Enhancing Operation of a Sewage Pumping Station for Inter Catchment Wastewater Transfer by Using Deep Learning and Hydraulic Model

This paper presents a novel Inter Catchment Wastewater Transfer (ICWT) method for mitigating sewer overflow. The ICWT aims at balancing the spatial mismatch of sewer flow and treatment capacity of Wastewater Treatment Plant (WWTP), through collaborative operation of sewer system facilities. Using a hydraulic model, the effectiveness of ICWT is investigated in a sewer system in Drammen, Norway. Concerning the whole system performance, we found that the S{\o}ren Lemmich pump station plays a vital role in the ICWT framework. To enhance the operation of this pump station, it is imperative to construct a multi-step ahead water level prediction model. Hence, one of the most promising artificial intelligence techniques, Long Short Term Memory (LSTM), is employed to undertake this task. Experiments demonstrated that LSTM is superior to Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), Feed-forward Neural Network (FFNN) and Support Vector Regression (SVR)

DeepCSO: Forecasting of Combined Sewer Overflow at a Citywide Level using Multi-task Deep Learning

Combined Sewer Overflow (CSO) is a major problem to be addressed by many cities. Understanding the behavior of sewer system through proper urban hydrological models is an effective method of enhancing sewer system management. Conventional deterministic methods, which heavily rely on physical principles, is inappropriate for real-time purpose due to their expensive computation. On the other hand, data-driven methods have gained huge interests, but most studies only focus on modeling a single component of the sewer system and supply information at a very abstract level. In this paper, we proposed the DeepCSO model, which aims at forecasting CSO events from multiple CSO structures simultaneously in near real time at a citywide level. The proposed model provided an intermediate methodology that combines the flexibility of data-driven methods and the rich information contained in deterministic methods while avoiding the drawbacks of these two methods. A comparison of the results demonstrated that the deep learning based multi-task model is superior to the traditional methods

Approbation of the texture analysis imaging technique in the wastewater treatment plant

publishedVersio

What effect does rehabilitation of wastewater pipelines have on the share of infiltration and inflow water (I/I-water)?

Infiltration and inflow water (I/I-water) is a big challenge in sewage systems in many countries. I/I-water above an acceptable level indicates that the sewage system is not functioning properly. I/I-water leads to increased pumping costs and increased sewage overflow, leading to increased pollution of the receiving waters. Many rehabilitation projects are driven by the need to reduce the share of I/I-water and common measures are to replace pipes and manholes. The share of I/I-water is predominantly driven by rainfall. This makes it difficult to document the efficiency of mitigating measures. One way to address this issue is to compare data from rehabilitation areas to areas where no measures have been implemented. Three rehabilitation areas in Asker Municipality, Norway, were successfully assessed by applying this approach. Asker has a 100% separate system. The strategy to reduce I/I-water in Asker Municipality was to rehabilitate sewage mains, either by full replacement or lining the old pipes, and replacement of manholes. The assessment shows that rehabilitation of selected municipal pipes, pipes proven to be in bad condition through closed circuit TV inspection, reduced the share of I/I-water only to a limited extent. Since the rehabilitation done was not a complete replacement of all pipes and manholes, the limited effects are assumed to be caused by the water finding other ways into the system. In separate systems other measures than renovations of pipes should be considered when aiming to reduce I/I-water.publishedVersio

Computational thermodynamic analysis of the interaction between coagulants and monosaccharides as a tool to quantify the fouling potential reduction in the biofilm membrane bioreactor

The membrane bioreactor (MBR) and the biofilm membrane bioreactor (BF-MBR) are among key solutions to water scarcity; however, membrane fouling is the major bottleneck for any expansion of these technologies. Prepolymerized aluminum coagulants tend to exhibit the greatest extent of fouling alleviation, with the reduction of soluble microbial products (SMPs) being among the governing mechanisms, which, nevertheless, has been poorly understood. This current study demonstrates that the investigation of the chemical coordination of monosaccharides, which are the major foulants in MBR and BF-MBR, to the main hydrolysis species of the prepolymerized aluminum coagulant, is among the key approaches to the comprehension of the fouling mitigation mechanisms in BF-MBR. Quantum chemical and thermodynamic calculations, together with the multivariate chemometric analysis, allowed the team to determine the principal mechanisms of the SMPs removal, understand the thermodynamic patterns of fouling mitigation, develop the model for the prediction of the fouling mitigation based on the thermodynamic stability of the inorganic-organic complexes, and classify these complexes into thermodynamically stable and less stable species. The results of the study are practically significant for the development of plant surveillance and automated process control with regard to MBR and BF-MBR systems

State of the Art of Online Monitoring and Control of the Coagulation Process

Coagulation is an essential process for the removal of suspended and colloidal material from water and wastewater. However, no comprehensive or universally accepted mathematical description of the process has been developed so far. Therefore, process optimization and control is usually based on data from jar tests and simple flow-proportional dosing concepts, while more accurate concepts based on water quality parameters that can be measured online are emerging. In addition, there have been attempts to develop software sensors and control schemes that involve advanced mathematical analyses of these parameters. The paper presents an overview of the parameters and physical sensors that are used for feed-forward and feed-backward control schemes and the experiences that have been made with their implementation. Moreover, the development and use of software sensors is described. Finally, the practical applications of different control techniques are given in order to illustrate the state of the art of coagulation control. Some thoughts about research needs conclude this review

Economic Model Predictive Control for optimal struvite recovery

publishedVersio

Explicit and interpretable nonlinear soft sensor models for influent surveillance at a full-scale wastewater treatment plant

In wastewater treatment plants, the most adopted sensors are those with the properties of low cost and fast response. Soft sensors are alternative solutions to the hardware sensor for online monitoring of hard-tomeasure variables, such as chemical oxygen demand (COD) and total phosphorus (TP). The purpose of this study is to obtain a modelling approach which is able to identify the nonlinearity of influent and explain the correlation of inputs-outputs. Thus, the variation of influent characteristics was investigated at the first stage, which provided the basis to build global and local multiple linear regression models. Secondly, a nonlinear modelling tool multivariate adaptive regression splines (MARS) was applied for influent COD and TP prediction. Satisfactory prediction accuracy was obtained in terms of root mean square error (RMSE) and R2. Unlike other machine learning techniques which are “black box” models, MARS provided interpretable models which explained the nonlinearity and correlation of inputs-outputs. The MARS models can be used not only for prediction, but also to provide insight of influent variation.acceptedVersio

State of the Art of Online Monitoring and Control of the Coagulation Process

Coagulation is an essential process for the removal of suspended and colloidal material from water and wastewater. However, no comprehensive or universally accepted mathematical description of the process has been developed so far. Therefore, process optimization and control is usually based on data from jar tests and simple flow-proportional dosing concepts, while more accurate concepts based on water quality parameters that can be measured online are emerging. In addition, there have been attempts to develop software sensors and control schemes that involve advanced mathematical analyses of these parameters. The paper presents an overview of the parameters and physical sensors that are used for feed-forward and feed-backward control schemes and the experiences that have been made with their implementation. Moreover, the development and use of software sensors is described. Finally, the practical applications of different control techniques are given in order to illustrate the state of the art of coagulation control. Some thoughts about research needs conclude this review