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Challenge

Doosan Oman CCGT Combined-Cycle Thermal Efficiency Optimizer

Optimizing Doosan Enerbility's $670m CCGT plant in Oman requires real-time balancing of gas turbine air intake and steam turbine pressure ratios. Build an optimization pipeline using SciPy and Python to calculate optimal combustion parameters that maximize megawatt generation while minimizing heat rate losses. Achieve an operational efficiency improvement of at least 2.5%.

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Challenge brief

What you are building

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

Build a thermodynamic efficiency optimization model for Oman CCGT power plants using SciPy.

Delivery guide

How work is evaluated

Evaluation

Evaluates SciPy thermal optimization efficiency gain and parameter constraint compliance.

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

Thermal Efficiency Lower Bound

Ensures output efficiency exceeds standard 55% baseline.

Binary check

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

Dimension 2

Efficiency Gain Metric

Percentage efficiency gain achieved over default operational curve. • target: 2.5 • range: 0-10

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

  • Formulate mathematical thermodynamic models for CCGT power plants in Python

  • Use SciPy `optimize.minimize` routines under non-linear boundary constraints

  • Optimize air-to-fuel ratios and steam turbine inlet pressures in real time

  • Reduce heat rate (kJ/kWh) consumption across partial load scenarios

Resources and assets

Reference links and supporting material

Dataset notes

Combined cycle gas turbine operating parameters, ambient desert temperature profiles, and fuel consumption heat rate curves.

Challenge setup

Evaluates SciPy thermal optimization efficiency gain and parameter constraint compliance.

Challenge input

JSON object with ambient_temp_c, fuel_flow_kg_s, target_mw

SciPy

Standard Python framework for mathematical and engineering optimization.

Evaluated output

JSON object with optimal_steam_pressure_bar, optimal_air_ratio, efficiency_pct

Evaluation checks
  • Ensures output efficiency exceeds standard 55% baseline.
  • Percentage efficiency gain achieved over default operational curve. • target: 2.5 • range: 0-10
Configured evaluation evidence
  • Benchmark: ThermoBench
  • Efficiency Gain Metric target: 2.5
  • 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
  • Cartesia · Required
  • Relicx · Optional
  • Storia AI · Optional

Evaluation contract

  • Thermal Efficiency Lower Bound · Weight 1
  • Efficiency Gain Metric · Weight 1

Metrics

  • Efficiency Gain Metric · Target 2.5

Benchmarks

  • ThermoBench
  • energy

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON object with ambient_temp_c, fuel_flow_kg_s, target_mw

    Output: JSON object with optimal_steam_pressure_bar, optimal_air_ratio, efficiency_pct

    Execution harness: Python

Evaluation tools

  • SciPy · Scientific Optimization Engine

Recipe freeze marker

No recipe freeze time is recorded.

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