djeddou messaoud | University Of Oum El Bouaghi (original) (raw)

Papers by djeddou messaoud

This paper presents a determination of daily reliability level of activated sludge wastewater tre... more This paper presents a determination of daily reliability level of activated sludge wastewater treatment plant in Eastern of Algeria, using a method developed by Niku et al (1979) for determination of coefficient of reliability (COR), for effluent concentrations of BOD5, COD, and TSS obtained from four years data operation (2009-2012). We calculated COR, and using Algerian standards concentrations we have determined a daily reliability level for the considered parameters. The results showed that in global effluent BOD5 and COD performances are in terms of compliance with the Algeria standards, effluent TSS performances are not enough good due to the high variability of the quantity and quality of waste water, and problems in some components of the treatment process.

New Approaches in Engineering Research Vol. 10, 2021

This paper deals with a steady-state heat transfer analysis in a 4-pass fire-tube boiler. A compu... more This paper deals with a steady-state heat transfer analysis in a 4-pass fire-tube boiler. A computational program has been developed to study the heat transfer between the combustion gases and the boiler tube walls. On these surfaces, the energy balance was established, taking into account the heat transfer by convection and thermal radiation. The heat transfer characteristics, namely, the heat flux densities and the corresponding wall temperatures, are evaluated for different operating conditions. The modeling approach was validated by comparing the calculated outlet gas temperature against the experimental data, of the PFTA 500HP fire-tube boiler, for three types of fuels and various operating pressures. The comparison shows that the calculation results are in good agreement with the boiler's experimental data. A parametric analysis has also been conducted to investigate the working pressure effect on the boiler thermal behavior.

Volume 2: Nuclear Fuels, Research, and Fuel Cycle; Nuclear Codes and Standards; Thermal-Hydraulics, 2021

Three Hybrid artificial neural network (ANN) models namely radial basis function (RBF), generaliz... more Three Hybrid artificial neural network (ANN) models namely radial basis function (RBF), generalized regression neural networks (GRNN), and multi-layer perceptron (MLP) combined with empirical mode decomposition (EMD) are developed for CHF predictive modelling using CHF experimental databases. First, the original experimental inputs data series are decomposed into several intrinsic mode functions (IMFs) and one residual by EMD, whose components are divided into high, medium and low components. The performance parameters of the hybrid models indicates that the root mean square error (RMSE) are 0.8831, 0.6522, and 0.4149; the mean absolute error (MAE) are 0.6697, 0.4636, and 0.1935. The values of the R-square of the developed prediction approach utilizing EMD-RBF, EMD-GRNN, and EMD-MLP models are 0.8553, 0.9302, and 0.9818, and the index of agreement are 0.9464, 0.9700, and 0.9894., The value of the R-square and the index of agreement of the proposed models are much higher than those o...

Process Safety and Environmental Protection, 2020

Abstract This study proposed a novel practical framework to assess the reliability and resilience... more Abstract This study proposed a novel practical framework to assess the reliability and resilience of a wastewater treatment plant (WWTP). Such assessment is further extended to enhance the system performance with various scenarios based on fault tree analysis (FTA). Coefficient of reliability (COR) and performance curves of the resilience assessment are determined with respect to discharge standards. The effluent parameter violating the environmental standards is determined, and the causes are traced using an FTA. Monte Carlo simulations (MCs) are performed to validate the FTA results, and a sensitivity analysis is conducted to determine the dominant events that contribute to the failure of the top event. Finally, four operational scenarios are compared by taking into account the social, economic, and environmental costs. The results showed that total nitrogen (TN) concentration exceeded the threshold in the case study. Similar reliability values were obtained by the minimum cut set (21 %) and MCs (17 %) for the TN violations. Thus, four enhanced TN removal reliability scenarios were proposed considering significant impacts of dissolved oxygen, water quality, and hydraulic retention time on the TN removal. Employing supplementary chemicals integrated with scenario 3 was the optimal alternative to operate the WWTP with an econo-socio-environmental cost of 322,600 $/yr.

In this research, an ARFIMA model is proposed to forecast new COVID-19 cases in Algeria two weeks... more In this research, an ARFIMA model is proposed to forecast new COVID-19 cases in Algeria two weeks ahead. In the present study, public health database from Algeria health ministry has been used to build an ARFIMA model and used to forecast COVID-19 new cases in Algeria until May 11, 2020.BackgroundThe aim of this study is first to find the best prediction method among the two techniques used and type of memory, either short or long, of the model constructed for the daily confirmed cases in Algeria, then make forecasts of the confirmed cases in the fifteen next days.MethodsThis study was conducted based on daily new cases of COVID-19 that were collected from the official website of Algerian Ministry of Health from March 1, 2020 to April 26, 2020. Auto Regressive Integrated Moving Average (ARFIMA) model was used to predict the trend of confirmed cases. The evaluation of the fractional differentiation parameter (d) is carried out using OxMetrics 6 software.ResultsThe ARFIMA model (0, 0....

