INSUS uses your furnace process, energy and metallurgical-quality data to recommend heat-treatment cycle adjustments for actual load and alloy conditions, helping reduce energy consumption without compromising proven quality limits. Operators keep the decision.
01
Heat treatment often dominates foundry energy cost.
02
Fixed cycles rarely match load and alloy conditions.
03
Recommendations screened against metallurgical quality history.
The problem
Foundry heat treatment is highly energy-intensive, yet many cycles continue to run on fixed time and temperature schedules. When load size, alloy composition or furnace conditions change, operators have limited visibility into how much energy the current cycle is actually using or where the safe efficiency boundary sits.
The shift
The opportunity is not new furnace hardware. It is using the data your furnace already produces to understand its achievable energy baseline and recommend cycle adjustments for the actual load and alloy being processed.
Today
With INSUS
How it works
This fits OptiStudio as process and energy decision support beside existing furnace controls. Access is read-only. AI does not control the furnace.
Connect
Connect furnace controls or PLC data, the plant historian and metallurgical quality records covering temperature profiles, cycle duration, load size, alloy type, energy consumption and corresponding test or reject outcomes.
Understand
Compare current heat-treatment energy performance against the furnace's own historically achievable performance for comparable load sizes and alloys.
Predict
Estimate the energy impact of candidate cycle adjustments and assess whether the predicted outcome remains within the foundry's established metallurgical-quality envelope.
Advise
Rank candidate heat-treatment adjustments by estimated energy impact while applying quality constraints. Operators review and apply the recommendations.
Verify
Compare recommended cycles with actual energy consumption and metallurgical test outcomes. The model is refined using the foundry's own furnace and alloy history over time.
Getting started
For foundry and heat-treatment teams that already log furnace process, energy and metallurgical-quality data. Delivered through OptiStudio as cycle energy decision support. Expert Services can help with data assessment and validation. Advisory and read-only. No new sensors when the furnace already logs the data in scope.
Related for machinery
Foundry heat-treatment energy sits beside weld-repair root causes and warranty field intelligence on the same machinery plant.
Heat-treatment cycles often run on fixed schedules even when load and alloy change. This use case recommends cycle adjustments from furnace, energy and metallurgical-quality data while staying inside the proven quality envelope.
Furnace temperature profiles, cycle duration, load size, alloy type, energy consumption and metallurgical test or reject results. No new sensors when the furnace already logs the temperature and energy data in scope.
Candidate recommendations are screened against the foundry's historical metallurgical test and reject outcomes. Energy savings are not treated as an acceptable trade for leaving the proven quality envelope.
No. It is advisory. It provides ranked recommendations for operator review and does not write directly to furnace PLCs or controls.
Yes. Alloy composition and load size are part of the input data so recommendations can be calibrated to the actual material and production conditions.
Through OptiStudio as process and energy decision support. Expert Services can support data assessment, integration and validation with your foundry and process teams.
Results are furnace- and alloy-specific. Melting-furnace optimization and other foundry processes are outside this scope. Actual savings must be established through a plant-specific pilot, not assumed from external guidance.
Machinery · Foundry and energy
Start with one furnace. Establish its achievable energy baseline, connect it to metallurgical-quality history and test whether recommended cycle adjustments can reduce energy use without changing the quality envelope.
