Mesoscale modeling of the crystallization parameters identification during the iron-based catalyst preparation process: the dilute concentration case. Issue 13 (2nd October 2020)

• Record Type: Journal Article
• Authors: Xu, Dinghua; Li, Feixiang; Li, Xiaonian; Yao, Nan; Xu, Yinghong; Zhang, Qifeng
• Is Part Of: Applicable analysis. Volume 99:Issue 13(2020)
• Page Start: 2191
• Page End: 2209
• Publication Date: 2020-10-02
• Subjects: Mesoscale heat diffusion model -- stochastic inverse problem -- crystallization -- parameter identification -- Tikhonov regularization -- nucleation rate -- radial growth speed; 35R30 -- 60D05 -- 74N05 -- 65M32; Mathematical analysis -- Periodicals; 515
• DOI: 10.1080/00036811.2018.1555327
• Languages: English
• ISSNs: 0003-6811

Abstract

ABSTRACT: In this paper we model the mesoscale crystallization of the crystals F e ( O H ) 3 in the dilute solution concentration in the sense that the microscale model is a stochastic process and meanwhile the macroscale model is a deterministic heat diffusion process. The crystallization process includes the steps of nucleation, nucleus radial growth and nuclei aggregation. In the case of the dilute solution concentration we may neglect the effect of the nuclei aggregation, i.e. the crystallization neglects the impingement. The coupled model of crystallization and heat diffusion show the mesoscale characteristic between the micro- and macro- aspect. Furthermore we reformulate the simultaneous identification of nucleation rate and crystal growth speed in the stochastic birth-growth process by the inverse approaches. The regularization strategy is adopted to implement the numerical inversion of the inverse problem. Finally, numerical examples are provided to show the effectiveness of the proposed algorithm.