Accumulative Tank Farm Performance Metrics in Refinery Simulation

       Accumulative tank farms require specialized performance metrics because they additionally execute production plans, shipment schedules, and request-driven operations rather than simple product storage.
       As a result, general tank farm statistics such as material flows, storage utilization, and tank levels are no longer sufficient to fully evaluate system performance. Engineers also need visibility into request execution, shipment progress, loading rack operations, and production plan fulfillment.
       Accumulative tank farm performance metrics therefore extend the standard storage statistics with planning-oriented KPIs that describe how efficiently the tank farm executes production schedules and shipment operations. These metrics help identify planning bottlenecks, monitor dispatch progress, evaluate request priorities, and support operational decision-making in refinery digital twins.

       Together, these request statistics provide complete visibility into request execution and production planning performance.

       Incoming shipment requests represent one of the core control mechanisms of accumulative tank farms. Unlike conventional storage facilities that simply receive product flows whenever capacity is available, accumulative tank farms process filling requests according to production planning priorities and current operational conditions.
       To evaluate request execution, simulation models should collect statistics describing both the current request status and its execution progress. These metrics provide engineers with real-time visibility into how effectively the tank farm satisfies planned material deliveries.

       Typical request statistics include:
        - active shipment request – the request currently being executed.
        - request type – the priority or category of the active request.
        - request completion ratio – the percentage of the planned volume that has already been received.
        - remaining amount to complete – the product volume still required to satisfy the active request.
        - request completion status – indicates whether the current request has been fully completed.
        - active filling tank – the storage tank currently receiving material for the active request.
       These statistics help engineers monitor production plan execution, identify delayed or incomplete requests, evaluate request priorities, and verify that incoming product flow is distributed according to the defined operational strategy. They are also useful for production dashboards, automated control algorithms, and refinery digital twins. These request execution metrics complement the general tank farm performance metrics used to evaluate material flows and storage utilization.

       Executing shipment plans is one of the primary responsibilities of accumulative tank farms. Unlike conventional storage facilities that simply transfer material flows between process units, accumulative tank farms must ensure that production schedules and shipment commitments are fulfilled according to plan.
       To evaluate shipment performance, simulation models should collect statistics describing both planned and actual shipment volumes, as well as the current execution status. These metrics provide engineers with a quantitative assessment of production planning performance and help identify deviations before they affect refinery operations.

       Typical shipment statistics include:
        - total planned shipment – the total product volume scheduled for shipment during the current planning period.
        - total shipped amount – the actual product volume already dispatched from the tank farm.
        - planned vs actual shipment - compare planned versus actual shipment volumes throughout the simulation.
        - shipment completion ratio – the percentage of the shipment plan that has been completed.
        - remaining shipment volume – the product volume still required to satisfy the current shipment plan.
        - shipment completion status – indicates whether the planned shipment has been fully completed.
        - next-month shipment plan – the planned shipment volume for the next planning period (to check Priority B).
        - product on loading racks – the amount of product that has already been transferred to loading racks but has not yet left the refinery.
       These statistics enable engineers to monitor shipment progress, compare planned and actual production schedules, identify delays in dispatch operations, and evaluate the overall effectiveness of refinery logistics. They also provide valuable input for production planning, logistics optimization, and operational decision-making within refinery digital twins.

       Loading operations represent the final stage of product dispatch from an accumulative tank farm. During simulation, products may already be transferred from storage tanks to racks facilities while the actual shipment has not yet been completed ("mass on wheels"). Monitoring these intermediate states is essential for accurately evaluating shipment progress and avoiding overestimation of available storage. Simulation models should therefore collect statistics describing the current state of loading operations and the interaction between storage tanks and loading facilities.
       These statistics provide engineers with visibility into the transition between storage and shipment, helping distinguish products that remain available in storage from those already committed to dispatch. They also improve production planning accuracy, support loading rack scheduling, and provide more realistic shipment analytics within refinery digital twins.

       Accumulative tank farms continuously assign storage tanks to different operational tasks, such as receiving incoming products or supplying outgoing shipments. Monitoring these assignments provides valuable insight into how storage resources are utilized during simulation and helps engineers verify that control algorithms distribute operations according to the intended strategy.
Unlike general storage statistics, tank assignment metrics focus on the operational role of individual tanks at a specific moment rather than on their fill levels or stored product volumes.

       Typical assignment statistics include:
        - active filling tank – the storage tank currently receiving product from the incoming pipeline.
        - active shipment tank – the storage tank currently supplying product for shipment.

