Tank Farm Control: Tank Farm Management in Refinery Digital Twins
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 farms are among the most dynamic assets in petroleum refining. Although often viewed as passive storage facilities, they directly influence refinery throughput, production stability, logistics, and operational flexibility. Modern refineries continuously adjust tank farm operations by redirecting product flows, switching between storage and direct transfer, regulating flow rates, blending feedstocks, and reconfiguring product routing. These control actions maintain material balance, prevent storage bottlenecks, and ensure a stable supply to downstream processing units.
In refinery digital twins, storage systems must respond to simulated events exactly as they would in real operations. Rather than relying on fixed process logic, tank farms should support runtime changes to operating modes, flow constraints, routing rules, and other control parameters without interrupting the simulation.
Effective tank farm control therefore extends far beyond storage management. It enables engineers and operators to coordinate oil and gas flows, optimize production, respond to abnormal situations, and evaluate alternative operating strategies within a single simulation environment. As digital twins become an integral part of refinery planning and operations, flexible tank farm control has become a fundamental capability rather than an optional feature.
A detailed description of the Accumulative Tank Farm control architecture is presented in our work.
A detailed description of the Flowing Tank Farm control architecture is presented in our work.
Effective tank farm control therefore extends far beyond storage management. It enables engineers and operators to coordinate oil and gas flows, optimize production, respond to abnormal situations, and evaluate alternative operating strategies within a single simulation environment. As digital twins become an integral part of refinery planning and operations, flexible tank farm control has become a fundamental capability rather than an optional feature.
A detailed description of the Accumulative Tank Farm control architecture is presented in our work.
A detailed description of the Flowing Tank Farm control architecture is presented in our work.
Operational Challenges in Tank Farm Control
Tank farm control is inherently complex because storage systems connect production units, logistics infrastructure, and transportation networks. Unlike individual process units that perform specific technological operations, tank farms must continuously balance oil and gas flows while adapting to changing refinery conditions.
One of the primary challenges is changing production schedules. Fluctuations in processing rates, feedstock availability, and product demand require continuous adjustments to product routing, transfer rates, and operating modes. At the same time, limited storage capacity may force operators to either accumulate products, redirect oil and gas flows to alternative destinations, or transfer it directly to downstream units to prevent production bottlenecks.
Maintenance activities add another layer of complexity. Taking storage tanks, pipelines, or connected process units out of service changes available flow paths and storage capacity, requiring the control system to safely reconfigure operations while maintaining stable refinery performance.
Refinery digital twins introduce an additional challenge: these operational changes must be reproduced dynamically during simulation. Instead of relying on fixed process configurations, digital twins should support runtime adjustments to operating modes, throughput limits, routing strategies, and other control parameters, accurately reflecting real refinery operations.
One of the primary challenges is changing production schedules. Fluctuations in processing rates, feedstock availability, and product demand require continuous adjustments to product routing, transfer rates, and operating modes. At the same time, limited storage capacity may force operators to either accumulate products, redirect oil and gas flows to alternative destinations, or transfer it directly to downstream units to prevent production bottlenecks.
Maintenance activities add another layer of complexity. Taking storage tanks, pipelines, or connected process units out of service changes available flow paths and storage capacity, requiring the control system to safely reconfigure operations while maintaining stable refinery performance.
Refinery digital twins introduce an additional challenge: these operational changes must be reproduced dynamically during simulation. Instead of relying on fixed process configurations, digital twins should support runtime adjustments to operating modes, throughput limits, routing strategies, and other control parameters, accurately reflecting real refinery operations.
Typical Tank Farm Control Tasks
Effective tank farm control involves much more than filling and emptying storage tanks. In modern refineries, storage operations continuously adapt to production schedules, feedstock availability, downstream demand, and logistics constraints. As a result, operators and automated control systems continuously adjust tank farm behavior.
One of the primary tasks is managing operating modes. Depending on current conditions, a tank farm may accumulate products, transfer them directly to downstream units, or temporarily bypass storage. Selecting the appropriate mode improves equipment utilization while minimizing unnecessary product transfers.
One of the primary tasks is managing operating modes. Depending on current conditions, a tank farm may accumulate products, transfer them directly to downstream units, or temporarily bypass storage. Selecting the appropriate mode improves equipment utilization while minimizing unnecessary product transfers.
