Product Mixer Performance Metrics in Refinery Simulation

       In refinery simulation, evaluating a blending operation requires considerably more information than simply measuring inlet and outlet flow rates. A ProductMixer continuously decides which incoming streams satisfy product quality specifications, determines how much of each stream is accepted into the final blend, rejects non-compliant material, injects additives, and verifies that the resulting product satisfies all blending constraints. These operations cannot be evaluated using conventional flow statistics alone.
       To support engineering analysis, ProductMixer Statistics combines process, optimization, and quality-control information within a single API. In addition to monitoring material flows, it evaluates product properties, tracks rejected streams, reports additive consumption, and verifies compliance with blending constraints. Together, these metrics provide a complete picture of the blending process and allow engineers to understand not only how much product is produced, but also why the optimizer selected a particular blending solution.
       Unlike conventional mixer statistics, which typically report only total inlet and outlet flows, ProductMixer statistics expose every major stage of the blending algorithm. Engineers can analyze incoming feed composition, evaluate the contribution of individual feedstocks to the final product, monitor rejected material, inspect calculated product properties, and quantify additive consumption throughout the simulation. This makes the statistics suitable not only for debugging blending models but also for refinery Digital Twins, optimization validation, operational dashboards, and production planning systems.
       The Petroleum Refining Library organizes these performance metrics into several logical categories covering incoming flows, blended product statistics, quality constraints, additives, and optimization summary information. Together, these statistics provide a comprehensive framework for monitoring refinery blending operations, validating optimization results, and analyzing ProductMixer performance throughout the simulation.

       ProductMixer exposes a comprehensive set of performance metrics covering every stage of the refinery blending process. Unlike conventional process mixers, which primarily report inlet and outlet flow rates, ProductMixer provides statistics describing feed composition, product formation, quality constraint evaluation, additive consumption, and optimization results. These metrics allow engineers to evaluate not only the quantity of blended product, but also the quality of the blending solution generated during simulation.
For easier interpretation, the statistics are organized into five logical categories based on their engineering purpose. Together, they provide a complete framework for analyzing refinery blending operations, validating optimization results, and integrating ProductMixer into Digital Twin dashboards and production planning systems.

       The Petroleum Refining Library provides direct access to all ProductMixer performance metrics through the ProductMixerStatistics class. The API enables developers to retrieve real-time operating data, evaluate blend quality, inspect optimization results, analyze additive consumption, and integrate ProductMixer statistics into custom applications, refinery dashboards, and Digital Twin platforms. Together, these statistics provide a complete operational picture of refinery blending. They enable engineers to monitor product quality, analyze rejected streams, validate blending constraints, optimize additive consumption, and compare alternative blending strategies using objective performance indicators collected throughout the simulation.

       Input statistics describe the composition and distribution of incoming streams before the blending process begins. These metrics provide complete visibility into the material entering the ProductMixer and allow engineers to evaluate feedstock availability, analyze the contribution of individual inlet streams, and verify that the blending process receives the expected product flows. Unlike product statistics, which characterize the blending result, input statistics represent the initial conditions supplied to the optimization algorithm.
The ProductMixer Statistics API provides both aggregate and stream-level information. Engineers can retrieve the total incoming flow, obtain the flow associated with a particular inlet connection, or analyze the contribution of individual input streams using their internal mixer indices. These statistics are available both as numerical values for application development and as formatted reports suitable for operator interfaces and debugging.

       Input statistics are widely used for feedstock analysis, refinery blending optimization, Digital Twin dashboards, and verification of production scenarios. They allow engineers to compare the contribution of different feedstocks, identify dominant inlet streams, validate upstream process behavior, and ensure that the optimization algorithm operates on the expected feed composition. Since all subsequent blending calculations are based on these incoming flows, input statistics form the foundation for evaluating ProductMixer performance and validating refinery blending simulations.

       While input statistics describe the feed entering the ProductMixer, product statistics characterize the results of the blending process. These metrics show how much material is incorporated into the final product, how much is rejected during blending, and how each incoming stream contributes to the finished blend. Together, they provide a direct measure of blending efficiency and product utilization.
Unlike conventional mixers, ProductMixer does not necessarily accept all incoming material. During optimization, each inlet stream is evaluated against the specified product quality constraints. Material that satisfies the blending requirements is directed to the final product, while the remaining portion is diverted to the residual outlet. Product statistics therefore provide visibility into both accepted and rejected material, enabling engineers to identify feedstocks that limit product quality or reduce production efficiency.

       Product statistics are widely used to evaluate blending efficiency, analyze rejected feedstocks, validate optimization results, and compare alternative blending strategies. They provide engineers with a clear understanding of how each inlet stream contributes to the final product, how much material is rejected due to quality constraints, and how effectively the available feedstock is utilized throughout the blending process. These metrics are also valuable for refinery Digital Twin dashboards, production planning, and optimization analysis.