       These statistics help engineers verify tank allocation strategies, analyze operational decisions, identify unnecessary tank switching, and ensure that filling and shipment operations are distributed efficiently across the available storage infrastructure. They also provide valuable information for validating production control algorithms and supporting refinery digital twins.

       Collecting accumulative tank farm performance metrics is essential for evaluating how effectively production plans are executed and how efficiently storage resources support refinery logistics. Unlike general tank farm statistics, these metrics focus on operational planning, shipment execution, and request management.

       Accumulative tank farm performance metrics can be used to:
        - monitor production plan execution by comparing planned and actual shipment volumes.
        - track incoming shipment requests and evaluate their completion progress.
        - detect delays in filling and shipment operations before they affect downstream production or customer deliveries.
        - evaluate loading operation efficiency by monitoring product transferred to loading facilities and current dispatch progress.
        - verify tank assignment strategies by analyzing which tanks participate in filling and shipment operations.
        - compare production planning scenarios by measuring the execution performance of alternative operating strategies.
        - support refinery logistics optimization through continuous monitoring of storage, shipment, and loading activities.
        - provide operational dashboards for refinery digital twins, enabling engineers to monitor planning KPIs and make data-driven operational decisions.
       By combining request statistics, shipment execution metrics, loading operation statistics, and tank assignment information, engineers obtain a comprehensive view of accumulative tank farm performance throughout the simulation. These metrics transform accumulative tank farm simulation into a production planning decision-support tool capable of evaluating shipment execution, request processing, and refinery logistics performance.

       PRL automatically creates an instance of RpAccumulativeStatistics for every accumulative tank farm during model initialization. No additional configuration is required. The statistics interface is available through the getStatistics() method, which extends the standard ReservoirParkStatistics API with accumulative-specific performance metrics for request tracking, shipment execution, and production planning.

       For example, shipment execution statistics can be accessed as follows:

       Similarly, incoming shipment request statistics are available through the incomingShipmentRequests object:

       These specialized statistics complement the general tank farm performance metrics provided by ReservoirParkStatistics, enabling detailed analysis of production planning, request execution, shipment scheduling, and loading operations in accumulative tank farms.

       Accumulative tank farms require a broader set of performance metrics than conventional storage systems. In addition to standard tank farm statistics, engineers must continuously monitor request execution, shipment progress, loading operations, and production plan fulfillment to accurately evaluate operational performance.
       Together with the general tank farm performance metrics described in the companion article, these specialized statistics provide a comprehensive framework for analyzing accumulative tank farm behavior during simulation. They enable engineers to validate production planning strategies, optimize shipment scheduling, improve refinery logistics, and support data-driven decision making in refinery digital twins.

1 What are accumulative tank farm performance metrics?
Accumulative tank farm performance metrics are specialized KPIs used to evaluate production planning, shipment execution, request processing, and loading operations. Unlike general storage statistics, they focus on how efficiently an accumulative tank farm fulfills production and shipment plans.

2 Why do accumulative tank farms require additional statistics?
Unlike flowing tank farms, accumulative tank farms operate according to production planning and shipment scheduling rules. As a result, engineers must monitor request execution, shipment progress, loading operations, and production plan completion in addition to standard material flow and storage statistics.

3 Which statistics are most important for accumulative tank farms?
The most important statistics include shipment request completion, remaining request volume, shipment plan execution, shipped amount, shipment completion ratio, loading rack inventory, active filling tank, and active shipment tank. Together, these metrics provide a comprehensive view of production planning performance.

4 How do shipment statistics improve refinery production planning?
Shipment statistics allow engineers to compare planned and actual shipment volumes, monitor dispatch progress, detect delays, and evaluate whether production schedules are being executed as intended. These insights help optimize refinery logistics and improve production planning decisions.

5 What is the difference between tank farm performance metrics and accumulative tank farm performance metrics?
General tank farm performance metrics focus on material flows, storage utilization, tank levels, and operational status. Accumulative tank farm performance metrics extend these statistics with request tracking, shipment execution, loading operations, and production planning KPIs required for accumulative storage systems.

6 How are accumulative tank farm statistics implemented in PRL?
PRL automatically creates an RpAccumulativeStatistics object for every accumulative tank farm. The statistics are available through the getStatistics() method and provide access to shipment planning, incoming shipment requests, loading operations, and active tank assignment without requiring additional configuration.

7 What is shipment completion ratio?
Shipment completion ratio measures the percentage of the planned shipment volume that has already been dispatched during the current planning period. It is one of the primary KPIs used to evaluate shipment plan execution in accumulative tank farm simulation.