Free screening tool for industrial waste heat recovery.
Enter your plant's waste heat streams → get in 5 minutes the applicable technologies, estimated costs, and investment payback.
| Step | Description |
|---|---|
| 1. Input | Define thermal streams: fluid, temperatures, flow rate, operating hours |
| 2. Analysis | Calculates thermal power (kW), annual energy (MWh), exergy, waste cost |
| 3. Balance | Interactive Sankey diagram of the energy balance |
| 4. Technologies | Recommends from 8 recovery technologies (HX, heat pumps, ORC, ...) |
| 5. Economics | Estimates CAPEX (±30%), payback, NPV, IRR for each technology |
| 6. Sensitivity | Energy price sweep (±50%) and tornado chart on 4 key parameters |
| 7. Report | Professional PDF report + Excel export + JSON save/load |
- 8 recovery technologies: gas-gas HX, economizer, liquid HX, HRSG, air/water heat pumps, ORC, combustion air preheater
- 10 preloaded industrial examples: foundry, dairy, ceramics, glass, paper, brewery, chemical, textile, data center, multi-stream complex
- Incentive analysis: generic CAPEX reduction (tax credits, grants — any country) + Italian White Certificates (TEE)
- Sensitivity analysis: energy price sweep with payback/NPV charts + tornado chart (±20% on price, CAPEX, hours, efficiency)
- All parameters editable: energy price, discount rate, analysis horizon, OPEX/installation multipliers
- Import/Export: CSV/Excel stream import, Excel export (3 sheets), JSON save/load, PDF report
- Methodology section: all formulas, correlations, and sources cited in-app
- 249 automated tests across 5 levels (unit, physics sanity, property-based, snapshot, real validation)
- Energy managers evaluating heat recovery in their plant
- ESCos and energy consultants doing industrial energy audits
- Engineering students and researchers in energy/thermal engineering
- Anyone with waste heat asking: "is it worth recovering?"
# Install
pip install -e ".[dev]"
# Run
streamlit run heatscout/web/app.pyOpens at http://localhost:8501. Load a preloaded example from the sidebar to get started.
pytest tests/ -v249 tests on 5 levels:
- Unit tests — functional validation of every module
- Physics sanity — cp vs tabulated values (ASHRAE, Perry's), thermodynamics laws
- Property-based (Hypothesis) — invariants verified on random inputs
- Snapshot golden — anti-regression on 10 examples (83 recommendations)
- Real validation — comparison with measured data from real plants (DOE, ETEKINA H2020)
| Component | Technology |
|---|---|
| Fluid properties | CoolProp + custom correlations |
| Charts | Plotly (Sankey, bar charts, sensitivity) |
| UI | Streamlit |
| ReportLab | |
| Economics | numpy-financial (NPV, IRR) |
| Linting | Ruff (pre-commit hooks) |
- CAPEX correlations have ±30% uncertainty (sources: Thekdi/ACEEE, IEA, Quoilin et al.)
- Estimated savings have ±15% uncertainty
- Efficiency models are first-order (simplified correlations)
- This tool is for initial screening — it does not replace a detailed engineering feasibility study
- All sources and formulas are documented in the in-app Methodology section
Development assisted by Claude Code (Anthropic).
See CONTRIBUTING.md for guidelines.