Mathematical Modeling of a Domestic Wastewater Treatment System Combining a Septic Tank, an Up Flow Anaerobic Filter, and a Constructed Wetland
| Autor: | Marycarmen Verduzco Garibay, Misael Sebastián Gradilla-Hernández, Carlos Yebra-Montes, José de Anda, Carolina Senés-Guerrero, Alberto Fernández del Castillo |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Biochemical oxygen demand
multiple linear regression lcsh:Hydraulic engineering differential neural network constructed wetland media_common.quotation_subject anaerobic filter Geography Planning and Development Septic tank Aquatic Science Biochemistry septic tank lcsh:Water supply for domestic and industrial purposes lcsh:TC1-978 Water Science and Technology Total suspended solids media_common lcsh:TD201-500 Chemical oxygen demand Environmental engineering modeling first-order kinetic wastewater treatment Wastewater Anaerobic filter Constructed wetland Environmental science Sewage treatment mass balance |
| Zdroj: | Water Volume 12 Issue 11 Water, Vol 12, Iss 3019, p 3019 (2020) |
| ISSN: | 2073-4441 |
| DOI: | 10.3390/w12113019 |
| Popis: | Systems combining anaerobic bioreactors with constructed wetlands (CW) have proven to be adequate and efficient for wastewater treatment. Detailed knowledge of removal dynamics of contaminants can ensure positive results for engineering and design. Mathematical modeling is a useful approach to studying the dynamics of contaminant removal in wastewater. In this study, water quality monitoring was performed in a system composed of a septic tank (ST), an up flow anaerobic filter (UAF), and a horizontal flow constructed wetland (HFCW). Biological oxygen demand (BOD5), chemical oxygen demand (COD), total Kjeldahl nitrogen (TKN), NH3, organic nitrogen (ON), total suspended solids (TSS), NO2&minus and NO3&minus were measured biweekly during a 3-month period. First-order kinetics, multiple linear regression, and mass balance models were applied for data adjustment. First-order models were useful to predict the outlet concentration of pollutants (R2 > 0.87). Relevant multiple linear regression models were found, which could be applied to facilitate the system&rsquo s monitoring and provide valuable information to control and improve biological and physical processes necessary for wastewater treatment. Finally, the values of important parameters ( and ) in mass-balance models were determined with the aid of a differential neural network (DNN) and an optimization algorithm. The estimated parameters indicated the high robustness of the treatment system since performance stability was found despite variations in wastewater composition. |
| Databáze: | OpenAIRE |
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