Prediction andOptimization of Ammonia Removal inDirect Aeration Process Based on Wastewater Properties: An IntegratedExperimental and Machine Learning Approach

Alkali-free direct aeration is an effective and economic approach for ammonia recovery from wastewater by increasing the pH through CO₂ stripping. However, optimizing operating parameters is challenging due to its unknown adaptability to different wastewaters, leading to extensive trial-and-error experiments. To address this issue, a machine learning framework was developed to predict the ammonia removal efficiency, identifying the initial concentration ratio of dissolved inorganic carbon and total ammonia nitrogen (DIC_i/TAN_i) as a critical feature. Experimental results showed that increasing DIC_i/TAN_i from 0.28 to 1.15 intensified CO₂ stripping and pH rise, facilitating continuous NH₃ (aq) existence and boosting the TAN removal efficiency from 34.33% to 96.03%. From the insight of ammonia mass transfer, a higher aeration temperature and gas–liquid ratio promoted ammonia removal, with more significant enhancements under higher DIC_i/TAN_i due to their interactions. Machine learning methods were adopted to capture these interactions and make predictions. The eXtreme gradient boosting (XGBoost) algorithm accurately predicted TAN removal efficiency (<i>R</i>² = 0.981) with robust generalization. Finally, we proposed a conceptual closed-loop control framework based on the XGBoost model to achieve dynamic and real-time process optimization. Overall, the offered critical indicator and machine learning-assisted prediction model have significant guidance in the industrial application of direct aeration to recover ammonia from diverse wastewater.