Optimal Control of Wastewater Treatment Plants Using Economic-Oriented Model Predictive Dynamic Strategies

This study summarizes the findings and lessons learned from the start up and operation of a large wastewater treatment plant in warm climate under modified conditions. The treatment works comprise of a newly constructed sewage treatment plant located in the city of Dubai, U.A.E with an ultimate capacity of 300 Ml/d. The plant can be reviewed in three major parts; (i) liquid stream, (ii) sludge stream and (iii) odor control stream all having the latest state of the art technology and related equipments. The challenge in starting up the plant was related to the fact that only 20% of the flow was available in the form of hauled sewage to be hauled from septic tanks located in various parts of the city and also from the nearby industry which was uncontrollable. Furthermore, some parts of the plant were not ready to be commissioned on the day of the planned startup not to mention that effluent irrigation main was not complete to receive the treated sewage effluent (TSE) from the plant. A comprehensive commissioning plan was implemented and followed during the startup which was tailored according to the actual conditions. The plant commissioning was conducted in a phased sequence to cater for the expected and unexpected changes related to the inlet conditions. Commissioning and operation of the plant was carried for over 18 months with success considering the challenging and difficult conditions.

Dynamic modelling and simulation is increasingly being employed as an aid in the design and operation of wastewater treatment plants (WWTPs). In this paper a simulation model is used to investigate control of an activated sludge process (ASP). It is shown how the recycle and wastage flow rate variables can be employed to continually maintain plant operation in the presence wastewater load disturbances and plant changes. Considerations are also given as to how plant operation could be optimised in order to increase treatment capacity and reduce operating costs.

This article desribes a system for monitoring operation of domestic wastewater treatment plant. The system consists of wireless subsystem of data transmission from the plant and the software for acquiring and presentation of data. The description of problems connected with usage of the plant was also included as well as further work that can extend existing solution.

Sludge management is a very relevant aspect in the operation of Wastewater Treatment Plants (WWTPs). In activated sludge systems, it is common to have daily (or continuous) monitoring of total suspended solids in the aeration tank (MLSS). If such control is not properly performed, it can cause solids to wash out in the secondary sedimentation tank or significantly impact BOD (Biochemical Oxygen Demand) and nitrogen removal. There are many commercially available systems which can provide real-time monitoring of solids (mainly optical or ultrasound sensors). Even though commercially available (usually with a high cost), there are still issues related to the use of such sensors. The most important one is the progressive accumulation of solids, which cause measurement errors. In this work, the authors investigated the application of a low-cost US sensor for MLSS (mixed-liquor suspended solids) monitoring in two full-scale activated sludge WWTPs. The tested sensor was similar to a previously described device, which had been previously employed in a pilot-scale UASB reactor in Brazil. The main differences were related to an integrated treatment and acquisition system which allowed real-time treatment of the US wave as well as data acquisition at a predefined time. The values generated by the sensor were compared with a commercial optical sensor installed in the same WWTP and double-checked with periodic gravimetric tests. The results at a Leeuwarden WWTP showed that the measurements of the US sensor, the optical sensor, and gravimetric test did not present significant differences during the test period at a significance level of 5%. Absolute errors were on average 0.04% (US sensor) and 0.03% (optic sensor) of MLSS compared to the gravimetric test. Although the use of the tested US sensor for monitoring solids in WWTP is promising, there are still several improvements that need to be made to the sensor. These include implementing a more precise calibration frequency, establishing a cleaning routine, and preventing sensor fouling. Furthermore, the sensor still needs a more thorough cost–benefit analysis, which would help assess the practicality of implementing this technology in various WWTPs.

Real-time quantification of activated sludge concentration and viscosity through deep learning of microscopic images.

Operational experience from hybrid RO system at SAFI wastewater re-use RO plant

Application of instrumentation, control and automation (ICA) is a reasonable way to improve the operation of wastewater treatment plant (WWTP), which has been realized in China but is still underway. A survey was carried out in Taihu Lake basin to investigate the current situation of ICA application. The survey focuses on control variable monitoring, instrument maintenance, control strategies, control manners and facility types. Survey results show that the process variables have not been adequately monitored and used for process control. The instruments are lack of sufficient maintenance, which causes control systems unreliable or left unused. Set-point control strategies are widely adopted and implemented mainly relies on manual on-site manners. On/off controllers predominate in treatment units. The survey results can be used as a reference to understand and help increase the ICA levels in WWTPs of China.

