Offline metal refining assistant: specific gravity assay, melt planning, stoichiometric reagent calculations and yield reconciliation for precious and base-metal scrap operations
Select the calculation module that matches your current bench task – all three work offline without account registration
All modules operate without network connection. Data stays local on your device. No cloud sync or account creation required.
Five stages cover the complete refining cycle, each with decision-support calculations built for bench-side use
Scarlet Loop serves jewelers, electronics scrap processors, and small foundries who need accurate calculations without relying on cloud services. The physics engine handles gold, silver, platinum-group metals, brass, and aluminum alloys. Every calculation runs deterministically: same inputs produce identical outputs. The machine learning layer tunes predictions to your equipment behavior over repeated melts, accounting for crucible material, furnace design, and operator technique.
Updated October 2026 to version 1.0.4 with refined loss attribution logic and expanded alloy catalog.
Physics-based models calibrated by your furnace's actual performance across refining cycles
Scarlet Loop performs specific gravity assays by comparing water displacement to catalog densities. You weigh a piece twice: once in atmosphere, once fully submerged. The app corrects for water temperature and surfaces every alloy match within measurement tolerance. When two candidates overlap, it recommends the quickest discriminating test.
The melt planner solves for reagent volumes using stoichiometric ratios adjusted by a practice multiplier you configure. It predicts oxidative loss, volatile burn-off, crucible retention, and pour skull based on metal composition and charge mass. Energy consumption estimates account for furnace efficiency and target liquidus. The safety module rejects plans that exceed crucible capacity or furnace temperature ceiling.
Post-melt reconciliation logs the gap between predicted and recovered mass, then updates learned coefficients for oxidation rates, retention factors, and energy draw. Over multiple melts, predictions converge toward your specific equipment behavior. Stock inventory displays live value for precious-metal ingots using user-set spot prices, with composition cards tracking purity and mass.
Six functional modules handle assay, planning, safety verification, execution tracking, reconciliation, and inventory valuation
Identifies metal by comparing measured density to catalog values. Accounts for water temperature variation and measurement precision. Flags ambiguous matches and suggests discriminating tests.
Calculates target mass, selects refining path, determines admixture ratios. Validates charge fits crucible capacity and furnace can reach liquidus temperature for selected alloy.
Computes stoichiometric volumes for inquartation or aqua regia dissolution. Applies configurable practice multiplier for real-world excess. Routes selection adapts to metal composition.
Predicts oxidation, volatile burn-off, crucible retention, and skull residue based on charge composition. Machine learning model refines coefficients from logged outcomes across melt cycles.
Calculates fuel consumption and runtime for target liquidus. Accounts for furnace efficiency, ambient temperature, and crucible thermal mass. Verifies power supply meets peak draw.
Tracks ingot inventory with composition cards showing purity and mass. Displays live precious-metal value using spot prices you configure. Logs provenance and refining history for each piece.
Version 1.0.4 · Utilities · Offline operation · No subscription
Download Scarlet LoopRequires iOS device · All data stored locally · No account creation · Updated 06.10.2026