A specialized open-source LLM for industrial exergy analysis and entropy generation detection.
| Metric | Value |
|---|---|
| Base model | Qwen2.5-7B-Instruct |
| Fine-tuning | LoRA r=16, Unsloth |
| Benchmark | 85.5% (40 tests x 3 runs, Grade B+) |
| vs Base Qwen | +17.3pp improvement |
| Temperature | 0.7 (optimal) |
| Training data | 800 examples, Alpaca format |
See docs/BENCHMARK_ANALYSIS.md for detailed results.
v0.4 training data pipeline is complete: 1369 examples (1235 train / 134 val) in ChatML format, generated via Claude Opus with thermodynamic QC. Covers 8 equipment types and 6 analysis families.
EntropyHunter is a domain-specific language model fine-tuned to perform second-law thermodynamic analysis on industrial equipment. Given equipment parameters, it can:
- Calculate exergy balances (input, output, destruction, efficiency)
- Identify entropy generation mechanisms (heat transfer, pressure drop, mixing)
- Perform Gouy-Stodola verification (Ex_destroyed = T₀ × S_gen)
- Classify equipment with Bejan number grading (A–F)
- Recommend practical improvements based on avoidable/unavoidable decomposition
- Conduct thermoeconomic analysis (SPECO methodology)
- Perform pinch analysis for heat integration
- Generate ISO 50001 energy management assessments
| Equipment | Subtypes | Analysis Depth |
|---|---|---|
| Compressor | screw, piston, scroll, centrifugal | Full |
| Boiler | fire-tube, water-tube, condensing, waste heat, biomass, electric | Full |
| Chiller | screw, centrifugal, absorption, air/water-cooled | Full |
| Pump | centrifugal, positive displacement, submersible, vertical turbine | Full |
| Heat Exchanger | shell & tube, plate, air-cooled, economizer, recuperator | Full |
| Steam Turbine | back-pressure, condensing, extraction, ORC, micro | Full |
| Dryer | rotary, fluidized bed, spray, belt, heat pump, infrared | Full |
7 equipment types × 48 subtypes × 14 analysis types
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("OlivenetAI/EntropyHunter-7B")
tokenizer = AutoTokenizer.from_pretrained("OlivenetAI/EntropyHunter-7B")
prompt = """Perform an exergy analysis for a shell & tube heat exchanger.
Operating conditions:
- Hot fluid: Flue gas, inlet 320°C, outlet 180°C, flow rate 2.5 kg/s
- Cold fluid: Water, inlet 25°C, outlet 85°C, flow rate 4.0 kg/s
- Dead state: T₀ = 25°C, P₀ = 101.325 kPa
Provide: exergy balance, efficiency, entropy generation, Bejan number, recommendations."""
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=1024, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))entropy-hunter/
├── taxonomy/ # Equipment & analysis type definitions
├── datagen/ # Synthetic training data generation pipeline
├── data/ # Generated datasets (not tracked in git)
├── training/ # LoRA fine-tuning scripts
├── eval/ # Evaluation benchmarks & results
├── docs/ # Analysis documentation
├── archive/ # Archived experiments
└── serving/ # Deployment configurations
EntropyHunter is trained via knowledge distillation:
- Domain Expert Design — Thermodynamic scenarios designed by chemical engineers with field experience
- Synthetic Data Generation — High-quality instruction-output pairs generated from frontier models
- Quality Control — Every example verified for thermodynamic consistency (energy balance, second law, Gouy-Stodola)
- LoRA Fine-tuning — Efficient adaptation on Qwen2.5-7B base using Unsloth
- Evaluation — Benchmarked against base model and frontier models on held-out test sets
Training data is informed by the ExergyLab platform's 7 analysis engines, 317 knowledge files, and industrial reference data.
- v0.0 — Project structure, taxonomy, prompt templates
- v0.1 — MVP: ~800 examples, basic exergy + EGM + SPECO
- v0.2 — Current: 85.5% benchmark, LoRA r=16, Qwen2.5-7B (active model)
-
v0.3— JSON-free experiment: -7.2pp regression, archived - v0.4 — 1369 ChatML examples, Calculation Summary scaffold, 14B base eval (in progress)
- v1.0 — Production: ExergyLab integration, edge deployment
Built on established thermodynamic methodologies:
- Exergy analysis: Kotas, Bejan, Tsatsaronis & Moran
- SPECO methodology: Tsatsaronis (2009) — Thermoeconomic cost allocation
- EGM: Bejan (1996) — Entropy Generation Minimization
- Advanced exergy: Tsatsaronis & Morosuk — EN/EX + AV/UN decomposition
- Pinch analysis: Linnhoff & Hindmarsh (1983) — Heat integration
- Gap analysis: 3-layer exergetic gap (minimum / BAT / actual)
Kemal Düzkar — Chemical Engineer & Founder, Olivenet Ltd.
Combining thermodynamic expertise with IoT and AI to hunt entropy in industrial systems.
Apache 2.0 — See LICENSE for details.
@misc{entropyhunter2026,
author = {Düzkar, Kemal},
title = {EntropyHunter-7B: Fine-tuned Model for Industrial Exergy Analysis},
year = {2026},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/OlivenetAI/EntropyHunter-7B}}
}"Every irreversibility is a missed opportunity. Every entropy generated is value destroyed. This model finds them."