Refinery Simulation Fundamentals Articles and Tutorials
This page presents a collection of articles focused on simulation and hybrid modeling of oil and gas industry facilities. More detailed information is available in our Solutions section.
First published: June 3, 2026
What is Petroleum Refining Library?The Petroleum Refining Library for AnyLogic supports the development of digital twins for oil and gas facilities. It provides a comprehensive set of components and Java class templates for modeling process units, tank farms, sources, and material flows. To demonstrate the capabilities of the library, a simplified gas condensate processing plant model is presented. The model enables production planning, analysis of product flows, and evaluation of facility utilization, helping engineers make effective operational and strategic decisions.
First published: July 4, 2026
Fundamentals of Petroleum Refining SimulationPetroleum refining simulation enables the development of refinery digital twins that reproduce continuous material flows, process operations, storage, and production planning. This article introduces the fundamental principles of refinery simulation, including material balance, hybrid simulation, optimization, and decision support for validating production plans and improving refinery performance.
First published: July 5, 2026
Hybrid Simulation and Optimization for Petroleum Refinery Digital TwinsThis article explains how hybrid simulation and optimization complement each other in refinery digital twins. It shows how the Petroleum Refining Library (PRL) combines linear programming with continuous simulation to support production planning and refinery optimization.
First published: July 6, 2026
Material Balance in Refinery Digital TwinsMaterial balance in refinery digital twins is implemented as a distributed, node-level consistency mechanism that guarantees physical correctness across Sources, Process Units, and Reservoir Parks. Optimization defines target flows, while event-driven simulation enforces physically feasible execution under local constraints. Material balance acts as the coupling layer ensuring global consistency emerges from strictly balanced local dynamics across the entire refinery network.
First published: July 6, 2026
Why Tank Farms Make Refinery Simulation Difficult?Tank farms introduce a storage-dependent layer that couples continuous material flows with discrete logistics decisions, making refinery systems history-dependent rather than rate-driven. This coupling transforms classical material balance models into hybrid systems where inventory distribution directly affects routing, blending, and shipment feasibility. As a result, refinery optimization shifts from continuous LP formulations to hybrid optimization problems with discrete state constraints and evolving feasibility boundaries.
First published: July 7, 2026
Refinery Maintenance Simulation: Modeling Shutdowns and Repairs in Digital TwinsDiscover how refinery maintenance simulation enables digital twins to model scheduled shutdowns of process units and storage tanks. By automatically reconfiguring material flows and updating optimization constraints, engineers can evaluate production losses, storage limitations, and maintenance scenarios before they occur.
First published: July 16, 2026
Refinery Decision Support SystemA Refinery Decision Support System (DSS) enables engineers to evaluate operational decisions before implementation by integrating Digital Twin simulation, mathematical optimization, artificial intelligence, and engineering expertise into a unified decision-making environment. This article explains the architecture of a modern refinery DSS, its role in production planning, refinery scheduling, logistics, and operational risk assessment, and demonstrates how simulation-driven decision support improves refinery efficiency, planning accuracy, and overall operational performance.
First published: July 24, 2026
Plan Completion Status in Refinery Production PlanningThis article explains how PlanCompletionStatus standardizes production plan evaluation in refinery production planning and simulation. It describes the available completion states, their thresholds, and demonstrates how Source, ShipmentNode, and FlowQuota use a unified status model to simplify simulation logic and ensure consistent plan interpretation.
First published: July 28, 2026
Digital Twin Models for Oil Refineries: A Task-Oriented ClassificationThis article introduces a task-oriented classification of refinery digital twin models based on engineering decision levels. It explains how different model classes support process simulation, production planning, refinery optimization, and enterprise-wide decision support using Petroleum Refining Library and AnyLogic.
First published: July 28, 2026
Digital Twin Architecture for Refineries Using Petroleum Refining LibraryLearn how Petroleum Refining Library combines process simulation, analytical models, and decision support to build scalable refinery digital twins. Discover a multi-layer architecture that enables optimization, forecasting, and custom engineering applications within a single AnyLogic-based digital twin framework.
