Machine-learning-aided application of high-gravity technology to enhance ammonia recovery of fresh waste leachate

Abstract

Stripping is widely applied for the removal of ammonia from fresh waste leachate. However, the development of air stripping technology is restricted by the requirements for large-scale equipment and long operation periods. This paper describes a high-gravity technology that improves ammonia stripping from actual fresh waste leachate and a machine learning approach that predicts the stripping performance under different operational parameters. The high-gravity field is implemented in a co-current-flow rotating packed bed in multi-stage cycle series mode. The eXtreme Gradient Boosting algorithm is applied to the experimental data to predict the liquid volumetric mass transfer coefficient (K_La) and removal efficiency (η) for various rotation speeds, numbers of stripping stages, gas flow rates, and liquid flow rates. Ammonia stripping under a high-gravity field achieves η = 82.73% and K_La = 5.551 × 10^-4 s^-1 at a pH value of 10 and ambient temperature. The results suggest that the eXtreme Gradient Boosting model provides good accuracy and predictive performance, with R² values of 0.9923 and 0.9783 for K_La and η, respectively. The machine learning models developed in this study are combined with experimental results to provide more comprehensive information on rotating packed bed operations and more accurate predictions of K_La and η. The information mining behind the model is an important reference for the rational design of high-gravity-field-coupled ammonia stripping projects.

Keywords: Ammonia stripping; Efficient mass transfer; High-gravity technology; Machine learning; Waste leachate.