How Tanks Are Modeled: Accumulative and Flowing Tank Farms?
Tank Farm Series:
1. Tank Farm Placement in Oil Refinery Material Flow Simulation Models
2. Tank Farm Operating Strategy in Refineries: How Storage Levels Control Unit Throughput?
3. How Tanks Are Modeled: Accumulative and Flowing Tank Farms?
4. Tank Farm Modeling in Refinery Simulation: Flowing vs Accumulative
5. Tank Farm Simulation Additional Flows: Losses, Additives and Removable Streams in Refinery Digital Twins
6. Tank Farm Performance Metrics in Refinery Simulation
7. Tank Farm Control: Tank Farm Management in Refinery Digital Twins
8. Tank Farm Management in Refinery Simulation
See also:
Accumulative Tank Farm Simulation in Refineries: Complete Guide
Flowing Tank Farm Operating Algorithms for Refinery Digital Twin Simulation
1. Tank Farm Placement in Oil Refinery Material Flow Simulation Models
2. Tank Farm Operating Strategy in Refineries: How Storage Levels Control Unit Throughput?
3. How Tanks Are Modeled: Accumulative and Flowing Tank Farms?
4. Tank Farm Modeling in Refinery Simulation: Flowing vs Accumulative
5. Tank Farm Simulation Additional Flows: Losses, Additives and Removable Streams in Refinery Digital Twins
6. Tank Farm Performance Metrics in Refinery Simulation
7. Tank Farm Control: Tank Farm Management in Refinery Digital Twins
8. Tank Farm Management in Refinery Simulation
See also:
Accumulative Tank Farm Simulation in Refineries: Complete Guide
Flowing Tank Farm Operating Algorithms for Refinery Digital Twin Simulation
Tank Representation in Simulation Models
Tank representation in simulation models defines how physical storage systems are mapped into computational objects that reproduce both structural properties and dynamic operational behavior within process and logistics systems. In refinery and oil & gas modeling, this abstraction transforms real-world storage units—such as tank farms, tanks, and buffer tanks—into state-based simulation entities capable of interacting with flows, constraints, and scheduling logic. Different industrial layouts are discussed in tank farm placement in refinery simulation models. The behavior of these simulated tanks can be evaluated using tank farm performance metrics, including flow statistics, storage statistics, and operational indicators collected throughout the simulation. Within the proposed refinery digital twin classification, tank farm models belong to the functional object model class.
Within refinery digital twin and tank farm simulation models, this representation is used to capture key operational mechanisms product flow accumulation, discharge processes, capacity limitations, and state transitions. The mathematical model for the limited tank capacity case is discussed here. These mechanisms are typically evaluated through flow statistics, storage utilization, and tank utilization metrics. Unlike static storage assumptions, modern simulation frameworks such as AnyLogic-based Petroleum Refining Library models treat tanks as dynamic objects that continuously update their state based on inflow/outflow conditions, production planning rules, and pipeline or loading constraints. Flow routing is coordinated through the tank farm control mechanism. Production planning rules are typically implemented using request-based production planning. Available storage capacity can also be increased through dynamic tank reallocation.
This modeling approach enables accurate representation of refinery logistics systems, supporting tasks such as production planning, feedstock balancing, and supply chain optimization. As a result, tank representation becomes a foundational element in oil and gas digital twin architectures, ensuring that storage behavior is consistently aligned with real operational dynamics and decision-making processes.
Tank farms are a key element of refinery logistics, providing a buffer between production, transportation, and processing units. They stabilize system operations by decoupling continuous refinery processes from discrete supply and shipment activities, reducing the impact of flow fluctuations and scheduling mismatches. Tank farms are a core layer in refinery simulation systems, acting as dynamic buffers between different elements such as production units, pipeline networks, storage systems and others.
In refinery imitation modeling, a tank is not just a storage vessel but a dynamic object that regulates balance across the supply chain. It tracks residual levels, manages inflow and outflow, and enforces constraints such as capacity limits, maximum and minimum volumes. During simulation, these parameters are continuously monitored using storage statistics and individual tank statistics. Maintenance outages can be represented using tank repair simulation. This makes tanks essential components in refinery simulation and digital twin systems.