Note: Operating modes refer to the current tank farm operating strategy, such as storage mode (product accumulation in tanks) or direct-flow mode (product bypasses storage tanks).
Another key function is product flow control. Tank farms receive products from multiple upstream sources and supply several downstream consumers. Control actions include regulating inlet and outlet throughput, redirecting product flows, limiting transfer rates, and adjusting routing based on equipment availability or production priorities.
Tank farms also support product blending and stream separation. Multiple crude oil grades, intermediate streams, or finished products may be blended to achieve target specifications, while individual process streams can be divided into multiple product streams when required. These operations maintain product quality while preserving overall material balance.
Maintenance activities require additional flexibility. When storage tanks, pipelines, pumps, or connected process units become unavailable, the control system must safely reconfigure material routing and restore normal operation once equipment returns to service.
Thus, refinery digital twins require runtime control rather than static process configuration. Engineers should be able to modify operating modes, flow limits, routing strategies, and other control parameters during simulation, making tank farm control a key component of production planning, operational analysis, and decision support.
Tank farms also support product blending and stream separation. Multiple crude oil grades, intermediate streams, or finished products may be blended to achieve target specifications, while individual process streams can be divided into multiple product streams when required. These operations maintain product quality while preserving overall material balance.
Maintenance activities require additional flexibility. When storage tanks, pipelines, pumps, or connected process units become unavailable, the control system must safely reconfigure material routing and restore normal operation once equipment returns to service.
Thus, refinery digital twins require runtime control rather than static process configuration. Engineers should be able to modify operating modes, flow limits, routing strategies, and other control parameters during simulation, making tank farm control a key component of production planning, operational analysis, and decision support.
Operating Modes and Flow Routing
One of the primary objectives of rtank farm control is selecting the appropriate operating mode for current production conditions.
Depending on their purpose, tank farms may accumulate products, smooth flow fluctuations, or transfer it directly between process units
Storage mode is used to accumulate products before supplying them to downstream units or loading facilities, supporting production and shipment schedules. Flow smoothing mode uses storage capacity as a hydraulic buffer to absorb short-term flow fluctuations and stabilize continuous process units. Alternatively, tank farms may operate in Direct Flow mode, where product flow bypasses storage and is transferred directly from the inlet to the outlet. This mode is useful when storage is unnecessary, capacity is limited, or immediate downstream supply is required. In simulation models, Direct Flow also reduces computational overhead, making it suitable for long simulation periods where detailed tank behavior is not required.
Modern refinery digital twins allow these operating modes and material routing strategies to be changed dynamically during simulation, enabling engineers to evaluate different operating scenarios while maintaining realistic refinery behavior.
Modern refinery digital twins allow these operating modes and material routing strategies to be changed dynamically during simulation, enabling engineers to evaluate different operating scenarios while maintaining realistic refinery behavior.
Flow Control
Oil and gas flow control is one of the core functions of a tank farm. Modern storage systems continuously regulate how products enter, move through, and leave the storage network, allowing refinery operations to adapt to changing production requirements, equipment availability, and logistics constraints.
Flow control includes throughput management, product routing, blending, and stream separation. Operators may adjust inlet and outlet flow rates, redirect products between processing units or loading facilities, blend multiple feedstocks to achieve target specifications, or divide a single process stream into several product streams according to predefined ratios. Runtime throughput control is discussed in accumulative tank farm control.Together, these functions maintain material balance while improving operational flexibility.
In refinery digital twins, flow control should be available at runtime rather than fixed during model development. Dynamic adjustment of flow rates, routing strategies, blending ratios, and separation coefficients enables engineers to evaluate alternative operating scenarios, optimize production, and respond to changing process conditions without rebuilding the simulation model.
Flow control includes throughput management, product routing, blending, and stream separation. Operators may adjust inlet and outlet flow rates, redirect products between processing units or loading facilities, blend multiple feedstocks to achieve target specifications, or divide a single process stream into several product streams according to predefined ratios. Runtime throughput control is discussed in accumulative tank farm control.Together, these functions maintain material balance while improving operational flexibility.
In refinery digital twins, flow control should be available at runtime rather than fixed during model development. Dynamic adjustment of flow rates, routing strategies, blending ratios, and separation coefficients enables engineers to evaluate alternative operating scenarios, optimize production, and respond to changing process conditions without rebuilding the simulation model.
