Refinery System Process Analysis & Testing using Agent Based Simulation
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Program Professional Framework
Refinery System Process Analysis & Testing using Agent Based Simulation
Program Overview
Refineries are networks of interacting units, tanks, pipelines, ships and decision makers, and many operational problems only appear when these elements interact over time. This programme shows how agent-based simulation (ABS) can represent such systems, test operating policies and expose bottlenecks before changes are made on site. Participants learn to define agents and their rules, build models in a general-purpose simulation tool, combine agent logic with discrete-event elements, validate results against plant data and run experiments on crude logistics, tankage and unit interactions.
Learning Outcomes
- ✓Decompose a refinery subsystem into agents, states, rules and interactions suitable for simulation.
- ✓Build a working agent-based model of crude receipt, tankage and unit feed with realistic constraints.
- ✓Compare agent-based, discrete-event and system dynamics approaches and select the right one for a given question.
- ✓Validate a model against historical operating data and document assumptions and limitations.
- ✓Design and run what-if experiments to test operating policies such as tank allocation rules or maintenance timing.
Target Audience
- Process and operations engineers in refineries
- Planning, scheduling and debottlenecking specialists
- Digital transformation and digital twin teams in downstream companies
- Industrial engineers and simulation analysts
Detailed Program Modules
01Simulation Approaches for Refinery Systems
- Agent-based, discrete-event and system dynamics compared
- Typical refinery questions suited to each approach
- Limits of steady-state process simulators
02Defining Agents and Behaviour
- Units, tanks, ships and operators as agents
- State charts, decision rules and communication
- Representing uncertainty and stochastic events
03Model Construction
- Building agents and environments in a simulation tool
- Linking agent logic with process yields and capacities
- Data inputs, parameter tables and model structure
04Verification and Validation
- Debugging, animation and trace checks
- Calibration against historical operating data
- Documenting assumptions and confidence limits
05Experimentation and Analysis
- Scenario design and parameter variation
- Monte Carlo replications and result statistics
- Bottleneck identification and sensitivity analysis
06Applying Results in Operations
- Testing tankage, logistics and maintenance policies
- Integrating simulation into digital twin initiatives
- Communicating results to operations and management
Training Methodology
Applied Project & Assessment
Applied Project
Participants build and validate an agent-based model of a refinery crude handling subsystem covering ship arrivals, tank allocation and crude unit feed, then test two alternative operating policies and present the impact on throughput and tank utilisation.
Assessment
Short diagnostic at the start, practical tasks linked to each learning outcome, and evaluation of the applied project against published criteria.