Search results

The purpose of this paper is to assess the water quality parameters resulting on: First, the flow direction in biofilters (ascending or descending), second, constructed wetland (CW) with local plant species and third, the combined system for the removal of organic matter and nutrients pollutants from water in arid regions.

An integrated system is presented and tested in situ with a vertical up-flow and down-flow biofilters. Two configurations schemes are followed by a three separated horizontal subsurface CWs: two planted with Phragmites australis, Typha latifolia and the third unplanted. The methodology is based on a statistical analysis of the collected data.

The present experiment demonstrated that the wetlands planted with P. australis and T. latifolia showed the highest removal. Moreover, T. latifolia performed better than P. australis for most of the parameters, notably in the first system, whereas the wetland efficiency indicated that P. australis contributed greatly to the removal of TP in the first system and NO₃-N in the second system. In general, for the highest removal efficiencies of the combined biofilters and wetlands system, the present study demonstrated that the first system performed better than the second for all the parameters.

The originality of the research is that it compares in situ two biofilter systems: vertical up-flow and down-flow biofilters. To avoid the effects of domestic wastewater that is discharged directly without treatment in the Oued Righ channel or in the lake, this integrated system can be one of the alternatives for wastewater treatment, as it reveals the need to protect aquatic ecosystems in arid regions, and can decrease the risks to human health and the environment.

Modeling Wastewater Treatment Plant (WWTP) constitutes an important tool for controlling the operation of the process and for predicting its performance with substantial influent fluctuations. The purpose of this paper is to apply an artificial neural network (ANN) approach with a feed-forward back-propagation in order to predict the ten-month performance of Touggourt WWTP in terms of effluent Chemical Oxygen Demand (CODeff).

The influent variables such as (pHinf), temperature (TEinf), suspended solid (SSinf), Kjeldahl Nitrogen (KNinf), biochemical oxygen demand (BODinf) and chemical oxygen demand (CODinf) were used as input variables of neural networks. To determine the appropriate architecture of the neural network models, several steps of training were conducted, namely the validation and testing of the models by varying the number of neurons and activation functions in the hidden layer, the activation function in output layer as well as the learning algorithms.

The better results were achieved with an architecture network [6-50-1], hyperbolic tangent sigmoid activation functions at hidden layer, linear activation functions at output layer and a Levenberg – Marquardt method as learning algorithm. The results showed that the ANN model could predict the experimental results with high correlation coefficient 0.89, 0.96 and 0.87 during learning, validation and testing phases, respectively. The overall results indicated that the ANN modeling approach can provide an effective tool for simulating, controlling and predicting the performance of WWTP.

This work is the first of its kind in this region due to the remarkable development in terms of population and agricultural activity in the region, which drove to the increase of water pollutants, so it is necessary to use the modern technologies to modeling and controlling of WWTP.

The purpose of this paper is to focus on the assessment of water resources in the Seybouse middle sub-basin. Analyses of water and various current uses are used to draw attention to the necessity of implementing water resources integrated management into a plan aiming at a rational exploitation.

Any sustainable management of water resources is closely dependent on the ability to accurately assess the quantity and quality of available water resources that are used as water supply for the population, agricultural production, industrial or energy. The analyzed and processed available data serve as database for integrated water resources management.

Analysis shows that the annual water supply is represented by 71 percent of the surface water resources and 29 percent of groundwater. The total volume of water used in the middle Seybouse basin is estimated at 36.22 hm3/yr. The predicted water needs are estimated to 79.19 hm3/yr in 2030. The groundwater of the Guelma alluvial aquifer exhibits a calcium chloride-facies general trend. The evolution of the chemical elements is related mainly to the geological nature of the reservoir lithologies. The heavy metals do not exhibit clear anomalies, but the surface water of the majority of streams is heavily infected with bacterial germs.

The obtained results show that the Seybouse middle sub-basin needs twice as much water by 2030 for the different uses. This requires a better management of water resources for a sustainable development in this specific region of Eastern Algeria.

This paper is devoted to the management of water resources in a specific region (the middle Seybouse basin) which constitutes an interesting example of considerations to be taken for a sustainable development.