In simulation models, tank farms are usually represented in two main configurations: flowing tanks, which handle continuous inflow/outflow dynamics, and accumulative tanks, which are more tightly linked to production plans and shipment scheduling. This distinction allows accurate modeling of both operational behavior and planning-level constraints in refinery systems. The operational differences between flowing and accumulative tank farms become evident when comparing their performance metrics, storage utilization, and operational statistics during simulation.
This component belongs to the functional object model class described in the refinery digital twin classification.
This modeling approach enables accurate representation of refinery logistics systems, supporting tasks such as production planning, feedstock balancing, and supply chain optimization. As a result, tank representation becomes a foundational element in oil and gas digital twin architectures, ensuring that storage behavior is consistently aligned with real operational dynamics and decision-making processes.
Tank farms are a key element of refinery logistics, providing a buffer between production, transportation, and processing units. They stabilize system operations by decoupling continuous refinery processes from discrete supply and shipment activities, reducing the impact of flow fluctuations and scheduling mismatches. Tank farms are a core layer in refinery simulation systems, acting as dynamic buffers between different elements such as production units, pipeline networks, storage systems and others.
In refinery imitation modeling, a tank is not just a storage vessel but a dynamic object that regulates balance across the supply chain. It tracks residual levels, manages inflow and outflow, and enforces constraints such as capacity limits, maximum and minimum volumes. During simulation, these parameters are continuously monitored using storage statistics and individual tank statistics. Maintenance outages can be represented using tank repair simulation. This makes tanks essential components in refinery simulation and digital twin systems.
In simulation models, tank farms are usually represented in two main configurations: flowing tanks, which handle continuous inflow/outflow dynamics, and accumulative tanks, which are more tightly linked to production plans and shipment scheduling. This distinction allows accurate modeling of both operational behavior and planning-level constraints in refinery systems. The operational differences between flowing and accumulative tank farms become evident when comparing their performance metrics, storage utilization, and operational statistics during simulation.
This component belongs to the functional object model class described in the refinery digital twin classification.
Tank representation in simulation models defines how physical storage systems are abstracted into computational objects that capture dynamic behavior, state transitions, and flow interactions within process and logistics networks, enabling accurate modeling of flow product accumulation, dispatch operations, and system-level constraints in digital twin environments.
Tank as a Dynamic Object vs Static Storage Unit
In refinery imitation simulation and digital twin models, a tank should be treated as a stateful dynamic object rather than a passive storage capacity. Its behavior is defined not only by resuals, but also by operational states that control how product enters, is processed, and leaves the tank within the overall logistics system. For the full algorithm and implementation details, see tank farm control.
The tank lifecycle can typically represented as a "state machine", where each state corresponds to a specific operational mode. The diagram below illustrates a structured workflow that governs tank transitions between filling, shipment preparation and maintenance activities in imitation model.
The tank lifecycle can typically represented as a "state machine", where each state corresponds to a specific operational mode. The diagram below illustrates a structured workflow that governs tank transitions between filling, shipment preparation and maintenance activities in imitation model.
Tank StateChart Model
All tank states in imitation model can be classified into two main groups based on their functional role in the system. The first group is the active operational cycle, reflecting the standard operational flow of material within the tank farm. The second group consists of inactive or auxiliary states, namely which temporarily exclude the tank.
Initial – tank initialization state, where the object is created and prepared for operation.
Filling – active inflow state, where flow is accumulated in the tank.
Awaiting passportzation – intermediate state where the tank is full or ready for shipment but requires validation or specification confirmation.
Passportization – processing state where product characteristics are formalized (quality, specification, certification).
Shipment – dispatch state where product is withdrawn according to logistics or production plans.
Repair – maintenance state where the tank is temporarily unavailable due to technical servicing (for accumulative and flowing tanks).
Reserve – standby state used for idle capacity. Such tanks are available for dynamic tank reallocation to other tank farms when needed.
Filling – active inflow state, where flow is accumulated in the tank.
Awaiting passportzation – intermediate state where the tank is full or ready for shipment but requires validation or specification confirmation.
