We present a hybrid industrial cooling system model that embeds analytical solutions within a multiphysics simulation. This model is designed for reinforcement learning (RL) applications and balances simplicity with simulation fidelity and interpretability. The model’s fidelity is evaluated against real world data from a large scale cooling system. This is followed by a case study illustrating how themodel can be used for RL research. For this, we develop an industrial task suite that allows specifying different problem settings and levels of complexity, and use it to evaluate the performance of different RL algorithms.