Tank Farm Performance Metrics in Refinery Simulation

       Tank farms are among the most critical components of refinery logistics, connecting production units, pipelines, storage facilities, and shipment systems. While simulation models accurately reproduce product flows and equipment behavior, their real value lies in the operational statistics they generate throughout the simulation.
       Tank farm performance metrics are among the most valuable outputs of refinery simulation models. Collecting tank farm performance metrics allows engineers to evaluate storage efficiency, monitor product flows, analyze tank utilization, assess available storage capacity, and identify operational bottlenecks before they affect real production. These metrics also provide an objective basis for comparing alternative operating strategies, validating production plans, and supporting decision-making in refinery digital twins.
       A comprehensive tank farm simulation should therefore provide much more than animated process visualization. It should continuously collect meaningful operational data that can be analyzed during and after the simulation. The following sections describe the key categories of statistics that every modern tank farm simulation model should provide.

       Flow statistics describe how products move through a tank farm during simulation and form the basis for evaluating storage and transportation performance. These metrics provide complete visibility into incoming and outgoing flows, allowing engineers to verify mass balance, analyze product distribution, and identify operational constraints. Learn more about additional material flows in tank farm simulation, including bypass pipelines, product losses, and additive injection. These statistics form the foundation for storage utilization analysis and overall tank farm performance evaluation.

       The following statistics are typically collected:
        - total incoming flow – the total amount or flow rate of entering the tank farm.
        - incoming flow by product – received for each product handled by the storage system.
        - incoming flow by inlet – flow statistics for each incoming pipeline or process connection.
        - total outgoing flow – the total amount or flow rate of leaving the tank farm.
        - outgoing flow by product – product-specific shipment or transfer statistics.
        - outgoing flow by outlet – transferred through each outgoing connection.
        - residual flow – that cannot be accepted by the tank farm and must be redirected or rejected.
        - internal consumption – consumed internally by the facility rather than transferred to downstream processes.
        - product losses – losses caused by evaporation, drainage, or other operational factors.
        - loss ratio – the percentage of losses relative to the total processed .
Note: Product losses and additive flows are discussed in more detail in Tank Farm Additional Flows in Refinery Digital Twins.

       These statistics help validate balances, detect abnormal operating conditions, evaluate the effectiveness of storage operations, and compare alternative operating scenarios. They also provide essential input for refinery production planning, logistics optimization, and digital twin analytics.

       Storage statistics characterize the current state, storage utilization, and available capacity of a tank farm. Unlike flow statistics, these metrics focus on the amount of product stored, storage capacity utilization, and the overall efficiency of storage operations. Together, storage utilization and tank utilization provide a complete picture of how efficiently the available storage capacity is used throughout the simulation. Unlike flow statistics, which describe product movement, these metrics focus on the amount of product stored, available storage capacity, and overall utilization of the tank farm. The behavior of storage capacity depends on whether the facility operates as a flowing or accumulative tank farm.

       The following storage statistics are commonly collected:
        - total residuals – the total amount of product currently stored in the tank farm.
        - total residuals excluding minimum operational level – the usable product volume available for shipment.
        - tank fill level – the current fill percentage of an individual storage tank.
        - overall tank farm fill level – the average fill level across all operating tanks.
        - storage utilization – the percentage of the available storage capacity currently occupied by product.
        - available storage capacity – the remaining capacity available for receiving additional product.
        - middle operating level – the target operating fill level based on minimum and maximum allowable tank capacities.
        - minimum operational residuals – the mandatory product volume that must remain in storage to ensure safe and stable operation.
        - active tank count – the number of tanks currently available for operation.
        - tank availability – the operational status of each storage tank.
        - tank farm status – whether the tank farm is currently full, empty, or operating within its normal range.
       These statistics provide a comprehensive view of storage utilization and tank utilization, helping engineers determine whether the available storage capacity is sufficient for future production, optimize storage operations, and prevent capacity-related bottlenecks. They also allow engineers to identify underutilized storage, evaluate operating strategies, and prevent situations where production is limited by insufficient tank capacity.

       While aggregate storage statistics provide an overview of the entire tank farm, engineers often need detailed information for individual storage tanks. Tank-level statistics are essential for monitoring operating conditions, balancing storage utilization, scheduling maintenance, and validating control algorithms.

       Typical statistics include:
        - current stored mass – the amount of product currently stored in a tank.
        - usable stored mass – the product available for shipment, excluding the minimum operational residual.
        - tank fill level – the percentage of tank capacity currently occupied.
        - available capacity – the remaining capacity available for receiving additional product.
        - tank operating status – whether the tank is active, inactive, under maintenance, or reserved.
        - tank availability – whether the tank can participate in filling or shipment operations.
       Note: Maintenance and dynamic reassignment of tanks are discussed in Dynamic Tank Reallocation Between Tank Farms.
       These statistics provide complete visibility into the operating condition of every storage tank and help engineers distribute product more evenly across the tank farm, detect underutilized assets, and ensure that sufficient capacity is available for future production or shipment operations.

