Estimation of combined sewer overflow discharge: a software sensor approach based on local water level measurements

A learning-based approach towards the data-driven predictive control of combined wastewater networks - An experimental study

Smart control in water systems aims to reduce the cost of infrastructure expansion by better utilizing the available capacity through real-time control. The recent availability of sensors and advanced data processing is expected to transform the view of water system operators, increasing the need for deploying a new generation of data-driven control solutions. To that end, this paper proposes a data-driven control framework for combined wastewater and stormwater networks. We propose to learn the effect of wet- and dry-weather flows through the variation of water levels by deploying a number of level sensors in the network. To tackle the challenges associated with combining hydraulic and hydrologic modelling, we adopt a Gaussian process-based predictive control tool to capture the dynamic effect of rain and wastewater inflows, while applying domain knowledge to preserve the balance of water volumes. To show the practical feasibility of the approach, we test the control performance on a laboratory setup, inspired by the topology of a real-world wastewater network. We compare our method to a rule-based controller currently used by the water utility operating the proposed network. Overall, the controller learns the wastewater load and the temporal dynamics of the network, and therefore significantly outperforms the baseline controller, especially during high-intensity rain periods. Finally, we discuss the benefits and drawbacks of the approach for practical real-time control implementations.

Stochastic Determination of Combined Sewer Overflow Loads for Decision-Making Purposes and Operational Follow-Up

Characterizing the emissions and impact of combined sewer overflows (CSOs) remains one of the key challenges in the field of urban wastewater. Considering the large number of existing CSOs, decision-makers need a pragmatic approach that allows fairly easy, hands-on determination of emissions (particularly loads) without compromising accuracy. This philosophy is incorporated in the Cockle tool presented here, which uses stochastically processed input from a vast amount of pre-registered water quality data (pollutant concentrations) in combination with spill flow time series either generated from hydrodynamic models or converted from monitored overflow water levels. Uncertainty is intrinsically covered by the statistical output range of the reported results. As a fully automated tool, Cockle allows to readily assess emissions within a chosen time frame, facilitating more accurate guidance for further remediation actions and/or mapping of the current state for operational follow-up.

Foul sewer model development using geotagged information and smart water meter data

Hydraulic modeling of a foul sewer system (FSS) enables a better understanding of the behavior of the system and its effective management. However, there is generally a lack of sufficient field measurement data for FSS model development due to the low number of in-situ sensors for data collection. To this end, this study proposes a new method to develop FSS models based on geotagged information and water consumption data from smart water meters that are readily available. Within the proposed method, each sewer manhole is firstly associated with a particular population whose size is estimated from geotagged data. Subsequently, a two-stage optimization framework is developed to identify daily time-series inflows for each manhole based on physical connections between manholes and population as well as sewer sensor observations. Finally, a new uncertainty analysis method is developed by mapping the probability distributions of water consumption captured by smart meters to the stochastic variations of wastewater discharges. Two real-world FSSs are used to demonstrate the effectiveness of the proposed method. Results show that the proposed method can significantly outperform the traditional FSS model development approach in accurately simulating the values and uncertainty ranges of FSS hydraulic variables (manhole water depths and sewer flows). The proposed method is promising due to the easy availability of geotagged information as well as water consumption data from smart water meters in near future.

Water Quality-Based Double-Gates Control Strategy for Combined Sewer Overflows Pollution Control

The combined sewer overflows (CSO) pollution has caused many serious environmental problems, which has aroused a worldwide concern. Traditional interception-storage measures, which exhibit the disadvantages of the larger storage tank volume and the low concentration, cannot efficiently control the CSO pollution. To solve this problem, a water quality-based double-gate control strategy based on the pollution based real-time control (PBRTC) rule was proposed, and the chemical oxygen demand (COD) concentration was taken as the control index. A case study was carried out in Fuzhou, China as an example, in which the hydraulic and water quality model were constructed to evaluate two schemes. According to the results, compared to the traditional scheme, the double-gate scheme can not only reduce the storage tank volume by 1515 m3, but also increase the average COD interception rate by 1.84 times, thus ensuring the effective and stable operation of the facility. Furthermore, the traditional scheme and the double-gate scheme were evaluated under design rainfall beyond the design return period, which confirmed the high performance of the double-gate scheme in controlling CSO pollution.

