Author/Authors
lamzouri, k. institut agronomique et vétérinaire hassan ii, morocco , mahi, m. office national de l electricité et de l eau potable-branche electricité (onee) - institut international de l’eau et de l’assainissement (iea), Morocco , latrach, l. université cadi ayyad - centre national d’études et de recherches sur l’eau et l’éner gie (cneree), Maroc , ouattar, s. institut agronomique et vétérinaire hassan ii, Morocco , bartali, e.h. institut agronomique et vétérinaire hassan ii, Morocco , mandi, l. université cadi ayyad - centre national d’études et de recherches sur l’eau et l’éner gie (cneree), Maroc
Title Of Article
Quantitative evaluation of the effect of parameters affecting biological and physicochemical phosphate removal from wastewaters in a Multi-Soil-Layering system
شماره ركورد
39605
Abstract
Wastewater disposal is a serious problem in Moroccan rural area. Discharged with high levels of phosphorus and nitrogen can result in eutrophication of receiving waters. Biological processes are the most adapted alternative to the needs of these areas, such as the Multi-Soil-Layering (MSL) system. The process of rural wastewater treatment by MSL, which is an innovative system used for the first time in Morocco, was studied by modelling the relationships between a set of environmental factors and total phosphorus removed, based upon 153 sampling. Three MSL pilot plants, constructed in three 36 cm × 30 cm × 65 cm plastic boxes, were continuously fed with domestic wastewater, with different hydraulic loading rate (HLR) of 250, 500 and 1000 l/m2/day. This study was to investigate and quantify the effect of parameters affecting biological and physico-chemical phosphate removal from wastewaters in this system, using neural networks (NNs) and multiple regression analysis (MRA). The results show the influence of the hydraulic loading rate (HLR), Hydrogen potential (pH), phosphorus load (PL), nitrite (NO2 --N), Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD5), and the Nitrate-nitrogen (NO3^– -N) in the phosphorus removal with a contribution of 36, 16, 15, 12, 9, 7 and 6% respectively.
From Page
313
NaturalLanguageKeyword
Multiple regression analysis , multi , soil , layering , neural networks , phosphorus removal
JournalTitle
Revue Marocaine des Sciences Agronomiques et Vétérinaires
To Page
318
JournalTitle
Revue Marocaine des Sciences Agronomiques et Vétérinaires
Link To Document