Join our team as a Process Simulation Twin and Optimization Engineer to deploy and sustain online process simulations, perform process optimization activities, and develop advanced process optimization data-enabled solutions. This role requires a strong background in process engineering and digitalization. You will be responsible for bridging the gap between process engineering and digitalization beyond traditional process simulation roles.
Key Highlights
Key Responsibilities
Technical Skills Required
Benefits & Perks
Nice to Have
Job Description
This position requires full relocation to Saudi Arabia. It is a permanent full time Expat position with an attractive relocation package.
Please note only qualified candidates will be contacted.
Overview:
We are seeking an experienced Process Simulation Twin (PST) and Optimization Engineer to join our Reliability Solutions Department (RSD) within our Global Manufacturing Excellence Center.
Global Manufacturing Excellence (GME) is the recognized Center of Excellence driving value across Saudi Aramco’s Downstream Global manufacturing portfolio.
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Your primary role is to deploy and sustain online process simulations (process digital twins), perform process optimization activities to identify opportunities, monitor process performance KPIs and develop advanced process optimization data-enabled solutions. This role sits at the boundary of process engineering and digitalization beyond traditional process simulation roles. You will be responsible for bridging the gap between rigorous thermodynamic models, process digital twins, real-time process data and hybrid analytics through AI.
Key Responsibilities:
As the successful candidate you will be required to perform the following:
- Lead the development, deployment, and sustainment of PST that integrate real-time process data with rigorous equation-oriented or sequential simulation engines in alignment with corporate objectives.
- Configure and troubleshoot the connectivity between plant historians, laboratory information systems, and simulation software to ensure seamless data ingestion and reconciliation.
- Utilize PST to perform automated monitoring of facility health, detecting deviations between ideal performance and actual plant operations to identify opportunity gaps.
- Develop and maintain thermodynamic models and mathematical equations that accurately represent the behavior of various processes ensuring the PST accurately reflects the current state of the facility (including heat exchanger fouling, catalyst decay, etc.).
- Perform offline engineering studies using PST to analyze complex operational problems, debottleneck constraints, and conduct "what-if" scenarios based on current operating conditions.
- Act as the technical liaison between data teams and Operations/Process Engineering teams, leading technical sessions on model architecture, data quality, and commissioning.
- Lead process optimization sessions to de-bottleneck key process units such as CDU/VDU, Hydrocrackers, Catalytic crackers, Diesel Hydrotreaters, Isomerization.
- Benchmark process optimization KPIs with 15+ operating facilities and provide development of programs, processes, guidelines and best practices.
- Provide subject matter expertise to operating facilities Operations, Maintenance, and Engineering teams by consulting, providing awareness sessions, and participating and leading technical exchanges and conferences.
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Minimum Requirements:
- You must hold a Bachelor’s degree in Chemical Engineering.
- You will have a minimum of 15 years of experience in Process Simulations, Online Modeling, or Real-Time Optimization (preferably ROMeo). 20 years of experience is preferred.
- You will be able to Demonstrate expertise in process engineering and flowsheet-based processes simulation. Preferred Equation Oriented simulation.
- You will have comprehensive understanding of refinery facilities, feedstock/product movements, margins, giveaways, process yields, and mass and energy balances.
- Demonstrated experience connecting simulations to live data sources (Data Historians, OPC, etc.).
- Strong understanding of data reconciliation, model tuning, and dealing with gross errors in industrial data is also required.
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