Nitrogen recovery using a membrane contactor: Modelling nitrogen and pH evolution
[EN] A hollow fibre membrane contactor has been applied for nitrogen recovery from anaerobic digestion supernatant at different operating conditions obtaining nitrogen recovery efficiencies over 99 %. A mathematical model able to represent the time evolution of pH and nitrogen concentration during t...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/161836 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/161836 |
| Access Level: | acceso abierto |
| Palabra clave: | Ammonia recovery Membrane contactor for nitrogen recovery Nitrogen recovery modelling Nutrient recovery from anaerobic digestion PH modelling TECNOLOGIA DEL MEDIO AMBIENTE |
| Sumario: | [EN] A hollow fibre membrane contactor has been applied for nitrogen recovery from anaerobic digestion supernatant at different operating conditions obtaining nitrogen recovery efficiencies over 99 %. A mathematical model able to represent the time evolution of pH and nitrogen concentration during the recovery process is presented in this paper. The developed model accurately reproduced the results obtained in 26 experiments carried out at different pH values (from 9 to 11), temperatures (from 25 to 35 degrees C), membrane surfaces (from 1.2 to 2.4 m(2)) and feed flow rates (from 0.33 x 10(-5) to 5.83 x 10(-5) m(3)/s) predicting the variations in nitrogen recovery rates measured at the different operating conditions evaluated. Furthermore, due to the combination of nitrogen and pH modelling, the model is able to predict the variations in OH-concentration (alkali addition) required to increase and maintain the pH during the process. Thus, this model is a useful tool for process design and optimisation since it can predict nitrogen recovery rates and reagents consumption at different operational conditions such as flow rate, pH, membrane surface and temperature. |
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