Application of TimeMachine Model in Predicting Operational Parameters of Wastewater Treatment Plants

Efficient operation of wastewater treatment plants is crucial for environmental protection and public health. However, accurately predicting key operational parameters has been a challenging task due to the complexity and dynamism of the wastewater treatment process. This study proposes an innovative time series prediction model, TimeMachine, based on the Mamba state space model, for long-term prediction of wastewater treatment plant operational parameters. The model effectively captures short-term fluctuations and long-term trends in wastewater treatment data, providing important data support for optimizing the operational management of wastewater treatment plants. We conducted extensive experiments on real wastewater treatment plant datasets, and the results show that the TimeMachine model outperforms existing state-of-the-art methods in terms of prediction accuracy, scalability, and computational efficiency. In particular, TimeMachine demonstrates significant advantages in predicting key parameters such as PAC dosing rate and effluent water quality. This study provides new insights into the application of advanced time series prediction models in the wastewater treatment field, contributing to improved operational efficiency, ensuring compliance with discharge standards, and optimizing resource utilization in wastewater treatment plants.