Adaptive optimization of natural coagulants using hybrid machine learning approach for sustainable water treatment.

The efficiency optimization methods for natural coagulants are often restricted due to non-scientific trial-and-error approaches. They are inaccurate in predicting the complex interactions of jet mixing parameters, coagulant dosage, and environmental conditions. To overcome these obstacles, this research paper proposes advanced hybrid models in machine learning to enhance flocculation efficiency. We use the CatBoost model with the NTK to learn the intricate nonlinear interactions among jet velocity, mixing time, coagulant dose, pH, and turbidity. CatBoost is effective for dealing with categorical data like diverse coagulants. Meanwhile, NTK boosts the model's generalization capability, especially when the sample size becomes small or experimental datasets are applied. Lastly, SOMs and MARS are used to identify pattern recognitions in tracing the crucial interaction among mixing parameters. Reinforcement learning techniques-that include DDPG and SAC for dynamic optimization of jet velocity, mixing time, and coagulant dosage-optimize the model in real time. Utilizing NAS and Hyperband to automate model tuning, the timestamp was reduced by 40%. The proposed models heavily improve the efficiency of the flocculation process by 20-25% and allow for a good predictive accuracy of 95-97%. Paramount, however, is that the model has interpretability properties assured by SHAP and counterfactual explanations, which would give actionable insights into the most influencing factors on the efficiency of flocculation. This work represents a substantial advancement for the discipline since it introduces robust, interpretable, and real-time optimization methods to offer a practical tool through which improvement of water treatment processes would be made both sustainable and efficient.