Rotterdam, Netherlands  ·  MSc Industrial Ecology

Salih Ali —
systems thinker,
engineer.

Mechanical engineering background. Currently studying how industrial systems can transition toward sustainability — energy, materials, institutions.

Before the MSc I worked in gas turbine efficiency — where I developed an instinct for system-level optimisation at the component level. The Industrial Ecology programme has pushed that instinct outward: from components to industrial systems, and from optimisation to transition.

My thesis interest sits at the intersection of technical interventions and institutional mechanisms — specifically, what conditions allow ecological gains to actually persist rather than being absorbed or reversed.

I'm open to internships and part-time technical roles in energy, sustainability consulting, maritime, or chemicals where an engineering background and a systems lens are useful.

2025–26

Saba Island 100% Renewable Transition ↗

Group project (4 people). Individual contributions: built the RAMP stochastic demand model from scratch (50 Monte Carlo runs, 5 user categories, 9,003 MWh/yr median); independently corrected and ran the Calliope energy optimisation model (4.72 MW solar, 0.577 MW wind, 9.8 MWh battery); caught a capacity mismatch between Calliope outputs and the financial model before submission. Full system stress-tested against a 5-day hurricane blackout scenario.

Python RAMP Calliope Energy systems
2025–26

RECLAIM × Zorluteks — Circular Retrofit Assessment

Assessed long-term viability of a digital circular retrofit strategy for a Turkish textile manufacturer. Contributed risk framework and three commercialisation routes including sustainability-linked finance.

Circular economy Strategy SLL finance
2025–26

PolyCycle Park — Industrial Symbiosis Modelling

Built energy and material balance diagrams (Linny-R) for EO/EG and PET plants in an industrial symbiosis park scenario.

Linny-R MFA Industrial symbiosis
2025–26

Climate & Carbon Modelling — Canada & Global ↗

Two-part analysis: (1) Canada temperature trend analysis using Berkeley Earth data — polar amplification, radiative forcing, and 2050 projection with R²=0.89 regression. (2) Six-pool dynamic carbon model (atmosphere, land, ocean) simulating BAU vs mitigation scenarios to 2100 using a Runge–Kutta solver; sensitivity analysis identified ocean uptake rate as dominant uncertainty (±15%).

Python Climate modelling Carbon cycle NOAA data Berkeley Earth

Modelling

  • Python / Jupyter
  • RAMP (stochastic)
  • Calliope (optimisation)
  • Linny-R (MFA)

Methods

  • Life Cycle Assessment
  • Material Flow Analysis
  • Research methods
  • Policy analysis

Domains

  • Energy transitions
  • Circular economy
  • Industrial ecology
  • Gas turbines / thermal

Open to internship and part-time opportunities in energy, sustainability, and related technical fields.