Passportization – processing state where product characteristics are formalized (quality, specification, certification).
Shipment – dispatch state where product is withdrawn according to logistics or production plans.
Repair – maintenance state where the tank is temporarily unavailable due to technical servicing (for accumulative and flowing tanks).
Reserve – standby state used for idle capacity. Such tanks are available for dynamic tank reallocation to other tank farms when needed.
Note: In the Petroleum Refining Library, the “reserve” state is assigned to tanks that have been temporarily transferred to other tank farms as part of a “dynamic reallocation”.
Interaction with input and output flows defines how the tank integrates into the overall refinery material balance. Model consistency can be verified using FlowDiscrepancy. Incoming streams drive accumulation dynamics and trigger transitions into filling and subsequent operational states, while outgoing streams are controlled by shipment rules, production planning, and product availability. The runtime decision-making process is explained in tank farm control.
In accumulative tank farms, operational logic is typically constrained so that only one tank valve is open at any given time—either the inlet or the outlet—ensuring strict separation between filling and discharge phases. In contrast, in flowing tank farms configurations, both inlet and outlet valves may be open simultaneously, enabling continuous and balanced flow exchange without discrete phase switching.
In accumulative tank farms, operational logic is typically constrained so that only one tank valve is open at any given time—either the inlet or the outlet—ensuring strict separation between filling and discharge phases. In contrast, in flowing tank farms configurations, both inlet and outlet valves may be open simultaneously, enabling continuous and balanced flow exchange without discrete phase switching.
Dynamic behavior in simulation context
This state-based representation allows the tank to behave as a decision-driven system element, where transitions are triggered not only by residual thresholds but also by operational rules such as:
- availability of shipment orders,
- completion of quality or passportization procedures,
- maintenance scheduling
- related technological Plants and others.
As a result, the tank becomes an integrated part of refinery logistics logic, directly interacting with production planning, storage optimization, and shipment scheduling within a digital twin environment.
- availability of shipment orders,
- completion of quality or passportization procedures,
- maintenance scheduling
- related technological Plants and others.
As a result, the tank becomes an integrated part of refinery logistics logic, directly interacting with production planning, storage optimization, and shipment scheduling within a digital twin environment.
Accumulative vs Flowing Tanks Comparison
Accumulative and flowing tanks represent two fundamentally different paradigms for imitation modeling storage systems in refinery simulation, differing in flow control logic, operational behavior, and integration with planning systems.
Representation of Tanks in Petroleum Refining Library
In the Anylogic-based Petroleum Refining Library (PRL) tanks are initialized at the beginning of the model execution with a predefined set of attributes from database that define both their physical properties and operational behavior. A tank is an internal object of a tank farm. Therefore, to add a tank to the model, it must be linked in the database to the corresponding tank farm and the appropriate farm must be created in AnyLogic, with the tank registered as part of it.
For accumulative tanks in a tank farm from rp_accumulatives database table, the following properties are defined at model start:
- tank name (identifier);
- minimum allowable fill level;
- maximum allowable fill level;
- total capacity;
- initial residuals(starting fill level);
- initial operational state (e.g., filling, reserve, shipment-ready).
These parameters define how each tank behaves within the accumulative logic, including its participation in scheduling, passportization, and shipment processes according to the shipment plan.
For accumulative tanks in a tank farm from rp_accumulatives database table, the following properties are defined at model start:
- tank name (identifier);
- minimum allowable fill level;
- maximum allowable fill level;
- total capacity;
- initial residuals(starting fill level);
- initial operational state (e.g., filling, reserve, shipment-ready).
These parameters define how each tank behaves within the accumulative logic, including its participation in scheduling, passportization, and shipment processes according to the shipment plan.
For flowing tanks in a tank farm from rp_flowing database table, the representation is structured at the level of a tank group rather than individual tanks. In this case, the following attributes are initialized:
- group name (identifier);
- minimum allowable fill level for the group;
- maximum allowable fill level for the group;
- total group capacity;
- initial residual level;
- number of tanks in the group.
- group name (identifier);
- minimum allowable fill level for the group;
- maximum allowable fill level for the group;
- total group capacity;
- initial residual level;
- number of tanks in the group.