Real-time foul sewer hydraulic modelling driven by water consumption data from water distribution systems

Real-time hydraulic modelling can be used to address a wide range of issues in a foul sewer system and hence can help improve its daily operation and maintenance. However, the current bottleneck within real-time FSS modelling is the lack of spatio-temporal inflow data. To address the problem, this paper proposes a new method to develop real-time FSS models driven by water consumption data from associated water distribution systems (WDSs) as they often have a proportionally larger number of sensors. Within the proposed method, the relationship between FSS manholes and WDS water consumption nodes are determined based on their underlying physical connections. An optimization approach is subsequently proposed to identify the transfer factor k between nodal water consumption and FSS manhole inflows based on historical observations. These identified k values combined with the acquired real-time nodal water consumption data drive the FSS real-time modelling. The proposed method is applied to two real FSSs. The results obtained show that it can produce simulated sewer flows and manhole water depths matching well with observations at the monitoring locations. The proposed method achieved high R², NSE and KGE (Kling-Gupta efficiency) values of 0.99, 0.88 and 0.92 respectively. It is anticipated that real-time models developed by the proposed method can be used for improved FSS management and operation.

Nitrifying moving bed biofilm reactor: Performance at low temperatures and response to cold-shock

In contrast with suspended growth systems, attached growth technologies such as the moving bed biofilm reactors (MBBR) have recently demonstrated significant nitrification rates at temperatures as low as 1 °C. The purpose of this study was to investigate the performance of the nitrifying MBBR system at elevated municipal concentrations with exposures to low temperatures and cold-shock conditions down to 1 °C using an enhanced temperature-controlled room. A removal rate of 98.44 ± 4.69 gN·m^-3·d^-1 was identified as the intrinsic rate of nitrifying MBBR systems at 1 °C and was proposed as the conservative rate for low temperature design. A temperature threshold at which attached growth nitrification displayed a significant decrease in kinetics was identified between 2 °C and 4 °C. Arrhenius correction coefficients of 1.086 and 1.09 previously applied for low temperature nitrifying MBBR systems resulted in conservative modeled removal rates on average 21% lower than the measured rates. Thus, an Arrhenius correction coefficient of 1.049 is proposed between the temperatures of 10 °C and 4 °C and another correction coefficient of 1.149 to model rates at 1 °C. For the transition from 4 °C to 1 °C, the adjustment of a previously reported Theta model is proposed in this study to account for exposure time at low temperatures; with the modified model showing strong correlation with measured rates (R² = 0.88). Finally, a comparison of nitrification kinetics between MBBR systems acclimatized to 1 °C and systems that are cold-shocked to 1 °C demonstrated that shocked removal rates are 21% lower.

Sweating the assets - The role of instrumentation, control and automation in urban water systems

Instrumentation, control and automation (ICA) are currently applied throughout the urban water system at water treatment plants, in water distribution networks, in sewer networks, and at wastewater treatment plants. However, researchers and practitioners specialising in respective urban water sub-systems do not frequently interact, and in most cases to date the application of ICA has been achieved in silo. Here, we review start-of-the-art ICA throughout these sub-systems, and discuss the benefits achieved in terms of performance improvement, cost reduction, and more importantly, the enhanced capacity of the existing infrastructure to cope with increased service demand caused by population growth and continued urbanisation. We emphasise the importance of integrated control within each of the sub-systems, and also across the entire urban water system. System-wide ICA will have increasing importance with the growing complexity of the urban water environment in cities of the future.

Robust model for estimating pumping station characteristics and sewer flows from standard pumping station data

Flow data represent crucial input for reliable diagnostics of sewer functions and identification of potential problems such as unwanted inflow and infiltration. Flow estimates from pumping stations, which are an integral part of most separate sewer systems, might help in this regard. A robust model and an associated optimization procedure is proposed for estimating inflow to a pumping station using only registered water levels in the pump sump and power consumption. The model was successfully tested on one month of data from a single upstream station. The model is suitable for identification of pump capacity and volume thresholds for switching the pump on and off. These are parameters which are required for flow estimation during periods with high inflows or during periods with flow conditions triggering pump switching on and off at frequencies close to the temporal resolution of monitored data. The model is, however, sensitive within the transition states between emptying and filling to observation errors in volume and on inflow/outflow variability.

DSM-flux: A new technology for reliable Combined Sewer Overflow discharge monitoring with low uncertainties

In the past ten years, governments from the European Union have been encouraged to collect volume and quality data for all the effluent overflows from separated stormwater and combined sewer systems that result in a significant environmental impact on receiving water bodies. Methods to monitor and control these flows require improvements, particularly for complex Combined Sewer Overflow (CSO) structures. The DSM-flux (Device for Stormwater and combined sewer flows Monitoring and the control of pollutant fluxes) is a new pre-designed and pre-calibrated channel that provides appropriate hydraulic conditions suitable for measurement of overflow rates and volumes by means of one water level gauge. In this paper, a stage-discharge relation for the DSM-flux is obtained experimentally and validated for multiple inflow hydraulic configurations. Uncertainties in CSO discharges and volumes are estimated within the Guide to the expression of Uncertainty in Measurement (GUM) framework. Whatever the upstream hydraulic conditions are, relative uncertainties are lower than 15% and 2% for the investigated discharges and volumes, respectively.