Dépôt institutionnel de l'universite Freres Mentouri Constantine 1
MODELLING BIOCHEMICAL OXYGEN DEMAND (BOD5) USING ARTIFICIAL NEURAL NETWORK BASED APPROACH: CASE STUDY OF SIDI MAROUANE WASTEWATER TREATMENT PLANT (WWTP) AT BENI HAROUN DAM RESERVOIR
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| dc.contributor.author | HEDDAM, Salim | |
| dc.contributor.author | LAMDA, Hilal | |
| dc.contributor.author | FILALI, Samir | |
| dc.date.accessioned | 2022-12-18T13:46:27Z | |
| dc.date.available | 2022-12-18T13:46:27Z | |
| dc.date.issued | 2015-11-24 | |
| dc.identifier.uri | http://depot.umc.edu.dz/handle/123456789/13589 | |
| dc.description.abstract | In this study, Multi-Layer Perceptron Neural Networks (MLPNN) and multiple linear regression (MLR) models were employed to predict the effluent Biochemical Oxygen Demand (BOD5) from a wastewater treatment plant (WWTP) of Sidi Marouane WWTP at Beni Haroun Dam Reservoir, Algeria. The two models are built using many variables, namely temperature of water (TE), conductivity (EC), water pH, suspended solids (SS), chemical oxygen demand (COD) and Biochemical oxygen demand (BOD5). | fr_FR |
| dc.language.iso | en | fr_FR |
| dc.publisher | Université Frères Mentouri - Constantine 1 | fr_FR |
| dc.subject | MLR and MLPNN | fr_FR |
| dc.subject | high statistical quality | fr_FR |
| dc.subject | MLPNN | fr_FR |
| dc.title | MODELLING BIOCHEMICAL OXYGEN DEMAND (BOD5) USING ARTIFICIAL NEURAL NETWORK BASED APPROACH: CASE STUDY OF SIDI MAROUANE WASTEWATER TREATMENT PLANT (WWTP) AT BENI HAROUN DAM RESERVOIR | fr_FR |
| dc.type | Article | fr_FR |
