Adaptive Feature Selection and Random Forest Modeling for Green Ammonia Production Process

Abstract Green ammonia as a potential clean energy source has attracted significant attention, only contributing to the effective utilization of new energy but also boosting the green transformation of the chemical industry. Effective and accurate modeling is crucial for the green ammonia process. This study innovatively introduces a machine learning method combining the adaptive immune genetic algorithm (AIGA) and random forest (RF) for the optimal modeling of the green ammonia production process. AIGA is responsible for intelligently screening key production parameters, whereas RF constructs a prediction model to predict green ammonia production yields under different feed loads. An example analysis on the Unisim software platform verifies the AIGA‐RF model, which maintains high accuracy even under varying production loads, significantly outperforming other algorithm combinations. The method opens new pathways for precise control and efficiency enhancement in green ammonia production.