Digital Twin Models for Oil Refineries: A Task-Oriented Classification
Why Traditional Digital Twin Classifications Are Not Enough?
Oil refineries require different classes of digital twin models for process simulation, production planning, and operational decision support because engineering decisions are made at different organizational levels. Traditional digital twin classifications focus on model fidelity, simulation technology, or implementation maturity. While these approaches describe how a model is built, they do not explain which engineering decisions it supports.
In petroleum refining, model architecture is driven by the production task, not by the level of detail. Optimizing a process unit, coordinating refinery material flows, executing a production plan, and managing a refinery network require fundamentally different refinery simulation models. This article proposes an alternative classification based on engineering objectives rather than model fidelity. Together, these model classes support refinery simulation at every level of engineering decision-making. Digital twin models should be classified by the engineering decisions they support rather than by simulation fidelity or implementation maturity.
In petroleum refining, model architecture is driven by the production task, not by the level of detail. Optimizing a process unit, coordinating refinery material flows, executing a production plan, and managing a refinery network require fundamentally different refinery simulation models. This article proposes an alternative classification based on engineering objectives rather than model fidelity. Together, these model classes support refinery simulation at every level of engineering decision-making. Digital twin models should be classified by the engineering decisions they support rather than by simulation fidelity or implementation maturity.
Each model class extends the capabilities of the previous one by supporting a broader class of engineering decisions in refinery operations.
A Task-Oriented Classification of Refinery Digital Twins
The proposed classification consists of four complementary model classes. Each class addresses a different level of engineering decision-making.
As production tasks become more complex, the required model architecture also changes. The transition between model classes is driven by new engineering objectives, not by increasing model fidelity or adding more equipment to the simulation.
This article classifies refinery digital twins into four model classes:
As production tasks become more complex, the required model architecture also changes. The transition between model classes is driven by new engineering objectives, not by increasing model fidelity or adding more equipment to the simulation.
This article classifies refinery digital twins into four model classes:
- Functional Object Models — Individual refinery assets
- Interaction Models — Interconnected refinery assets
- Enterprise Models — Entire refinery
- Enterprise Network Models — Multi-refinery production networks
Functional Object Models
Functional Object Models represent individual refinery assets. Their primary objective is to optimize the operation of a single technological object within a refinery process. Typical engineering tasks include throughput optimization, operating mode selection, maintenance planning, and inventory management. These models focus on local operational performance rather than refinery-wide objectives.
At this level, the key engineering question is: How should this technological object operate to achieve its production objectives?
At this level, the key engineering question is: How should this technological object operate to achieve its production objectives?
Interaction Models
Interaction Models analyze how refinery assets influence one another through refinery material flows, process constraints, and shared resources. Typical engineering tasks include bottleneck analysis, flow redistribution, maintenance impact assessment, feedstock allocation. These models reveal how local operating decisions propagate through interconnected refinery assets. At this level, the key engineering question is:
How do refinery objects interact, and how do their operating decisions affect the overall process?
How do refinery objects interact, and how do their operating decisions affect the overall process?
Enterprise Models
These models treat the refinery as a single integrated production system. Their objective is not to optimize individual assets, but to ensure that the entire refinery operates efficiently while meeting production targets and process constraints. Typical engineering tasks include production planning, refinery scheduling, material balance, production optimization, and refinery-wide optimization. At this level, the key engineering question is:
How can the refinery achieve its production objectives as a whole?
How can the refinery achieve its production objectives as a whole?
Enterprise Network Models
Enterprise Network Models extend the digital twin beyond a single oil refinery to represent an entire refining network. Their objective is to optimize production planning, logistics, supply chain optimization, and resource allocation across multiple facilities while considering shared infrastructure and business constraints.
Typical engineering tasks include crude allocation, inter-refinery product transfers, pipeline and terminal optimization, supply chain planning, production coordination, and network-wide optimization. Optimization is performed at the enterprise level rather than for an individual refinery.
At this level, the key engineering question is:
How can the refining network be optimized?
Typical engineering tasks include crude allocation, inter-refinery product transfers, pipeline and terminal optimization, supply chain planning, production coordination, and network-wide optimization. Optimization is performed at the enterprise level rather than for an individual refinery.
At this level, the key engineering question is:
How can the refining network be optimized?
A Unified Refinery Digital Twin
The four model classes are not alternatives but complementary building blocks of a refinery digital twin.
A Functional Object Model can be used independently to optimize a single process unit or tank farm. As the scope expands to include interactions between multiple facilities, Interaction Models become necessary. Enterprise Models support refinery-wide production planning and optimization, while Enterprise Network Models extend decision-making across multiple refineries and the entire supply chain.иTogether, these model classes provide a scalable framework for developing industrial digital twin solutions for oil refineries, from individual process units to enterprise-wide refinery networks, using Petroleum Refining Library and AnyLogic.
A Functional Object Model can be used independently to optimize a single process unit or tank farm. As the scope expands to include interactions between multiple facilities, Interaction Models become necessary. Enterprise Models support refinery-wide production planning and optimization, while Enterprise Network Models extend decision-making across multiple refineries and the entire supply chain.иTogether, these model classes provide a scalable framework for developing industrial digital twin solutions for oil refineries, from individual process units to enterprise-wide refinery networks, using Petroleum Refining Library and AnyLogic.
Conclusion
The proposed task-oriented classification shows that refinery digital twin architecture should be driven by engineering objectives rather than simulation fidelity. As engineering challenges evolve, digital twins should expand by introducing new classes of models rather than simply increasing model detail.
