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Challenge

CCGT Operational Efficiency & Carbon Monitor for Doosan Oman Plant

Doosan Enerbility won a $670m CCGT power plant contract in Oman. Build an operational efficiency and carbon intensity monitoring agent in Python that calculates heat rate, fuel consumption, and CO2 emissions per MWh. Achieve live thermodynamic efficiency calculations with under 1% marginal deviation.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Develop an thermodynamic analytics agent in Python to continuously evaluate heat rate and carbon intensity for a major combined-cycle gas plant in Oman.

Delivery guide

How work is evaluated

Evaluation

Evaluates heat rate and carbon intensity calculation accuracy under desert ambient conditions.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

thermodynamic_range_check

Verifies efficiency falls within realistic CCGT bounds (45-65%)

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

heat_rate_precision

Accuracy compared against thermodynamic benchmark model • target: 0.99 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • Process gas turbine fuel flow rates, steam generation, and megawatt power output.

  • Calculate Net Plant Heat Rate (kJ/kWh) and thermal efficiency percentage.

  • Compute real-time carbon intensity (gCO2/kWh) under varying ambient temperatures.

  • Produce emission compliance logs for Omani environmental authorities.

Resources and assets

Reference links and supporting material

Dataset notes

Plant telemetry simulation for high-efficiency combined cycle power generation under GCC climate.

Challenge setup

Evaluates heat rate and carbon intensity calculation accuracy under desert ambient conditions.

Challenge input

JSON with power_mw, natural_gas_flow_kg_s, and ambient_temp_c

Python Thermodynamics Tools

Provides accurate enthalpy and thermodynamic math.

Evaluated output

JSON with thermal_efficiency_percent, heat_rate_kj_kwh, and co2_g_kwh

Evaluation checks
  • Verifies efficiency falls within realistic CCGT bounds (45-65%)
  • Accuracy compared against thermodynamic benchmark model • target: 0.99 • range: 0-1
Configured evaluation evidence
  • Benchmark: ThermoPlantBench
  • Heat Rate Precision target: 99%
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

Action Space
  • AutoKeras · Required
  • Alibi Detect · Optional
  • Deepgram · Optional

Evaluation contract

  • thermodynamic_range_check · Weight 1
  • heat_rate_precision · Weight 1

Metrics

  • heat_rate_precision · Target 0.99

Benchmarks

  • ThermoPlantBench
  • energy

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON with power_mw, natural_gas_flow_kg_s, and ambient_temp_c

    Output: JSON with thermal_efficiency_percent, heat_rate_kj_kwh, and co2_g_kwh

    Execution harness: Python

Evaluation tools

  • Python Thermodynamics Tools · Math Engine

Recipe freeze marker

No recipe freeze time is recorded.

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