The prediction of WWTP influent characteristics: Good practices and challenges

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
The prediction of WWTP influent characteristics: Good practices and challenges
Original language description
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.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
20801 - Environmental biotechnology

Project
<a href="/en/project/SS01020210" target="_blank" >SS01020210: Machine Learning Approach Using Cloud Computing and Water Quality Prediction to Reduce Emmisions to the Water Ecosystems</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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