The prediction of WWTP influent characteristics: Good practices and challenges

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22320%2F22%3A43924650" target="_blank" >RIV/60461373:22320/22:43924650 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S2214714422004536" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2214714422004536</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jwpe.2022.103009" target="_blank" >10.1016/j.jwpe.2022.103009</a>

Jazyk výsledku

angličtina

Název v původním jazyce

The prediction of WWTP influent characteristics: Good practices and challenges

Popis výsledku v původním jazyce

The prediction of influent characteristics using state-of-the-art mathematical models can help optimize wastewater treatment plants (WWTP) processes. However, WWTP operators lack experience with such models and the historical data necessary for their calibration; thus mathematical models for inflow prediction are barely used in practice. On the other hand, the scientific community has recently made great strides in developing predictive modeling approaches for estimating inflow quantity and quality. This review paper compares existing models based on the dataset used, modeling approach and targeted application. Due to the significant differences in data resolution used for calibration and variable mathematical approaches, it is impossible to define one universally correct modeling approach. Besides machine learning approaches such as ANN, hybrid modeling approaches are also capable of good approximations of water and wastewater treatment processes. Moreover, this review evaluated the accuracy and robustness of predictive models used in specific situations. To bridge the theory-to-practice gap, existing models need to be connected with real-time data transfer.

Název v anglickém jazyce

The prediction of WWTP influent characteristics: Good practices and challenges

Popis výsledku anglicky

The prediction of influent characteristics using state-of-the-art mathematical models can help optimize wastewater treatment plants (WWTP) processes. However, WWTP operators lack experience with such models and the historical data necessary for their calibration; thus mathematical models for inflow prediction are barely used in practice. On the other hand, the scientific community has recently made great strides in developing predictive modeling approaches for estimating inflow quantity and quality. This review paper compares existing models based on the dataset used, modeling approach and targeted application. Due to the significant differences in data resolution used for calibration and variable mathematical approaches, it is impossible to define one universally correct modeling approach. Besides machine learning approaches such as ANN, hybrid modeling approaches are also capable of good approximations of water and wastewater treatment processes. Moreover, this review evaluated the accuracy and robustness of predictive models used in specific situations. To bridge the theory-to-practice gap, existing models need to be connected with real-time data transfer.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

20801 - Environmental biotechnology

Projekt

<a href="/cs/project/SS01020210" target="_blank" >SS01020210: Využití cloud-computingu a prediktivní analýzy odpadní vody za účelem snížení emisí do vodního ekosystému</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Water Process Engineering

ISSN

2214-7144

e-ISSN

—

Svazek periodika

49

Číslo periodika v rámci svazku

103009

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

9

Strana od-do

nestrankovano

Kód UT WoS článku

000866142500009

EID výsledku v databázi Scopus

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