Digital Twins for Asset Performance: Soft Sensors, Hybrid Models and What-If Simulation – Dubai, 21–25 Jun 2027

Programme overview

Introduction:

Digital twins for asset performance often stop at a three-dimensional viewer or a dashboard: models drift from the plant, virtual measurements go unchecked and nobody can show what a what-if run changed in operating or maintenance decisions. This Core Concept course equips reliability, process and digital engineers to choose the right twin type, build physics-based, data-driven and hybrid models, deploy soft sensors, calibrate and validate them against field data, and govern the twin through its life. Participants produce a Digital Twin Use-Case Design and Business Case for a rotating asset or process unit.

Course Objectives:

• Classify a proposed digital twin by scope, type and maturity level using ISO/IEC 30173 concepts and the prototype, instance and aggregate distinction
• Specify the data flows linking historian, CMMS or EAM records and field IoT streams to an asset or process twin
• Select physics-based, data-driven or hybrid modelling for a given equipment behaviour and justify the choice against available data and required fidelity
• Design, calibrate and validate soft sensors and virtual measurements for quantities that are costly or impossible to measure directly
• Run what-if simulations on efficiency, throughput and maintenance timing and translate the results into operating and intervention decisions
• Build a twin business case with value-tracking metrics, lifecycle ownership and cyber protection measures for sponsor approval

Target Audience:

• Reliability engineers who analyse equipment degradation and define performance limits for critical machines
• Maintenance engineers who plan interventions on pumps, compressors, turbines and heat exchangers
• Process engineers who optimise unit throughput, energy use and product quality
• Digital and automation engineers who integrate plant data sources and deploy simulation models
• Asset performance analysts who monitor efficiency deviations and report equipment health

Course Outline:

Day 1: Digital Twin Scope, Types and Maturity Levels

• ISO/IEC 30173 Terms: Physical Entity, Digital Representation and Digital Thread
• Digital Twin Prototype, Instance and Aggregate for Equipment Fleets
• Asset Twin Versus Process Twin: Boundaries, Granularity and Update Rates
• Twin Maturity Ladder from Descriptive Replica to Prescriptive Advisor
• Current-State Twin Readiness Audit for a Plant Equipment Register

Day 2: Twin Reference Architecture and Plant Data Integration

• ISO 23247 Framework Entities: Observable Element, Data Collection and Twin Entity
• Historian Tag Structures, Sampling Rates and Compression Effects on Model Inputs
• CMMS and EAM Integration: Equipment Hierarchy, Work History and Failure Codes
• Field IoT Gateways, Edge Pre-Processing and Contextualised Asset Metadata
• Twin Data Contract: Tag Mapping, Units, Latency and Ownership Matrix

Day 3: Physics-Based, Data-Driven and Hybrid Model Building

• First-Principles Performance Curves for Pumps, Compressors and Heat Exchangers
• Regression and Neural Network Surrogates Trained on Operating Envelopes
• Hybrid Grey-Box Modelling: Physics Core With Data-Driven Residual Correction
• Soft Sensor Design for Fouling Factor, Polytropic Efficiency and Product Quality
• Kalman Filter State Estimation for Noisy and Missing Plant Measurements

Day 4: Calibration, Validation, What-If Simulation and Twin Governance

• Parameter Calibration Against Test-Run and Steady-State Plant Data
• Validation Metrics, Acceptance Bands and Model Drift Recalibration Triggers
• What-If Scenarios: Load Changes, Washing Intervals and Overhaul Deferral
• Twin Lifecycle Governance: Model Versioning, Change Control and Model Owner Roles
• Cyber Protection for Twin Data Paths: Segmentation, Access Rights and Write-Back Limits

Day 5: Capstone: Digital Twin Use-Case Design and Business Case

• Case Data Pack Review: Centrifugal Compressor Train or Crude Preheat Exchanger Network
• Use-Case Canvas Build: Decision Supported, Model Type and Required Inputs
• Soft Sensor and Hybrid Model Prototype Specification for the Case Unit
• Value Model Build: Energy Savings, Throughput Gain, Deferred Overhaul and Running Cost
• Use-Case Design and Business Case Defence Before a Technical Review Panel

Skills You Will Gain:

• Twin Scope Definition
• Plant Data Contract Design
• Hybrid Grey-Box Modelling
• Soft Sensor Engineering
• Model Calibration and Validation
• Scenario Simulation Analysis
• Twin Lifecycle Governance
• Twin Value Tracking

Why Attend This Course:

• Leave with a Digital Twin Use-Case Design and Business Case for a rotating asset or process unit comparable to your own
• Test whether a twin proposal from an integrator or internal team has the data, fidelity and ownership to stay useful after go-live
• Practise building virtual measurements and hybrid equipment models on realistic operating data
• Compare twin practice with engineers from oil and gas, petrochemicals, power generation, water and mining operations

Conclusion:

A digital twin earns its place when its models stay calibrated to the plant, its virtual measurements are trusted and its what-if results change real operating and maintenance decisions. The course moves from twin types and maturity, through reference architecture and plant data integration, to physics-based, data-driven and hybrid modelling with soft sensors, then to calibration, scenario simulation, governance and cyber protection. The final day produces a Digital Twin Use-Case Design and Business Case ready for technical and sponsor review.