Improving Fertilizer Operations with Sigmafine
The highly competitive fertilizer industry often suppresses the product’s value, thus, it is critical that plants consistently achieve high asset utilization with maximized efficiency. To do so, top-tier fertilizer producers are demanding that their process data becomes more consistent, accurate, and accessible from start to finish; to do this they are turning to Sigmafine.
Whether it means getting a consistent picture of transactions among the companies in a chemical site, estimate contaminants in an unmeasured pond or apply performance monitoring of energy intensive processes such as large rotating machines, fired heaters, etc. Sigmafine plays a key role in constraining raw data through first principle conservation of mass or energy and tracking how materials are moved across your facility.
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Sigmafine improves the quality of real-time data
Sigmafine is a model-based solution, robust and scalable, that runs periodically, even autonomously. The model incorporates measurements from meters, sensors, financial transactions, engineering estimates, even business logics, etc., to create a single and consistent dataset. The model often becomes the solution to:
- Inventory & loss monitoring with the estimation of unmeasured flows and inventories
- Early faulty detection of flow meters, level gauges and analyzers
- Product composition monitoring & validation
- Relating plant performance to business events; e.g. plant capacity vs product type
- Extending upstream material qualities for the estimation of downstream properties
Production Accounting: Inventory & Loss Monitoring
Sigmafine interfaces with several data repositories (real-time historians, relational databases, ERP systems, etc.) making cross validation of separated measurements possible using data reconciliation methodology. Data issues that are typically identified by Sigmafine include missing transactional data and biased measurements on the streams or the inventories.
Beyond providing an estimate for measurements in error, the magnitude of these discrepancies is quantified through Sigmafine statistical indicators allowing to sort and support an effective instrument maintenance plan.
Production accounting: Estimating unmeasured flows & inventories
Receiving raw materials (sulfuric acid, potash, phosphoric acids, etc) as well as the exporting of the final fertilizer products are generally well monitored, however the internal inventories intermediates are often not measured, and hence may not be fully utilized, as shown in the general schema below. Sigmafine goes beyond monitoring and cross-checking measurements as the solution also estimates unmeasurable flows or inventories, for example, the 11 boundary meters and 2 raw material inventories are used to estimate 3 flows and 6 inventories; which represents a 60% increase in the number of estimated measurement points. The Sigmafine result shows that the internal inventories are not steady, instead they are rapidly depleted on days with high outputs. Running extremely low inventories increases the risk of having unplanned plant wide shut-down and planning to avoid low inventory periods could include an earlier increasing of the upstream producing plants, thus highlighting a potential site capacity expansion.
Trucking estimates (undeclared delivery of product)
Early Detection of Faulty Measurements
Historically meters and analyzers were conditioned based on a maintenance schedule. Sigmafine inherently calculates the meter health via statistical data quality indicators. Meter health results can be seen visually within the model or consolidated via dedicated reports and by doing so, meters with unreliable measurements can be prioritized for maintenance. Reliable measurements quality indicator lies between 1, -1 however the results from the test period shown below exceeds this range indicating a significant error on this meter prior to its recalibration on day 25.
Material Quality Estimation of Unmeasured Inventories
Adhering to the target chemical ratios in fertilizer formulation as well as keeping contaminants at low levels are critical for the quality of the final product. This comes in contrast with poor availability in measurements and analyses. Sigmafine quality tracking supports the estimation of physical properties such as compositions and contaminants level through the facility using the few analytical data available and leveraging the mass balance results as well as the tracking of materials across the plant.
In the case below, Sigmafine tracks the quality parameters in inventories as a function of the raw material qualities in which the impurity levels is being increased, while the stored material is within specification (as related the target sulfur content), the continued use of high level Selenium in raw material could lead to re-processing cost due to the final product becoming out of specification. In this regard, Sigmafine quality tracking acts as an early warning system for material qualities (key components & impurities) at inventories.
*Model assumptions includes – no simultaneous production of products, inventories are well mixed and trucking services was inactive in the test period; initial inventory was approximately 100 times that of the daily feed rate
Connecting Productivity to Business events /Managing shared assets
Sigmafine mass & composition tracking analysis follows the logical movements of raw materials through to products, thus capable of linking operational performance to business events. For example, the daily production can be reported in terms on the raw materials consumed, product ratio, suppliers, shift. In the example below the impact of varying ratio of high/low sulfur products on the overall plant capacity is shown. , As the fraction of high sulfur product is increased, the total production capacity decreases, suggesting that while the plant is being operated to meet the product demand, the production planning could be tuned to produce these targets more effectively. In this example, operating at high ratio of (10/1) should be avoided, suggesting operations should aim at operating within a 0.3 to 0.6 range of high/low sulfur ratio in the products.
Sigmafine is a comprehensive, model-based solution with advanced calculations that, supports fertilizer operations and its supply chain, including plants with limited instrumentation. The newly cleansed, conditioned data-set facilitates improved management of inventories, giving insight on semi-finished and finished product material qualities when laboratory data are not yet available avoiding out-of-spec products and minimizing reprocessing costs.