       Besides continuous flow and storage statistics, a tank farm simulation should provide high-level operational indicators that describe the current condition of the storage system. These indicators are useful for control logic, dashboards, and automated decision support.

       Typical operational indicators include:
        - tank farm is full – indicates that no additional product can be accepted.
        - tank farm is empty – indicates that no usable product remains for shipment.
        - number of active tanks – tanks currently participating in normal operation.
        - available storage capacity – free capacity across all active tanks.
        - current operating mode – for example, storage mode or direct-flow mode when applicable.
        Note: Operating modes are closely related to the tank farm planning algorithm used to control storage operations.
       These indicators simplify the integration of simulation models with optimization algorithms, production scheduling systems, and digital twins by providing a concise representation of the current operating state without requiring analysis of every individual tank.

       PRL automatically initializes a statistics object for every tank farm. No additional configuration is required. The statistics interface is available through the getStatistics() method, which returns an instance of ReservoirParkStatistics or one of its specialized subclasses depending on the tank farm type.
The ReservoirParkStatistics class is organized into several logical groups of statistics, making it easy to access specific performance metrics.

For example, the total incoming flow can be obtained as:

Similarly, storage statistics are available through the tankLevels object:

       The accumulative tank farm (RpAccumulative) extends the base statistics with additional classes for shipment planning, request execution, and loading operations. These capabilities are described in the companion article dedicated to accumulative tank farm performance metrics.

       Collecting operational statistics is not an end in itself. The primary purpose of these metrics is to support engineering analysis, operational optimization, and data-driven decision making.

       Tank farm performance metrics can be used to:
        - validate material balances by comparing incoming and outgoing product flows.
        - evaluate storage utilization and identify underutilized or overloaded tanks.
        - detect storage bottlenecks before they limit production or shipment operations.
        - verify production and logistics strategies by comparing simulation results across multiple operating scenarios.
        - optimize storage capacity by analyzing residuals and available tank volume.
        - monitor operational efficiency through utilization, loss, and flow statistics.
        - support production planning by ensuring sufficient storage capacity for future production campaigns.
        - provide operational dashboards for refinery digital twins and decision support systems.
       By analyzing these statistics, engineers can identify inefficiencies, improve storage management, reduce operational risks, and compare alternative control strategies without affecting real refinery operations.

       A modern tank farm simulation should provide much more than animated flows. It should continuously collect comprehensive operational statistics that describe product movement, storage conditions, tank utilization, and overall system performance.
       Together, flow statistics, storage statistics, individual tank statistics, and operational indicators provide a complete picture of tank farm behavior throughout the simulation. These performance metrics enable engineers to evaluate storage utilization, improve tank utilization, validate production strategies, identify operational bottlenecks, validate production strategies, identify operational bottlenecks, and support optimization within refinery digital twins.

1 What are tank farm performance metrics?
Tank farm performance metrics are quantitative indicators used to evaluate the operation of storage facilities during simulation. They typically include flow statistics, storage utilization, tank utilization, residuals, storage capacity, product losses, and operational status. These metrics help engineers assess system performance and identify opportunities for optimization.

2 What statistics should a tank farm simulation collect?
A comprehensive tank farm simulation should collect statistics on incoming and outgoing flows, storage residuals, tank fill levels, available storage capacity, storage utilization, tank utilization, product losses, and the operational status of individual tanks and the entire tank farm. These statistics are automatically collected by PRL Tank Farm Statistics API.

3 What is the difference between storage utilization and tank utilization?
Storage utilization measures how efficiently the overall storage capacity of a tank farm is used, while tank utilization focuses on the operating condition and occupancy of individual storage tanks. Together, these metrics provide a complete picture of storage performance.

4 Why are tank farm performance metrics important?
Performance metrics allow engineers to validate balances, monitor storage efficiency, identify operational bottlenecks, compare alternative operating strategies, and support production planning. They are also an essential source of operational data for refinery digital twins.

5 How do tank farm statistics improve refinery simulation?
Operational statistics transform a refinery simulation into an engineering decision-support tool. Instead of simply visualizing flows, engineers can evaluate storage performance, analyze bottlenecks, optimize storage capacity, and compare multiple operating scenarios using objective performance metrics.

6 Which KPIs are most important for tank farm simulation?
The most commonly used KPIs include total incoming and outgoing flow, storage utilization, tank utilization, fill level, available storage capacity, residuals, product losses, and active tank count. Together, these indicators provide a comprehensive assessment of tank farm performance.