This dual representation in Petroleum Refining Library allows the simulation to accurately model both discrete, state-driven accumulation behavior and aggregated continuous-flow behavior within refinery tank farm systems.
Role of Tank Farms in Refinery Digital Twins
In a refinery digital twin, tank farms represent a critical layer of storage and logistics abstraction, connecting production units, pipelines, and downstream shipment systems into a unified simulation environment. Within refinery logistics simulation and oil and gas digital twin models, tank farms act as dynamic buffering nodes that stabilize fluctuations between continuous production and discrete shipment operations.
A tank farm in this context is not just a physical storage facility, but a modeled system element used for production planning, inventory balancing, and flow synchronization. It enables accurate representation of hydrocarbon storage dynamics, including inflow from process units, outflow to pipelines or loading racks, and internal state changes driven by operational constraints.
In tank farm simulation models, tanks allowing engineers to evaluate how storage capacity, pipeline constraints, and demand variability affect overall refinery performance. This is essential for feedstock management, supply chain optimization, and what-if analysis in digital twin environments.
By integrating tank farms into a digital twin of refinery operations, the model gains the ability to simulate realistic residuals propagation effects, reduce bottlenecks in crude oil supply chain simulation, and improve decision-making in production scheduling and logistics planning.
A tank farm in this context is not just a physical storage facility, but a modeled system element used for production planning, inventory balancing, and flow synchronization. It enables accurate representation of hydrocarbon storage dynamics, including inflow from process units, outflow to pipelines or loading racks, and internal state changes driven by operational constraints.
In tank farm simulation models, tanks allowing engineers to evaluate how storage capacity, pipeline constraints, and demand variability affect overall refinery performance. This is essential for feedstock management, supply chain optimization, and what-if analysis in digital twin environments.
By integrating tank farms into a digital twin of refinery operations, the model gains the ability to simulate realistic residuals propagation effects, reduce bottlenecks in crude oil supply chain simulation, and improve decision-making in production scheduling and logistics planning.
Tank Representation in Petroleum Refining Library
All tank representation approaches described in this article are available in the Petroleum Refining Library (PRL). The library provides dedicated simulation components for both accumulative tank farms and flowing tank farms, enabling engineers to reproduce their different operational logic within refinery and oil & gas digital twins.
Petroleum Refining Library models tanks as dynamic objects with configurable physical parameters, operational states, inflow and outflow behavior, and production planning constraints. Together with AnyLogic, these components allow engineers to build realistic refinery storage systems, evaluate alternative operating strategies, and integrate tank farms into complete digital twins of refineries, gas processing plants, terminals, and other hydrocarbon logistics facilities.
Petroleum Refining Library models tanks as dynamic objects with configurable physical parameters, operational states, inflow and outflow behavior, and production planning constraints. Together with AnyLogic, these components allow engineers to build realistic refinery storage systems, evaluate alternative operating strategies, and integrate tank farms into complete digital twins of refineries, gas processing plants, terminals, and other hydrocarbon logistics facilities.
Conclusion
The analysis of flowing and accumulative tank farms demonstrates that refinery storage systems cannot be reduced to a single modeling abstraction without losing essential behavioral distinctions. These two structures represent fundamentally different operational regimes in refinery logistics: flow stabilization versus product planning.
Flowing systems function as dynamic buffers that mitigate short-term fluctuations between process units and ensure continuous, stable transport. In contrast, accumulative systems represent structured product-handling nodes where hydrocarbons are accumulated, conditioned, and released according to operational schedules and logistical constraints.
From a digital twin perspective, separating these paradigms is essential for preserving the accuracy of flow dynamics, storage behavior, and system-wide coordination. The combined use of both approaches enables the construction of realistic hybrid models that capture both real-time process behavior and higher-level planning logic within refinery networks.
Flowing systems function as dynamic buffers that mitigate short-term fluctuations between process units and ensure continuous, stable transport. In contrast, accumulative systems represent structured product-handling nodes where hydrocarbons are accumulated, conditioned, and released according to operational schedules and logistical constraints.
