Data underlying the publication: Multi-Objective Optimization of Energy Efficiency and Geomechanical Safety in High-Temperature Aquifer Thermal Energy Storage (HT-ATES) Systems Based on Coupled Thermo-Hydro-Mechanical (THM) Analysis

This repository contains the complete code and dataset for a multi-objective optimization framework developed for the design of High-Temperature Aquifer Thermal Energy Storage (HT-ATES) systems. The research focuses on achieving a balanced design that enhances energy production while minimizing geomechanical risks. Our approach involves building surrogate models using XGBoost to approximate high-fidelity THM simulation outputs and integrating these models with a NSGA-II based optimization algorithm. This framework efficiently explores the trade-offs among competing objectives, enabling the identification of optimal design configurations. The implementation is done in Python and leverages libraries such as pymoo (0.6.1.3), XGBoost (2.1.3), and scikit-learn (1.2.2).