Simulation study of a gas-diffusion electrocrystallization process for PGMs recovery.

This poster presents research conducted within the FIREFLY project, which aims to develop circular and sustainable technologies for recovering value-added metals from industrial waste and spent catalysts. The focus is on Gas-diffusion Electrocrystallization (GDEx), an electrochemical method for recovering platinum group metals (PGMs), including Pt, Pd, and Rh. The study combines simulation and experimental validation to model the electrogeneration of reductants that promote nanoparticle precipitation. A parametric study was conducted, varying operating conditions such as concentration, flow rate, and current density, resulting in over 2,000 theoretical results. These results support the development of a machine learning algorithm for decision-making in catalyst stream treatment, contributing to the sustainable evolution of the catalyst-based industry.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-09-09
DOI
https://doi.org/10.5281/zenodo.17121985
Primary Topic
Geochemistry and Geologic Mapping
Type
article
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article

Simulation study of a gas-diffusion electrocrystallization process for PGMs recovery.

Zenodo (CERN European Organization for Nuclear Research)
Geochemistry and Geologic Mapping
article

Simulation study of a gas-diffusion electrocrystallization process for PGMs recovery.

article en

Abstract

This poster presents research conducted within the FIREFLY project, which aims to develop circular and sustainable technologies for recovering value-added metals from industrial waste and spent catalysts. The focus is on Gas-diffusion Electrocrystallization (GDEx), an electrochemical method for recovering platinum group metals (PGMs), including Pt, Pd, and Rh. The study combines simulation and experimental validation to model the electrogeneration of reductants that promote nanoparticle precipitation. A parametric study was conducted, varying operating conditions such as concentration, flow rate, and current density, resulting in over 2,000 theoretical results. These results support the development of a machine learning algorithm for decision-making in catalyst stream treatment, contributing to the sustainable evolution of the catalyst-based industry.

Zenodo (CERN European Organization for Nuclear Research)
Flemish Government (BE), Flemish Institute for Technological Research (BE)
Industry, innovation and infrastructure
Openalex Percentile: Top 41%
Geochemistry and Geologic Mapping
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