Freelancer profile translated to English.
Back to original languageAbout Younes
Digital Twin & Industrial Process Modeling | Phy-Chem-Bio Simulation · Python · ProsimPlus · AspenPlus · ProII · gPROMS · ML
Process engineer with a Ph.D. in chemical engineering, I intervene where physicochemical and biological complexity exceeds standard tools: mechanistic, hybrid, or data-driven modeling, dynamic simulation, digital twins, and intelligent exploitation of industrial data.
My involvement covers the entire digital continuum of processes:
- Modeling & Simulation — mechanistic, hybrid, or data-driven (phy-chem-bio) modeling, static & dynamic, coded in Python / Julia / Fortran or via industrial simulators (AspenPlus, gPROMS, SUMO, West, ProII, Matlab/Simulink)
- Analytics & Industrial Data — SCADA/lab data exploitation, troubleshooting, diagnostics, virtual sensors, predictive models
- Digital Twin — design, prototyping, and deployment of digital solutions on real systems (TRL 3→6), Python software architecture, versioning, API
- Digital R&D Strategy — data roadmap definition, R&D team management, maturation of innovative solutions
- Training — Industry 4.0 (CESI), Python, Data Science, SQL (ASCENT)
What sets me apart: I start from the process equations: mass/energy balances, reaction kinetics, thermodynamics of solutions and multiphase systems; before applying data and machine learning. I am not a data scientist adapting to industry:I am a process engineer who masters digital from end to end.
Sector-agnostic by training and experience: hydrometallurgy (thesis), Oil&Gas, three-phase catalysis, chemistry and pharmaceuticals (Solvay, Adisseo, Daikin, Oril, SNCZ), water treatment (SIAAP).
Remote work primarily, occasional travel possible. Available for consulting, applied R&D, or training missions, on a time and materials or fixed-price basis.
French
Native or bilingual
English
Fluent
Remote only
Primarily works remotely
Experience
- SAS Chemin du RoiData Engineering & Industrial IoT Consultant - Biogas Digital TwinENERGY AND UTILITIESSeptember 2025 - January 2026 (4 months)Beauvais, France
Design and deployment of a complete data infrastructure for a biogas unit with biomethane injection into the grid.
Infrastructure & ConnectivityHybrid OT/IT architecture connecting the secure industrial network to a Cloud VPS. Implementation of an OpenVPN tunnel for process data access, pfSense network configuration, TimescaleDB deployment for high-frequency time-series storage.Multi-Source ETL Pipelines -Development of three independent collection agents:- REST API synchronization (process inputs, inputs) per minute + Consumption of an MQTT Sparkplug broker
- SFTP retrieval of NatranGaz injection data with AM/BMP/AMJ consolidation logic,
- Service via python script for Zeiss NIR spectra via OPCUA.
- Retrieval of electricity consumption data by setting up a ticmaster on the meter + MODBUS
- CI/CD via GitHub Actions with self-hosted runner.
Data Model- Design of an optimized dimensional schema: TimescaleDB hypertables for time series,
- PostgreSQL arrays storage for high-dimensionality spectra,
- Double temporality structure for ML predictions (prediction_timestamp / target_timestamp) allowing for model drift analysis.
Monitoring & SecurityHeartbeat via Healthchecks.io, API rate limiting, request validation, secure credential management via environment variables.Stack: Python, bash, PostgreSQL/TimescaleDB, MQTT, OPCUA, Modbus, REST API, SFTP, OpenVPN, Docker, GitHub Actions, StreamlitObjective: Lay the data foundations for a hybrid digital twin (AM2+ ML) for biogas production optimization and enzyme dosage strategy evaluation. - SIAAP – Direction InnovationProcess Modeling & Digital Twin Manager | Python · Simulation · Virtual SensorsENVIRONMENTALAugust 2024 - Today (2 years)Colombes, France
Scientific lead for digital modeling within the largest sanitation authority in Europe (9 million inhabitants).
- Scientific animation and supervision of projects in process modeling and regulation within the framework of the Mocopée and MeSeine programs with partner laboratories.
- Management of a team of 4 to 8 people, development of Python + SQL unit simulators and digital twins to reduce regulatory non-conformities and generate virtual sensors (GHG).
- Implementation of GitFlow, Power BI dashboards, and Streamlit applications.
- Development of a predictive river oxygen model to estimate the impact of discharges during rainfall.
- Refactoring of existing tools to make them maintainable and deployable (refactoring, python transition, pep8, versioning, and modular architecture).
- INEVO Technologie, filiale d'ORANOR&D Process Engineer – Digital TwinsCHEMICALNovember 2022 - July 2024 (1 year and 8 months)Lyon, FranceProjects carried out for: Solvay, Adisseo, Daikin, Oril, SNCZ — process modeling, digital twins, and thermodynamic simulation, with advanced deployment of the OIAnalytics platform.
- Training of 40+ engineers in advanced methods (AspenPlus, ACM) and Python upskilling; ProSim Plus referent.
- Development and deployment of a digital twin for a major agri-food company.
- Modeling of electrolytic precipitation for feasibility studies (ORANO).
- Writing a literature review on gas-liquid reactive flows and technical watch on gas-liquid equipment.
- Thermodynamic diagnosis of high-pressure polymer contamination; sizing of a minimal purge solution under Aspen Plus and evaluation of the installation to go higher in pressure and lower in temperature.
- Expert use of the OIAnalytics platform (industrial data analytics, modeling, digital twin deployment) — internal referent.
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Education
- Ph.D.University of Pau2022Ph.D.
- Engineering DegreeENSGTI2019Diplôme d'Ingénieur –