Multi‑objective process optimization for CO2 capture using metamodels

IFP Energies nouvelles (IFPEN) est un acteur majeur de la recherche et de la formation dans les domaines de l’énergie, du transport et de l’environnement. De la recherche à l’industrie, l’innovation technologique est au cœur de son action, articulée autour de quatre priorités stratégiques : Mobilité Durable, Energies Nouvelles, Climat / Environnement / Economie circulaire et Hydrocarbures Responsables.

Dans le cadre de la mission d’intérêt général confiée par les pouvoirs publics, IFPEN concentre ses efforts sur :

• l’apport de solutions aux défis sociétaux de l’énergie et du climat, en favorisant la transition vers une mobilité durable et l’émergence d’un mix énergétique plus diversifié ;
• la création de richesse et d’emplois, en soutenant l’activité économique française et européenne et la compétitivité des filières industrielles associées.

Partie intégrante d’IFPEN, l’école d’ingénieurs IFP School prépare les générations futures à relever ces défis.

Multi‑objective process optimization for CO2 capture using metamodels

Post‑combustion CO2 capture, utilization and storage (CCUS) is an IPCC‑recommended approach to reduce anthropogenic CO2 emissions and mitigate global warming. Among mature post-combustion CO2 capture technologies, amine scrubbing has been extensively researched over the past decade for its proven industrial readiness and robustness.

However, the capital and operating costs (CAPEX and OPEX) of amine-based capture process are strongly driven by the packed absorber design. Key challenges include large packing volumes, pressure drop across packing which increases fan power demand, reboiler energy requirement for solvent regeneration, and operational robustness. Design choices – such as packing type and geometry, column diameter, solvent flow rate and quality, and intercooling – create trade‑offs between CO2 capture efficiency and capital and operating costs.

Optimizing the absorber design therefore requires a multi‑objective approach. High‑fidelity process simulations are computationally expensive, making exhaustive design-space exploration impractical. Metamodels (also called surrogate models) offer a promising way to reduce computational cost, but robust training methods and practical strategies for deploying these models to industrial processes are still in development.

Description

This internship aims to develop and validate a methodology for optimizing an amine‑wash CO2 absorber using metamodels, applied to two industrial case studies (an NGCC power plant and a cement plant). The workflow will be built on in-house numerical tools: a rate-based simulator and a surrogate‑model construction toolkit.

The work performed will include:

• Identifying the relevant design and operating variables;
• Designing an efficient Design of Experiments (DOE) and running the simulations with our rate-based calculation code;
• Training and evaluating metamodels with appropriate cross-validation;
• Performing multi‑objective optimizations to obtain and analyze Pareto-optimal solutions and quantify the associated improvements in absorber performance (e.g., lower packing volume, reduced packing pressure drop, minimized material use, etc.).

In a second phase, the methodology may be used to characterize an ideal packing (surface area and mass transfer coefficients) that would optimize absorber performance.

Required profile

Final-year Master’s student (M2) in Process or Chemical Engineering (or equivalent) with strong interest in modelling and simulation.

Perks for candidates

• Hands-on data-driven modelling
• Multi-objective process optimization
• Equipment sizing and cost evaluation
• Direct connection to industrial decarbonation challenges

Additional information
- Internship duration: 5-6 months
- Workplace: IFPEN Lyon, Rond-point de l'échangeur de Solaize, 69360 Solaize
- Transport: Public transportation / personal vehicle
- Paid internship: 1150€/month (gross)
- To apply: Send CV + short motivation to supervisors