AI USE CASE
Tank Farm Inventory and Blending Optimization
ML-driven scheduling reduces storage waste and optimizes blending across chemical tank farms.
What it is
Applies machine learning and combinatorial optimization to allocate storage tanks and sequence blending operations dynamically. The system continuously adjusts schedules based on incoming shipments, production demand, and product compatibility constraints.
Data you need
Historical tank usage logs, product specifications, blending recipes, shipment schedules, and product compatibility matrices.
Required systems
- erp
- data warehouse
Why it works
- Engage process engineers early to encode domain constraints (compatibility, minimum heel volumes) accurately.
- Start with a pilot covering a subset of tanks before rolling out to the full farm.
- Establish a feedback mechanism so operators can flag schedule anomalies, improving model retraining.
- Integrate real-time sensor and ERP data feeds to keep scheduling inputs current.
How this goes wrong
- Incomplete or inconsistent tank history data undermines model accuracy from the start.
- Product compatibility constraints not fully encoded lead to unsafe or invalid blending schedules.
- Operations teams distrust model recommendations and override them without feedback loops, degrading future performance.
- Integration with legacy SCADA or ERP systems is underestimated, causing delays and cost overruns.
When NOT to do this
Do not deploy this solution at a facility with fewer than 20 tanks or where blending recipes rarely change, the optimization payoff will not justify the integration and modelling investment.
Example vendors
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