The rapid identification of the amount and characteristics of chemical oxygen demand (COD) in influent water is critical to the operation of wastewater treatment plants (WWTPs), especially for WWTPs in the face of influent water with a low carbon/nitrogen (C/N) ratio. Given that, this study carried out batch kinetic experiments for soluble chemical oxygen demand (SCOD) and nitrogen degradation for three WWTPs and established machine learning (ML) models for the accurate prediction of the variation in SCOD. The results indicate that four different kinds of components were identified via parallel factor (PARAFAC) analysis. C1 (Ex/Em = 235 nm and 275/348 nm, tryptophan-like substances/soluble microbial by-products) contributes to the majority of internal carbon sources for endogenous denitrification, whereas C4 (230 nm and 275/350 nm, tyrosine-like substances) is crucial for readily biodegradable SCOD composition according to the machine learning (ML) models. Furthermore, the gradient boosting decision tree (GBDT) algorithm achieved higher interpretability and generalizability in describing the relationship between SCOD and carbon source components, with an R2 reaching 0.772. A Shapley additive explanations (SHAP) analysis of GBDT models further validated the above result. Undoubtedly, this study provided novel insights into utilizing ML models to predict SCOD through the measurements of the excitation–emission matrix (EEM) in specific Ex and Em positions. The results could help us to identify the degradation and transformation relationship between different kinds of carbon sources and nitrogen species in the wastewater treatment process, and thus provide a novel guidance for the optimized operation of WWTPs.

This book emphasizes the growing interest in the design and operation of large wastewater treatment plants throughout the past decades. The outstanding role of LWWTPs nowadays results from the shift of the world's population from rural to urbanized areas with centralized sewer systems. The urban population of the world has grown rapidly from 751 million in 1950 to 4.2 billion in 2018. Asia is home to 54% of the world's urban population, followed by Europe and Africa with 13% each. Today, 55% of the world's population lives in urban areas, a proportion that is expected to increase to 68% by 2050. Projections show that urbanization, combined with the overall growth of the world's population could add another 2.5 billion people to urban areas by 2050, with close to 90% of this increase taking place in Asia and Africa. Topics covered in this book include operational data and research centred around large-scale applications of innovative technologies and approaches; topics connected with the design and operation of LWWTPs, such as data acquisition for design purposes, application of mathematical models, nitrogen removal optimization and financial sustainability. Emerging topics of interest are also examined, such as the removal of micropollutants and microplastics, greenhouse gas emissions, activated sludge granulation and the effect of street thawing salt on plants. In Focus–a book series that showcases the latest accomplishments in water research. Each book focuses on a specialist area with papers from top experts in the field. It aims to be a vehicle for in-depth understanding and inspire further conversations in the sector.

Effects of microplastics on the properties of different types of sewage sludge and strategies to overcome the inhibition: A review

The operation of waste-water treatment plant is usually affected by numerous factors as biological, physical and chemical factors. The regulation of dissolved oxygen is a critical step for the plant's control and efficiency. The performance and design of a Neural Networks based Model Predictive Controller for DO (Dissolved Oxygen) control in a waste-water treatment plant are reported in this research. The novelty of present method is to address distributed system of five reactors in series as a single-lumped system with 65-ODE equations due to slow process dynamics. The present Neural Networks based MPC (Model Predictive Controller) happened to be faster response than conventional PID controller. The closed loop performance is evaluated through simulations using MATLAB and SIMULINK.KeywordsWaste-water treatment plantDissolved oxygen concentrationNeural networks based model predictive controller

Effects of chromium on activated sludge and on the performance of wastewater treatment plants: A review

High salinity in sewage sludge can affect not only the operation of wastewater treatment plants (WWTPs) but also the quality of treated water generated, thereby limiting its downstream reuse. Using data on geochemical parameters, both for the central WWTP in Abu Dhabi, UAE, and literature values for potential regional saline water sources (e.g., shallow groundwater and regional Arabian Gulf seawater), a variety of chemical fingerprinting diagnostic ratios were calculated and plotted in order to determine the source of salinity in the municipal sewage. Data were compared with data from a regional WWTP that was not impacted by salinity. Monitoring data demonstrated persistently elevated levels of salinity in the municipal wastewater arriving at the central WWTP from the city. Dilution/concentration analysis using a conductivity vs. chloride plot showed both potential sources, i.e. Arabian Gulf seawater and coastal hypersaline groundwater, as feasible sources of wastewater salinization. Further diagnostic analysis using a Panno Plot indicated that coastal groundwater was the only likely source of salinization of municipal sewage. Additional confirmation of the identity of the source and the extent of mixing using different lines of evidence like stable isotope ratios is recommended for future study.

In this study, Activated Sludge Model No. 1 (ASM1) was used for modelling of wastewater treatment plant. The operational data of municipal wastewater treatment plant (WWTP) were collected and treated. Experimental data and biological treatment technology were structured according to the mathematical model requirements. They were implemented into computer scheme of the plant. Very good agreement between the experimental and calculated MLSS and effluent concentrations of the monitored pollution indicators was obtained after calibration of the model. The calibrated model was used to analyze the influence of oxygen concentration in the reactor and the influence of the prepared substitution of anaerobic sludge stabilization by aerobic technology on effluent quality. Changes of oxygen concentration significantly influenced the effluent values of ammonium nitrogen and nitrate nitrogen. Considerable increase of MLSS resulted from dynamic simulations performed for biological treatment with aerobic sludge stabilization. In both cases the results of dynamic simulation indicate that the WWTP will comply with the effluent standard values.

Prediction of effluent quality in a wastewater treatment plant by dynamic neural network modeling