Operator Control and Automation
Modern tank farms combine automatic control with operator supervision. While routine operations are typically handled by process control systems, operators remain responsible for responding to abnormal situations, maintenance activities, production changes, and logistics disruptions.
Control actions may be initiated manually or by higher-level systems such as production scheduling, Advanced Process Control (APC), Manufacturing Execution Systems (MES), or refinery digital twins. These systems can adjust operating modes, throughput limits, product routing, and other operating parameters to maintain production targets.
An effective control system should support runtime reconfiguration, allowing engineers and operators to modify operating parameters without interrupting the simulation. This capability is particularly valuable for evaluating alternative operating strategies in refinery digital twins.
All control actions should be validated before they are applied to ensure consistency with current process conditions and equipment availability. Robust validation improves simulation reliability and prevents invalid operating scenarios.
Control actions may be initiated manually or by higher-level systems such as production scheduling, Advanced Process Control (APC), Manufacturing Execution Systems (MES), or refinery digital twins. These systems can adjust operating modes, throughput limits, product routing, and other operating parameters to maintain production targets.
An effective control system should support runtime reconfiguration, allowing engineers and operators to modify operating parameters without interrupting the simulation. This capability is particularly valuable for evaluating alternative operating strategies in refinery digital twins.
All control actions should be validated before they are applied to ensure consistency with current process conditions and equipment availability. Robust validation improves simulation reliability and prevents invalid operating scenarios.
Tank Farm Control in Petroleum Refining Library
The tank farm control principles described above are implemented in Anylogic-based Petroleum Refining Library (PRL) through the ReservoirPark template, which provides a unified framework for both flowing and accumulative tank farm. The library supports runtime control of operating modes, product blending, product separation, throughput limits, and other operating parameters without requiring the simulation model to be rebuilt.
To simplify model development and debugging, every tank farm includes an integrated operator control panel for configuring farm-specific parameters. Parameter changes automatically trigger corresponding events, allowing custom business logic to respond immediately through an event-driven architecture.
Petroleum Refining Library also validates operating mode transitions and parameter changes to prevent invalid operating states and improve simulation robustness. While these capabilities are available for all tank farm types, accumulative tank farm extends them with specialized algorithms for filling requests, shipment requests, passportization, tank allocation, and production planning, which are covered in a dedicated article.
To simplify model development and debugging, every tank farm includes an integrated operator control panel for configuring farm-specific parameters. Parameter changes automatically trigger corresponding events, allowing custom business logic to respond immediately through an event-driven architecture.
Petroleum Refining Library also validates operating mode transitions and parameter changes to prevent invalid operating states and improve simulation robustness. While these capabilities are available for all tank farm types, accumulative tank farm extends them with specialized algorithms for filling requests, shipment requests, passportization, tank allocation, and production planning, which are covered in a dedicated article.
Petroleum Refining Library provides a unified runtime API for controlling tank farm behavior during simulation. Rather than modifying the simulation model, engineers can change operating parameters programmatically or through the integrated control panel. Most control actions immediately trigger corresponding events, allowing external business logic to respond through Petroleum Refining Library's event-driven architecture.
Managing Operating Modes
Operating modes are controlled directly through the ReservoirPark operator panel. Users can switch between storage operation and direct product transfer by changing the corresponding control parameter during simulation or direct flow can be enabled by setting the isDirectFlowMode parameter (Label: Direct flow), allowing products to bypass storage tanks and flow directly from the inlet to the outlet whenever appropriate.
Managing Operating Modes
Operating modes are controlled directly through the ReservoirPark operator panel. Users can switch between storage operation and direct product transfer by changing the corresponding control parameter during simulation or direct flow can be enabled by setting the isDirectFlowMode parameter (Label: Direct flow), allowing products to bypass storage tanks and flow directly from the inlet to the outlet whenever appropriate.
Input mixing flows
Runtime throughput control is performed using dedicated API methods that temporarily override the nominal inlet capacity.:
Runtime throughput control is performed using dedicated API methods that temporarily override the nominal inlet capacity.:
setInputSpeedLimit(double speedLimit);
resetInputSpeedLimit();Product Blending and Stream Separation
Petroleum Refining Library supports runtime modification of blending and separation strategies using dedicated API methods:
Petroleum Refining Library supports runtime modification of blending and separation strategies using dedicated API methods:
setInputMerge(Map<Integer, Double> inputFlowRatios);
setOutputSplit(Map<Integer, Double> separationFactors);setInputMerge() defines the blending ratios for incoming product streams, while setOutputSplit() specifies how an outgoing process stream is divided among multiple destinations. Together, these methods provide flexible runtime control of refinery material routing while preserving overall material balance.
