Monte carlo dataset for techno-economic assessment of sustainable aviation fuel production via methanol-to-jet

This dataset contains large-scale Monte Carlo simulation results for a techno-economic assessment (TEA) of a methanol-to-jet (MtJ) fuel production pathway.
A total of approximately 3 million simulation runs were generated by systematically varying key economic, technical, and operational input parameters within literature-based and industry-relevant bounds. The underlying TEA model is implemented as a steady-state process and cost model, evaluated through an Excel-based framework coupled to Python. For each Monte Carlo sample, the model computes the net production cost (NPC) of sustainable aviation fuel per mass (kg).

The dataset is designed to support:
-Uncertainty and sensitivity analysis of MtJ techno-economic performance
-Development and benchmarking of machine learning surrogate models
-Explainable AI (XAI) studies for identifying dominant cost drivers
-Reproducible comparison of TEA assumptions across studies

To facilitate reuse, the dataset is accompanied by detailed variable descriptions, parameter bounds, and the Python scripts used for data generation. Reduced-size sample files are provided for rapid testing and machine learning prototyping.