A stochastic modelling approach for the characterisation of collision exchange processes. (2nd February 2022)

• Record Type: Journal Article
• Authors: Huang, Chunbing; Cattani, Federica; Piccione, Patrick M.; Galvanin, Federico
• Is Part Of: Chemical engineering science. Volume 248:Part A(2022)
• Publication Date: 2022-02-02
• Subjects: Collision-exchange process -- Compartment-based modelling -- Stochastic simulation -- Stochastic Markov process -- Seed coating system; Chemical engineering -- Periodicals; Génie chimique -- Périodiques; Chemical engineering; Periodicals; Electronic journals; 660
• DOI: 10.1016/j.ces.2021.117207
• Languages: English
• ISSNs: 0009-2509

Abstract

Graphical abstract: Highlights: A compartment-based stochastic model is given to study collision-exchange processes. Stochastic model allows to evaluate the distribution of material amount. Proposed model is applied to seed coating process in industry. Model prediction is in excellent agreement with experiment coating distributions. Model validity is given by identifying parameters from experimental observations. Abstract: A collision-exchange process is a common physical process where system members interact with each other to exchange materials and these individual interactions cumulatively drive a macroscopic system evolution in time. In this paper, a compartment-based stochastic model is formulated to study the collision-exchange process between members in a system. The discrete Markov analysis on the stochastic model presents the analytical results that show the independence of the system equilibrium on its initial distribution, and the derived differential equations reveal the deterministic time evolution of material amount on system members. As a specific example of a physical system that can be described via this model, a seed coating process is presented where the inter-particle coating variability is expressed by the stochastic model parameters. The promising agreement between simulation predictions and experimental results demonstrates the feasibility of stochastic modelling on the collision-exchange process and facilitates further model identification and applications to industrial processes.