Fast Bayesian filtering for wastewater treatment plants with inaccurate process noise statistics

Accurate state estimation of wastewater treatment plants is critical for optimizing wastewater treatment processes and reducing operating costs and energy consumption. Due to their large size and numerous state variables, these wastewater treatment plants are considered as high-dimensional systems. The complexity of wastewater treatment plants results in varying and complex process noise statistics, posing challenges for state estimation. This paper proposes a novel state estimation method for wastewater treatment plants subject to inaccurate process noise statistics. The high-dimensional state vector is partitioned into multiple state blocks based on the system architecture, and lost correlations between blocks are compensated by considering time-series correlations. Real-time modification of the process noise covariance matrix is applied to adaptively adjust the inaccurate process noise statistics and compensate for errors from block division. It is verified through simulations that the proposed Bayesian algorithm can achieve satisfactory estimation results while the computational cost is moderate.