4.7 Article

A mechanistic model of direct forsterite carbonation

Journal

CHEMICAL ENGINEERING JOURNAL
Volume 404, Issue -, Pages -

Publisher

ELSEVIER SCIENCE SA
DOI: 10.1016/j.cej.2020.126480

Keywords

Carbon sequestration; Mineral carbonation; Reaction kinetics; Dynamic modeling; Simulation

Funding

  1. Federal Ministry of Education and Research (BMBF) in the CO2Min project [FKZ: 033RO14B]
  2. German Research Foundation (DFG) [MI 1851/3-1]

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Mineral carbonation is a promising method for sequestering large amounts of carbon dioxide and producing value-added substitutes, with dynamic models playing a crucial role in understanding carbonation reaction mechanisms. Through simulations, the study was able to quantify the influences of key process conditions on the reaction kinetics.
Mineral carbonation is a promising method to sequester large amounts of carbon dioxide and to produce value-added substitutes for the cement, paper, and plastic industries. In understanding carbonation reaction mechanisms in batch operation, dynamic models play a crucial role, and they allow to evaluate the effects of important process quantities such as temperature, pressure, particle size of solid phases and additives on reaction kinetics. We develop a mechanistic, dynamic forsterite carbonation model that accounts for gas, liquid, and multiple solid phases. In this model, gas-liquid and dissociation equilibria and surface-controlled reactions between solids and liquid phase are based on nonideal thermodynamics. We account for particle size distribution of raw material and product phases by formulating population balances considering nucleation and growth of particles. We model gas and liquid phases each as a homogeneous phase and we use isopotential conditions to describe equilibrium. The resulting high index system of differential and algebraic equations (DAE) is reformulated to obtain a DAE of differential index 1. Model predictions qualitatively match experimental data taken from literature and thus, we can quantify influences of key process conditions by simulation.

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