FAQ
1. What is a refinery digital twin?
A refinery digital twin is a virtual representation of refinery assets, processes, and operations used for process simulation, production planning, optimization, and engineering decision support.
2. Is a refinery digital twin a single simulation model?
No. A refinery digital twin typically consists of multiple specialized models designed for different engineering and operational tasks.
3. How should refinery digital twins be classified?
Rather than classifying digital twins by model fidelity or simulation technology, they can be classified by the engineering tasks they are designed to solve.
4. What are the main classes of refinery digital twin models?
The proposed classification includes Functional Object Models, Interaction Models, Enterprise Models, and Enterprise Network Models.
5. What is a Functional Object Model?
A Functional Object Model represents an individual refinery asset, such as a process unit or tank farm, and is used to optimize its operation.
6. What is an Interaction Model?
An Interaction Model simulates material flows and operational dependencies between multiple refinery objects to analyze bottlenecks and process interactions.
7. What is an Enterprise Model?
An Enterprise Model represents the entire refinery and supports production planning, material balance, refinery optimization, and operational decision-making.
8. What is an Enterprise Network Model?
An Enterprise Network Model extends the digital twin to multiple refineries and supports crude allocation, logistics optimization, and supply chain planning.
9. Why isn't model fidelity enough to classify digital twins?
Two models with similar levels of detail may solve completely different engineering problems. The required model architecture depends on the production task rather than simulation fidelity.
10. Can multiple digital twin models work together?
Yes. Functional Object Models, Interaction Models, Enterprise Models, and Enterprise Network Models complement each other and together form a comprehensive refinery digital twin.
11. How does Petroleum Refining Library support refinery digital twins?
Petroleum Refining Library provides reusable simulation components for developing refinery digital twins in AnyLogic, covering process simulation, production planning, logistics, and refinery optimization.
12. Why is AnyLogic suitable for refinery digital twins?
AnyLogic combines discrete-event, agent-based, and system dynamics simulation, making it well suited for building scalable refinery digital twins ranging from individual process units to enterprise-wide production systems.
13. What is refinery simulation?
Refinery simulation is the use of mathematical and discrete-event models to analyze refinery processes, production planning, material flows, and operational performance.
14. What is refinery optimization?
Refinery optimization combines process optimization, production planning, logistics optimization, and mathematical optimization to improve production planning, resource utilization, logistics, and profitability.
15. Why is process simulation important in oil refineries?
Process simulation helps engineers evaluate operating scenarios, production capacity, material balance, and process constraints before implementing operational changes.
16. What is the difference between refinery simulation and refinery optimization?
Refinery simulation predicts refinery behavior under different operating conditions, while refinery optimization identifies operating strategies that maximize production, profitability, or resource utilization. In practice, optimization often relies on simulation models for validation and decision support.
A refinery digital twin is a virtual representation of refinery assets, processes, and operations used for process simulation, production planning, optimization, and engineering decision support.
2. Is a refinery digital twin a single simulation model?
No. A refinery digital twin typically consists of multiple specialized models designed for different engineering and operational tasks.
3. How should refinery digital twins be classified?
Rather than classifying digital twins by model fidelity or simulation technology, they can be classified by the engineering tasks they are designed to solve.
4. What are the main classes of refinery digital twin models?
The proposed classification includes Functional Object Models, Interaction Models, Enterprise Models, and Enterprise Network Models.
5. What is a Functional Object Model?
A Functional Object Model represents an individual refinery asset, such as a process unit or tank farm, and is used to optimize its operation.
6. What is an Interaction Model?
An Interaction Model simulates material flows and operational dependencies between multiple refinery objects to analyze bottlenecks and process interactions.
7. What is an Enterprise Model?
An Enterprise Model represents the entire refinery and supports production planning, material balance, refinery optimization, and operational decision-making.
8. What is an Enterprise Network Model?
An Enterprise Network Model extends the digital twin to multiple refineries and supports crude allocation, logistics optimization, and supply chain planning.
9. Why isn't model fidelity enough to classify digital twins?
Two models with similar levels of detail may solve completely different engineering problems. The required model architecture depends on the production task rather than simulation fidelity.
10. Can multiple digital twin models work together?
Yes. Functional Object Models, Interaction Models, Enterprise Models, and Enterprise Network Models complement each other and together form a comprehensive refinery digital twin.
11. How does Petroleum Refining Library support refinery digital twins?
Petroleum Refining Library provides reusable simulation components for developing refinery digital twins in AnyLogic, covering process simulation, production planning, logistics, and refinery optimization.
12. Why is AnyLogic suitable for refinery digital twins?
AnyLogic combines discrete-event, agent-based, and system dynamics simulation, making it well suited for building scalable refinery digital twins ranging from individual process units to enterprise-wide production systems.
13. What is refinery simulation?
Refinery simulation is the use of mathematical and discrete-event models to analyze refinery processes, production planning, material flows, and operational performance.
14. What is refinery optimization?
Refinery optimization combines process optimization, production planning, logistics optimization, and mathematical optimization to improve production planning, resource utilization, logistics, and profitability.
15. Why is process simulation important in oil refineries?
Process simulation helps engineers evaluate operating scenarios, production capacity, material balance, and process constraints before implementing operational changes.
16. What is the difference between refinery simulation and refinery optimization?
Refinery simulation predicts refinery behavior under different operating conditions, while refinery optimization identifies operating strategies that maximize production, profitability, or resource utilization. In practice, optimization often relies on simulation models for validation and decision support.