Digital Twins and AI for Refineries and Gas Plants
التوائم الرقمية والذكاء الاصطناعي في المصافي ومصانع الغاز
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- Available in-house
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- Formats
- In-house training0 sessions
- Duration
- 5 daysCertificate
Overview
A digital twin is a live model of a plant that combines process simulation, equipment models and real-time data, so engineers can see what is happening, test changes safely and predict what will happen next. In refineries and gas plants, twins are now used to optimise energy and throughput, monitor equipment health, train operators and support turnaround planning, and AI is making them faster and easier to keep up to date. This course explains what a digital twin really is, how it is built and where it creates value. Participants learn the building blocks: first-principles and data-driven models, data integration from historians and laboratory systems, model calibration and the role of AI and hybrid models. Real applications are examined unit by unit, followed by the practical questions of cost, data quality, model maintenance, cybersecurity and how to run a twin project that operations will actually use.
Objectives
- Define the types and maturity levels of digital twins
- Explain how process simulation, equipment models and real-time data are combined
- Describe the role of machine learning and hybrid models in a twin
- Identify high-value twin applications in refineries and gas plants
- Assess data, infrastructure and model maintenance requirements
- Plan a digital twin project with clear value measures
- Recognise common reasons why twin projects fail
Who should attend
Process, operations and optimisation engineers, reliability engineers, digital transformation teams and managers in refineries, gas plants and petrochemical facilities.
Training methodology
Participants interact with a working demonstration twin of a gas processing train built from a steady-state simulator and live-style historian data. They calibrate the model, compare predictions with plant data, and use it to test operating changes. Case studies from operators illustrate energy optimisation, equipment health and operator training applications. Group exercises focus on selecting use cases and defining a project scope, and teams present a twin roadmap for one of their own units.
Programme outline
Day1Digital Twin Concepts
- What a digital twin is and is not
- Twin types: asset, process and system twins
- Maturity levels from monitoring to autonomy
- The digital thread and data flows
- Industry examples in refining and gas
- Live demonstration: a gas plant twin
- Discussion: where twins add value
Day2Building Blocks
- Steady-state and dynamic process simulation
- Equipment performance models
- Historian, LIMS and maintenance data integration
- Data validation and reconciliation
- Model calibration and tuning
- Practical exercise: calibrating a twin to plant data
- Interpreting model-to-plant gaps
Day3AI and Hybrid Models
- Machine learning surrogate models
- Hybrid first-principles and data models
- Soft sensors and inferential models
- Anomaly detection within a twin
- Keeping models current as the plant changes
- Practical exercise: building a soft sensor
- Case study: AI-assisted optimisation
Day4Applications
- Energy and throughput optimisation
- Equipment health and fouling monitoring
- Operator training simulators
- Turnaround and what-if planning
- Emissions monitoring
- Practical exercise: testing an operating change on the twin
- Case studies from refineries and LNG plants
Day5Delivering a Twin Project
- Use case selection and value case
- Architecture, cloud and cybersecurity
- Vendor selection and contracting
- Organisation, ownership and adoption
- Common failure reasons
- Team presentations: twin roadmap
- Final assessment
Dates and fees
There are no scheduled dates for this course at the moment. Request it as in-house training, or ask us about the next date.
Certificate
Every delegate who attends the full programme receives a Harfard Technology certificate of completion stating the course title, duration, dates and venue.
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