University of Bath

Stormflow management in wastewater treatment plants

This project examines and identifies a sensor and process model for hindered-compression settling velocity.

An image of flooding in a field.
Advanced sensor-based control of wastewater treatment plants (WWTPs) can help in decreasing the untreated sewage discharge into recipient water bodies during stormflow conditions, and in decreasing their pollutant load.

The aims set in this study are (1) identifying a process model for hindered-compression settling velocity for which all parameters can be estimated using the sensor data with both good settling and filamentous bulking; (2) evaluating the feasibility of the sensor setup as a means to infer experimental data on compressive solid stress; (3) assessing uncertainty sources associated with the model identification method and the settling column design; and (4) evaluating and validating the new settling velocity process model using the triangulation approach.

Project outline

Two of the key questions regarding secondary settling in activated sludge wastewater treatment are (a) Does a process model exist for which all hindered and compression settling velocity parameters can be estimated using experimental data?; (b) What is the minimum data that need be inferred, from a settling sensor setup to identify process models?” This international research effort aimed to address these questions by carrying out a comprehensive practical identifiability assessment of constitutive functions for hindered and compression settling velocity using sensor data and one-dimensional (1-D) simulation models. For model validation, the triangulation technique was used, including independent laboratory- and full-scale measurements as well as 1-D and computational fluid dynamics (CFD) simulation models.

Science

The increasing frequency of hydraulic shock events – as a result of climate change – necessitates more effective operation and control of secondary settling tanks (SSTs) in wastewater treatment plants (WWTPs) in the future. Theoretically, the maximum permissible SST loading capacity determines the maximum permissible hydraulic WWTP load. However, the SST capacity varies with sludge settleability, and thus process operation and control necessitates effective sensor technology and identifiable simulation models – two focal areas chosen for this project. Settling sensors should ideally provide experimental data for estimating settling velocity parameters; yet, up to date, no simple and robust methods exist to calibrate hindered and compression settling parameters. Additionally, parameter identifiability of activated sludge settling velocity models was shown to be a significant challenge in the past.

As significant outcomes of this project, a pseudo 2P and a 3P exponential term were identified to describe hindered settling velocity and the compressive solids stress gradient, respectively. Three parameters are required to estimate using LHSS – all practically identifiable using the data obtained using the innovative multi-probe sensor setup. Only one of the compression settling parameters shows significant dependence on initial solids concentration. The process model developed was validated using the triangulation approach, including independent laboratory- and full-scale measurement data and using 1-D and CFD simulation models. Negligible uncertainties – assessed by means of CFD simulations – introduced by the 1-D parameter estimation approach were obtained, thus suggesting the reliability of the practical identifiability assessment approach. The multi-probe settling sensor setup developed can be used to quantify the solid stress ()-gradient, and future research should assess the benefits of using -gradient sensor data for settling model calibration.

Impact

Advanced sensor-based control of wastewater treatment plants (WWTPs) – facilitated by the use of settling sensors and reliable simulation models - can be helpful to significantly decrease the untreated sewage discharge into recipient water bodies during stormflow conditions, and thereby decreasing their pollutant load (e.g., nutrients, micropollutants, antibiotic resistance genes). Increased frequency of extreme weather patterns is expected to happen as a result of climate change that can potentially increase the frequency of peak-flow conditions in WWTPs. Therefore, there is an urgency in developing and implementing effective climate-change-adaptation means in WWTPs – an area directly addressed by the present research.