Open Thermal AI · Industrial Heat-Pump Intelligence

Industrial heat-pump projects:decide whether to proceed before deciding what to buy.

Turn source temperature, process demand, operating hours and energy price into an honest early decision: proceed, proceed with conditions, or pause—before equipment selection begins.

Public-interest releaseCore access free through at least September 2028

Deterministic calculations · Assumptions labeled · Projects stay in this browser · we do not sell your data

First answers

Answer the three questions that decide whether to continue

  • 01

    Can it work?

    Recommended, conditional, or pause—based on temperatures, source stability, and demand.

  • 02

    Is it worth it?

    See COP, energy use, annual cost change, and payback when investment is known.

  • 03

    What comes next?

    Get missing-data items, engineering risks, and what to verify on site or with an OEM.

Four inputs are enough to begin

  1. 1Source temperature
  2. 2Target temperature
  3. 3Heat duty or energy bill
  4. 4Run hours and energy price

You can start with incomplete data. The evaluation will show what is still missing instead of inventing values.

Engineering decision guides

Start with the decision you need to make

Practical guides connect source and demand measurements to a screening calculation—not to a catalogue model.

Feasibility & ROI

Is an industrial heat-pump project worth doing?

Use temperature lift, seasonal COP, operating overlap, tariffs and payback to make an early go / pause decision.

Read the decision guide →

Waste-heat recovery

Direct heat exchange, condenser recovery, or heat pump?

Compare the three routes without double-counting recoverable heat or hiding the refrigeration power penalty.

Read the decision guide →

Ammonia refrigeration

What should be measured before ammonia heat recovery?

A field checklist for heat balance, condensing pressure, simultaneous demand, safety and project risks.

Read the decision guide →

Learn from tested cases

See how similar projects were screened

Public values and completion assumptions stay separately labeled. Load a case, inspect the inputs, and edit them for learning.

Start with your project—not a perfect data sheet

The first screen is free, editable, and designed to show both potential and reasons to pause.