| Revista: | Journal of applied research and technology |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000427849 |
| ISSN: | 1665-6423 |
| Autores: | Marzbali, Mojtaba Hedayati1 Esmaieli, Mohamad1 |
| Instituciones: | 1University of Tehran, School of Chemical Engineering, Teherán. Irán |
| Año: | 2017 |
| Periodo: | Oct |
| Volumen: | 15 |
| Número: | 5 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Experimental, aplicado |
| Resumen en inglés | This study investigates the use of synthesized mesoporous carbon in the fixed bed adsorption, as a promising process, to eliminate tetracycline from wastewater. In order to study the adsorptive capability of adsorbent, particles were embedded in a laboratory-scale Pyrex glass tube. An increase in initial concentration and decrease in bed height and flow rate led to the higher adsorption capacity. The highest bed capacity of 76.97 mg g−1 was obtained using 4 cm bed depth, 4 mL min−1 and 50 mg L−1 influent concentration. The initial part of breakthrough curve perfectly matched the Adams-Bohart model at all experimental conditions. However, it was anticipated that Yoon-Nelson model could predict the whole curve acceptably, the results showed an inaccurate fitting. Therefore, the adaptive neuro-fuzzy inference system (ANFIS) was used to predict the breakthrough curve using data series of adsorption experiments. This model indicated a good statistical prediction in terms of relative errors |
| Disciplinas: | Química |
| Palabras clave: | Química farmacéutica, Ingeniería química, Carbón activado, Columnas de absorción, Tetraciclina, Aprendizaje de máquinas |
| Keyword: | Medicinal chemistry, Chemical engineering, Activated carbon, Tetracycline, Adsorption columns, Machine learning |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