Maintenance Activities
Tank maintenance schedules are configured externally through the rp_tanks_repairs database table. Each record specifies the tank farm, the storage tank, and the maintenance start and end times. During simulation, Petroleum Refining Library automatically removes tanks from operation for the specified period and restores them after maintenance is completed, allowing refinery digital twins to reproduce realistic changes in available storage capacity and flow routing caused by planned maintenance.
Tank maintenance schedules are configured externally through the rp_tanks_repairs database table. Each record specifies the tank farm, the storage tank, and the maintenance start and end times. During simulation, Petroleum Refining Library automatically removes tanks from operation for the specified period and restores them after maintenance is completed, allowing refinery digital twins to reproduce realistic changes in available storage capacity and flow routing caused by planned maintenance.
Best Practices for Tank Farm Control
Effective tank farm control requires flexible, configurable, and reliable control strategies rather than static storage management.
Treat tank farms as active process objects that dynamically interact with production units, logistics systems, and production planning.
Separate control logic from process logic by making operating modes, routing, and throughput limits configurable instead of hard-coded.
Support runtime reconfiguration so operating parameters can be adjusted without interrupting the simulation.
Validate all control actions before applying operating mode, routing, or throughput changes to maintain a consistent system state.
Use event-driven control to react immediately to parameter changes and reduce unnecessary computational overhead.
Following these principles helps engineers build refinery digital twins that accurately reproduce storage operations while supporting production planning, operational analysis, and decision-making.
Treat tank farms as active process objects that dynamically interact with production units, logistics systems, and production planning.
Separate control logic from process logic by making operating modes, routing, and throughput limits configurable instead of hard-coded.
Support runtime reconfiguration so operating parameters can be adjusted without interrupting the simulation.
Validate all control actions before applying operating mode, routing, or throughput changes to maintain a consistent system state.
Use event-driven control to react immediately to parameter changes and reduce unnecessary computational overhead.
Following these principles helps engineers build refinery digital twins that accurately reproduce storage operations while supporting production planning, operational analysis, and decision-making.
Conclusion
Tank farm control is a fundamental part of refinery operations. Beyond simply storing crude oil and petroleum products, modern storage systems continuously regulate flows, operating modes, throughput limits, and product routing to maintain stable production under changing operating conditions.
As refinery digital twins become increasingly important for production planning and operational optimization, the ability to modify storage behavior dynamically has become essential. Flexible control mechanisms allow engineers to evaluate alternative operating strategies, respond to equipment availability, coordinate maintenance activities, and optimize flow movement without rebuilding simulation models.
Whether implemented through custom control logic or specialized simulation libraries, effective tank farm control should combine dynamic configuration, event-driven architecture, operator interaction, and robust validation mechanisms. Together, these capabilities transform tank farms from passive storage assets into actively managed components of the refinery production system.
As refinery digital twins become increasingly important for production planning and operational optimization, the ability to modify storage behavior dynamically has become essential. Flexible control mechanisms allow engineers to evaluate alternative operating strategies, respond to equipment availability, coordinate maintenance activities, and optimize flow movement without rebuilding simulation models.
Whether implemented through custom control logic or specialized simulation libraries, effective tank farm control should combine dynamic configuration, event-driven architecture, operator interaction, and robust validation mechanisms. Together, these capabilities transform tank farms from passive storage assets into actively managed components of the refinery production system.
FAQ
1 What is tank farm control?
Tank farm control is the process of managing the operation of storage facilities within a refinery or oil and gas terminal. It includes controlling operating modes, product routing, flow rates, blending, storage capacity, and interactions with upstream and downstream process units.
2 Why is tank farm control important in refinery operations?
Tank farms connect production units, pipelines, storage tanks, loading facilities, and transportation systems. Effective control helps maintain stable production, prevent storage bottlenecks, improve logistics, and optimize flow movement throughout the refinery.
3 What is the difference between tank farm management and tank management?
Tank farm management focuses on the operation of the entire storage system, including flow routing, operating modes, and coordination with other refinery units. Tank management deals with individual storage tanks, including filling levels, maintenance, inspections, and operational status.
