Abstract
Fouling of fine-pore diffusers can cause substantial aeration energy wastage. It remains challenging to monitor their condition and decide the optimal time for labour-intensive and costly cleaning actions. In this study, we show that data from standard sensors (airflow rate, dissolved oxygen concentration, pressure and airflow valve position), which are fed to simple models, can track the diffuser's condition. Additionally, the parameter estimation of diffuser dynamic wet pressure, oxygen transfer rate, respiration rate and the joint alpha fouling factor (αF) was facilitated by an active fault detection inspired method. The method executes a sequence with piecewise constant valve positions via the control system. As a result, airflow rates in a sequence similar to a staircase are obtained, which simplifies the estimation of dissolved oxygen dynamics and airflow rate dynamics. The proposed method was evaluated on a full scale over 18 months and successfully detected a reduced cleaning in the diffusers and several sensor-related disturbances. Ultimately, the findings motivate further research on how modelling combined with repetitive process disturbances can leverage data-driven insights from standard instrumentation.