于介孔尺寸和沉积碳颗粒的复杂运动以及活性炭上孔的随机分布。在本文中,CMS的形成被建模为马尔可夫过程,特别是具有恒定过渡强度的纯出生过程。这种纯出生过程可能会导致CMS形成动力学的最简单的随机模型,尤其是在初始阶段。尽管它很简单,但是该模型可用于初步探索CMS形成的随机特征。该模型产生了纯出生过程的主方程以及表征CMS形成的随机变量的均值和方差的控制方程。为了验证模型,将这些方程的解析解与可用的实验数据进行了比较;该模型与数据非常吻合。同时,通过随机建模对这些可用数据的分析表明,它们是高度准确的。

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