       Constraint statistics evaluate whether the blended product satisfies the required quality specifications. Unlike flow statistics, which describe material movement, these metrics measure product properties such as octane number, sulfur content, density, or any other configured blending constraint. They enable engineers to compare calculated product characteristics with the specified quality limits and validate the optimization results.
The ProductMixer Statistics API reports both the characteristics of the incoming blend and the calculated properties of the final product. This allows engineers to understand how the blending process modifies product quality and how additives or changes in feed composition influence the final result. Each reported characteristic is evaluated according to its configured constraint type and direction (minimum or maximum), making the statistics suitable for refinery quality control, optimization validation, and Digital Twin applications.

       Constraint statistics are essential for validating blend quality, comparing optimization scenarios, troubleshooting product specification violations, and analyzing refinery blending performance. They provide engineers with complete visibility into how each quality constraint influences the blending solution and help verify that the final product satisfies all technological and commercial requirements before it leaves the ProductMixer.

       Additives play an essential role in refinery blending by adjusting product properties to satisfy quality specifications or improve commercial value. ProductMixer supports two independent additive mechanisms. The first introduces predefined additives directly into individual inlet streams or the final product according to user-defined ratios. The second automatically injects a balance additive during optimization to satisfy blending constraints or maximize product production. ProductMixer statistics provide complete visibility into both mechanisms and quantify additive consumption throughout the blending process.
The ProductMixer Statistics API reports additive consumption for individual inlet streams, additives contained in the final blended product, and the balancing additive generated by the optimization algorithm. These metrics enable engineers to evaluate additive usage, compare alternative blending strategies, estimate additive consumption, and verify the optimizer's decisions. They are particularly valuable when minimizing additive consumption is one of the optimization objectives.

       Additive statistics help engineers evaluate additive consumption, validate optimization strategies, estimate operating costs, and compare alternative blending solutions. By exposing both manually configured additives and automatically calculated balancing additives, ProductMixer provides comprehensive information for refinery quality control, production optimization, Digital Twin dashboards, and blend cost analysis.

       Besides detailed flow, constraint, and additive metrics, ProductMixer provides summary statistics describing the current operating mode and optimization status. These statistics give engineers a concise overview of the blending process without requiring analysis of every individual performance indicator. They are particularly useful for operator dashboards, model debugging, and monitoring refinery Digital Twins.
The summary includes the ProductMixer identifier, the current calculation mode, the active optimization objective, and, when available, the maximum achievable production rate. Depending on the selected operating mode, the summary also indicates whether ProductMixer is minimizing balancing additive consumption or maximizing finished product production. This information allows engineers to quickly verify that the blending algorithm is operating under the expected optimization strategy.

       Summary statistics provide a high-level overview of ProductMixer operation and are commonly used for simulation monitoring, optimization verification, and Digital Twin dashboards. Although concise, these metrics allow engineers to determine the current optimization mode, verify that automatic calculations are active, and assess the overall state of the blending process without examining detailed flow or quality statistics.

1. What is the Manual Product Blending Algorithm?
The Manual Product Blending Algorithm is a blending mode in which the optimization model is fully defined by the user. Instead of relying on a built-in optimization strategy, users specify the objective function, decision variables, and constraints that determine how inlet streams are blended.

2. When should the manual algorithm be used?
The manual algorithm is appropriate when the required blending strategy cannot be represented by the standard automatic algorithm. Typical examples include custom operating rules, company-specific optimization objectives, or specialized process constraints.

3. Can I define my own optimization objective?
Yes. The optimization objective is completely user-defined. It may maximize product throughput, minimize blending costs, maximize profit, reduce the consumption of specific components, or optimize any other linear objective.

4. What types of constraints can be included?
Any linear constraints supported by the selected optimization solver can be used. These typically include flow balance equations, capacity limits, product quality requirements, component availability, and other operational constraints.

5. What happens if no feasible solution is found?
If the optimization problem has no feasible solution, no valid blend is produced. The incoming material is routed to the rejected flow streams, ensuring that the simulation remains physically consistent.

6. Does the manual algorithm support linear programming only?
Yes. The current implementation is designed for linear optimization problems. Nonlinear blending problems must be reformulated as linear models or solved using an external optimization approach.

7. Can the optimization model change during the simulation?
Yes. The optimization problem may be rebuilt whenever operating conditions change, allowing the blending strategy to adapt dynamically to new process conditions, production targets, or material availability.

8. Can the Manual Product Blending Algorithm be used for gasoline or diesel blending?
Yes. The algorithm can be used for gasoline blending, diesel blending, jet fuel blending, and any other refinery product that can be represented by a linear optimization model.

9. Can the Manual Product Blending Algorithm be integrated with refinery process simulation models?
Yes. The algorithm is fully integrated with refinery process simulation and digital twin models developed using the Petroleum Refining Library.