Multi‑objective process optimization for CO2 capture using metamodels

IFP Energies nouvelles - Lyon; Solaize; On-site; EUR 12,000 - 14,000; Part time

Benefits offered by this job

Hands‑on data‑driven modelling

Multi‑objective process optimization

Equipment sizing and cost evaluation

Direct connection to industrial decarb

Job summary

IFP Energies nouvelles - Lyon is seeking a Final-year Master’s student in Process or Chemical Engineering for a 5–6 month internship focused on multi‑objective optimization of an amine‑wash CO2 absorber using metamodels. You will work with rate‑based simulators and surrogate‑model toolkits to explore design variables and improve absorber performance.

The role offers hands‑on data‑driven modelling, equipment sizing and cost evaluation, and a direct link to industrial decarbonation challenges.

Qualifications

• Final-year Master’s student (M2) in Process or Chemical Engineering with strong modelling interest.
• Interest in computational modelling and simulation for process optimization.
• Motivation to work on industrial decarbonation challenges.

Responsibilities

• Identify design and operating variables relevant to absorber optimisation.
• Design an efficient DOE and run simulations with rate‑based tools.
• Train and evaluate metamodels with cross‑validation.
• Perform multi‑objective optimisations and analyse Pareto‑optimal solutions.

Skills

Data‑driven modelling

Multi‑objective optimization

DOE design

Education

Master’s (M2) in Process or Chemical Engineering

Tools

Rate-based simulator

Surrogate‑model toolkit

Job description

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

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)