From a digital twin perspective, separating these paradigms is essential for preserving the accuracy of flow dynamics, storage behavior, and system-wide coordination. The combined use of both approaches enables the construction of realistic hybrid models that capture both real-time process behavior and higher-level planning logic within refinery networks.
FAQ
1. What is the role of a tank in refinery simulation?
A tank acts as a dynamic object that buffers flows between production, pipelines, and processing units. It stabilizes operational variability and enables coordination between continuous production and discrete logistics operations.
2. What is the difference between accumulative and flowing tank farms?
Accumulative tank farms operate with discrete storage logic, where tanks are filled and emptied sequentially. Flowing tank farms allow simultaneous inflow and outflow, enabling continuous flow exchange and group-level balancing.
3. Why is a tank considered a dynamic object and not just storage capacity?
Because its behavior depends on operational states (filling, shipment, repair, reserve) and flow-driven transitions. It actively participates in decision logic, planning, and logistics coordination.
4. How are tank states organized in the Petroleum Refining Library?
All states are divided into two groups:
Active cycle: Filling → AwaitingPassportization → Passportization → Shipment
Inactive/auxiliary states: Repair, Reserve
This structure defines whether a tank participates in flow or is temporarily excluded.
5. How do input and output flows interact with a tank?
In accumulative systems, only one flow direction is active at a time (either inlet or outlet). In flowing systems, both directions can operate simultaneously, enabling continuous exchange.
6. How are tanks initialized in Petroleum Refining Library models?
At simulation start, each tank is defined with: name, minimum and maximum fill levels, capacity, initial inventory, initial state (e.g., filling or reserve)
7. How are flowing tank farms represented in Petroleum Refining Library?
They are modeled as tank groups, defined by: group name, min/max fill limits, total capacity, initial inventory, number of tanks in the group. This abstraction supports aggregated continuous-flow behavior.
8. Why is tank modeling important for digital twin systems?
Because tanks determine residual dynamics, bottleneck formation, and synchronization between production, storage, and transportation. Accurate modeling directly impacts planning and optimization quality in refinery digital twins.
9. What happens if a tank reaches capacity limits?
Operational constraints prevent overfilling. The system may redirect flow, block input, or trigger alternative routing depending on the simulation logic and planning rules.
A tank acts as a dynamic object that buffers flows between production, pipelines, and processing units. It stabilizes operational variability and enables coordination between continuous production and discrete logistics operations.
2. What is the difference between accumulative and flowing tank farms?
Accumulative tank farms operate with discrete storage logic, where tanks are filled and emptied sequentially. Flowing tank farms allow simultaneous inflow and outflow, enabling continuous flow exchange and group-level balancing.
3. Why is a tank considered a dynamic object and not just storage capacity?
Because its behavior depends on operational states (filling, shipment, repair, reserve) and flow-driven transitions. It actively participates in decision logic, planning, and logistics coordination.
4. How are tank states organized in the Petroleum Refining Library?
All states are divided into two groups:
Active cycle: Filling → AwaitingPassportization → Passportization → Shipment
Inactive/auxiliary states: Repair, Reserve
This structure defines whether a tank participates in flow or is temporarily excluded.
5. How do input and output flows interact with a tank?
In accumulative systems, only one flow direction is active at a time (either inlet or outlet). In flowing systems, both directions can operate simultaneously, enabling continuous exchange.
6. How are tanks initialized in Petroleum Refining Library models?
At simulation start, each tank is defined with: name, minimum and maximum fill levels, capacity, initial inventory, initial state (e.g., filling or reserve)
7. How are flowing tank farms represented in Petroleum Refining Library?
They are modeled as tank groups, defined by: group name, min/max fill limits, total capacity, initial inventory, number of tanks in the group. This abstraction supports aggregated continuous-flow behavior.
8. Why is tank modeling important for digital twin systems?
Because tanks determine residual dynamics, bottleneck formation, and synchronization between production, storage, and transportation. Accurate modeling directly impacts planning and optimization quality in refinery digital twins.
9. What happens if a tank reaches capacity limits?
Operational constraints prevent overfilling. The system may redirect flow, block input, or trigger alternative routing depending on the simulation logic and planning rules.