4 What operating modes can a tank farm support?
Tank farms typically support Storage Mode, Flow Smoothing Mode, and Direct Flow Mode. Depending on the refinery design, additional operating configurations may also be implemented.
5 How is product flow controlled in a tank farm?
Flow control typically includes regulating inlet and outlet throughput, redirecting product routing, blending incoming streams, separating outgoing streams, and coordinating transfers with connected processing units and logistics systems.
6 Why is runtime control important in refinery digital twins?
Production conditions continuously change because of maintenance activities, equipment availability, production schedules, and logistics events. Runtime control allows engineers to modify operating parameters during simulation without rebuilding the model, making digital twins much more realistic and useful for operational analysis.
7 Can tank farm control be automated?
Yes. Modern refinery control systems combine operator actions with automatic control algorithms. Digital twins can also integrate with production planning systems, optimization algorithms, MES, SCADA, or custom control logic to automate many operational decisions.
8 What are the benefits of event-driven tank farm control?
An event-driven architecture immediately reacts to changes in operating conditions instead of continuously checking system status. This improves simulation performance, simplifies control logic, and enables faster responses to equipment failures, routing changes, or operator actions.
9 How does Petroleum Refining Library (PRL) support tank farm control?
Petroleum Refining Library implements tank farm control through configurable operating modes, runtime flow management, throughput limits, product blending and separation, integrated operator controls, and an event-driven control architecture. These capabilities allow engineers to build realistic refinery digital twins while keeping the control logic modular and maintainable.
10 What is the difference between flowing and accumulative tank farms?
A flowing tank farm transfers flow directly through the storage system without long-term accumulation, while an accumulative tank farm stores products before they are shipped or supplied to downstream units. Both approaches are used in refinery simulation depending on operational requirements.
Tank farm control is the process of managing the operation of storage facilities within a refinery or oil and gas terminal. It includes controlling operating modes, product routing, flow rates, blending, storage capacity, and interactions with upstream and downstream process units.
2 Why is tank farm control important in refinery operations?
Tank farms connect production units, pipelines, storage tanks, loading facilities, and transportation systems. Effective control helps maintain stable production, prevent storage bottlenecks, improve logistics, and optimize flow movement throughout the refinery.
3 What is the difference between tank farm management and tank management?
Tank farm management focuses on the operation of the entire storage system, including flow routing, operating modes, and coordination with other refinery units. Tank management deals with individual storage tanks, including filling levels, maintenance, inspections, and operational status.
4 What operating modes can a tank farm support?
Tank farms typically support Storage Mode, Flow Smoothing Mode, and Direct Flow Mode. Depending on the refinery design, additional operating configurations may also be implemented.
5 How is product flow controlled in a tank farm?
Flow control typically includes regulating inlet and outlet throughput, redirecting product routing, blending incoming streams, separating outgoing streams, and coordinating transfers with connected processing units and logistics systems.
6 Why is runtime control important in refinery digital twins?
Production conditions continuously change because of maintenance activities, equipment availability, production schedules, and logistics events. Runtime control allows engineers to modify operating parameters during simulation without rebuilding the model, making digital twins much more realistic and useful for operational analysis.
7 Can tank farm control be automated?
Yes. Modern refinery control systems combine operator actions with automatic control algorithms. Digital twins can also integrate with production planning systems, optimization algorithms, MES, SCADA, or custom control logic to automate many operational decisions.
8 What are the benefits of event-driven tank farm control?
An event-driven architecture immediately reacts to changes in operating conditions instead of continuously checking system status. This improves simulation performance, simplifies control logic, and enables faster responses to equipment failures, routing changes, or operator actions.
9 How does Petroleum Refining Library (PRL) support tank farm control?
Petroleum Refining Library implements tank farm control through configurable operating modes, runtime flow management, throughput limits, product blending and separation, integrated operator controls, and an event-driven control architecture. These capabilities allow engineers to build realistic refinery digital twins while keeping the control logic modular and maintainable.
10 What is the difference between flowing and accumulative tank farms?
A flowing tank farm transfers flow directly through the storage system without long-term accumulation, while an accumulative tank farm stores products before they are shipped or supplied to downstream units. Both approaches are used in refinery simulation depending on operational requirements.
Last updated on 29.06.2026