Modeling nutrient removal and energy consumption in an advanced activated sludge system under uncertainty

Szeląg, Bartosz; Kiczko, Adam; Zaborowska, Ewa; Mannina, Giorgio; Mąkinia, Jacek

doi:10.1016/j.jenvman.2022.116040

Cited by 9 publications

(2 citation statements)

References 46 publications

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“…Furthermore, critical aspects such as optimizing the selection of sample collection points within the WWTP, acquiring measurement data for model calibration, and considering the interactions between calibrated kinetic parameters have often been overlooked [Andraka et al, 2018]. The uncertainties associated with identified WWTP model parameters are not factored in during system construction, which can affect settings and simulation outcomes [Szeląg et al, 2022]. The procedures devised for calibrating and optimizing model parameters often involve iteratively adjusting parameter values until a strong correlation between calculated results and measurements is achieved [Mąkinia and Zaborowska, 2020].…”

Section: Balancing Economic and Ecological Aspects: A Multi-criteria ...mentioning

confidence: 99%

“…Machine learning methods, including neural networks and their various adaptations, are utilized to achieve this, reducing measurement time and equipment operating costs. The presented methodology also addresses uncertainty analysis using the GLUE method during model calibration [Mannina et al, 2010;Szeląg et al, 2022]. This uncertainty pertains to the interactions among identified parameters and their impact on simulation results.…”

Section: Balancing Economic and Ecological Aspects: A Multi-criteria ...mentioning

confidence: 99%

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Innovations in Wastewater Treatment: Harnessing Mathematical Modeling and Computer Simulations with Cutting-Edge Technologies and Advanced Control Systems

Drewnowski,

Szeląg,

Sabba

et al. 2023

J. Ecol. Eng.

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The wastewater treatment landscape in Central Europe, particularly in Poland, has undergone a profound transformation due to European Union (EU) integration. Fueled by EU funding and rapid technological advancements, wastewater treatment plants (WWTPs) have adopted cutting-edge control methods to adhere to EU Water Framework Directive mandates. WWTPs contend with complexities such as variable flow rates, temperature fluctuations, and evolving influent compositions, necessitating advanced control systems and precise sensors to ensure water quality, enhance energy efficiency, and reduce operational costs. Wastewater mathematical modeling provides operational flexibility, acting as a virtual testing ground for process enhancements and resource optimization. Real-time sensors play a crucial role in creating these models by continuously monitoring key parameters and supplying data to predictive models. These models empower real-time decision-making, resulting in minimized downtime and reduced expenses, thus promoting the sustainability and efficiency of WWTPs while aligning with resource recovery and environmental stewardship goals. The evolution of WWTPs in Central Europe is driven by a range of factors. To optimize WWTPs, a multi-criteria approach is presented, integrating simulation models with data mining methods, while taking into account parameter interactions. This approach strikes a balance between the volume of data collected and the complexity of statistical analysis, employing machine learning techniques to cut costs for process optimization. The future of WWTP control systems lies in "smart process control systems", which revolve around simulation models driven by real-time data, ultimately leading to optimal biochemical processes. In conclusion, Central Europe's wastewater treatment sector has wholeheartedly embraced advanced control methods and mathematical modeling to comply with EU regulations and advance sustainability objectives. Real-time monitoring and sophisticated modeling are instrumental in driving efficient, resource-conscious operations. Challenges remain in terms of data accessibility and cost-effective online monitoring, especially for smaller WWTPs.

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Section: Balancing Economic and Ecological Aspects: A Multi-criteria ...mentioning

confidence: 99%

Section: Balancing Economic and Ecological Aspects: A Multi-criteria ...mentioning

confidence: 99%

Innovations in Wastewater Treatment: Harnessing Mathematical Modeling and Computer Simulations with Cutting-Edge Technologies and Advanced Control Systems

Drewnowski,

Szeląg,

Sabba

et al. 2023

J. Ecol. Eng.

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Machine learning assisted combined systems of wastewater treatment plants with constructed wetlands optimal decision-making

Dai,

Pang,

Zhao

et al. 2024

Bioresource Technology

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Application of a generalized hybrid machine learning model for the prediction of H2S and VOCs removal in a compact trickle bed bioreactor (CTBB)

Barbusiński,

Szeląg,

Parzentna-Gabor

et al. 2024

Chemosphere

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Modeling nutrient removal and energy consumption in an advanced activated sludge system under uncertainty

Cited by 9 publications

References 46 publications

Innovations in Wastewater Treatment: Harnessing Mathematical Modeling and Computer Simulations with Cutting-Edge Technologies and Advanced Control Systems

Innovations in Wastewater Treatment: Harnessing Mathematical Modeling and Computer Simulations with Cutting-Edge Technologies and Advanced Control Systems

Machine learning assisted combined systems of wastewater treatment plants with constructed wetlands optimal decision-making

Application of a generalized hybrid machine learning model for the prediction of H2S and VOCs removal in a compact trickle bed bioreactor (CTBB)

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