In this research, an extreme learning machine (ELM) is proposed to predict the new COVID-19 cases... more In this research, an extreme learning machine (ELM) is proposed to predict the new COVID-19 cases in Algeria. In the present study, public health database from Algeria health ministry has been used to train and test the ELM models.The input parameters for the predictive models include Cumulative Confirmed COVID-19 Cases (CCCC), Calculated COVID-19 New Cases (CCNC), and Index Day (ID).The predictive accuracy of the seven models has been assessed via several statistical parameters. The results showed that the proposed ELM model achieved an adequate level of prediction accuracy with smallest errors (MSE= 0.16, RMSE=0.4114, and MAE= 0.2912), and highest performance’s (NSE = 0.9999, IO = 0.9988, R2 = 0.9999). Hence, the ELM model could be utilized as a reliable and accurate modeling approach for predicting the new COVIS-19 cases in Algeria.The proposed ELM model, it can be used as a decision support tool to manage public health medical efforts and facilities against the COVID-19 pandemic...

Journal of Mechanical Engineering and Sciences

This study investigates the potential of a simple and Hybrid artificial neural network (ANN) to p... more This study investigates the potential of a simple and Hybrid artificial neural network (ANN) to predict dense alumina's critical thermal shock temperature (ΔTc). The predictive models have been constructed using two ANNS models (M1, M2). In the first model (M1), elaboration, physical and mechanical parameters have been exploited to build three ANNs, namely generalized linear regression (M1-GLRNN), extreme learning machine (M1-ELM), and radial basis function (M1-RBFNN). The second model (M2) has been built by the three models mentioned above incorporated by the Shannon Entropy (SE) method. To compare the performance of all the developed models, coefficient of correlation (R), root mean square error (RMSE), mean absolute percentage error (MAPE), and Nash-Sutcliffe efficiency coefficient (NSE) have been considered. It is found that M2-RBFNN model with (RMSE = 4.3526, MAPE= 0.3406, NSE = 0.9921, and R= 0.9960) had superiority to the M1-RBFNN model (RMSE = 4.7030, MAPE= 0.3003, NSE =...

This study investigates the potential of a simple artificial neural network for the prediction of... more This study investigates the potential of a simple artificial neural network for the prediction of COVID-19 New Confirmed Cases in Algeria (CNCC).Four different ANN models were built (GRNN, RBFNN, ELM, and MLP). The performance of the predictive models is evaluated based on four numerical parameters, namely root mean squared error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE), and Pearson correlation coefficient (R). Taylor diagram was also used to examine the similarities and differences between the observed and predicted values obtained from the proposed models.The results showed the potential of the multi-layer perceptron neural network (MLPNN) which exhibited a high level of accuracy in comparison to the other models.

In the stability studies of a homogeneous earth dam many critical Cases have been taken into cons... more In the stability studies of a homogeneous earth dam many critical Cases have been taken into consideration such as end of construction, steady seepage, reservoir drawdown and earthquake. Under the steady-state seepage condition, the application of a horizontal drain has been a common method to lower the free surface and dissipate the excessive pore water pressure, especially for a dam having impervious foundation.

Arabian Journal of Geosciences, 2019

The previous results of artificial neural network (ANN) prediction models in natural slope stabil... more The previous results of artificial neural network (ANN) prediction models in natural slope stability are encouraging, which gives logical hope for the practical application of these models for earth dams. In this context, this study presents an ANN model which allows the user to get four factors of safety (FS (i)) of small earth dams under long-term stability condition with static or earthquake loading immediately. Safety factors (FS (i)) for the model are calculated by carrying out two computations each for two cases, that is, earth dam subjected to full reservoir steady-state seepage condition with and without earthquake (FS (F + EQ) and FS (F)) and earth dam empty reservoir with and without earthquake (FS (Em + EQ) and FS (Em)). A database of 1372 different inputs and 4 outputs was built through strength reduction finite element method (SR-FEM). The used ANN is a feed-forward back-propagation neural network (FBNN) with three layers. The most appropriate FBNN architecture was found 11-21-4, as this gave the best FS (i) prediction with the lowest error. The relative importance of the inputs parameters is studied using both Garson's algorithm and connection weight approach. Moreover, for further verification, the developed model has been used for prediction FS (i) of new earth dam datasets. The predicted results have been compared with the obtained ones from different limit equilibrium (LE) slope stability computations. The comparison had confirmed a very satisfactory capability of the ANN model to predict the FS (i) .

Modeling Sludge volume index of activated sludge process in municipal WWTP is a difficult task to... more Modeling Sludge volume index of activated sludge process in municipal WWTP is a difficult task to accomplish due to the high nonlinearity of the plant and the non-uniformity and variability of influent quantity, quality parameters, and operation condition. ANNs were developed for the prediction of the Sludge Volume Index using influent quality parameters and operating parameters of Batna Wastewater Treatment Plant from 2011 to 2014. The best model given by the neural network for the SVI prediction composed of one input layer with fifteen input variables, one hidden layer with thirteen nodes and one output layer with one output variable with R = 0.8784 and RMSE = 0.443. The results demonstrate the ability of the appropriate Neural Network models for the prediction of SVI. This provides a very useful tool that can be used by WWTP operators in their daily management to increase treatment process performances and WWTP reliability. DJEDDOU M. & al. / Larhyss Journal, 24 (2015), 351-370

Cette etude presente une determination et l'analyse duniveau quotidien de fiabilite d'une... more Cette etude presente une determination et l'analyse duniveau quotidien de fiabilite d'une station de traitement des eaux usees residuaires utilisant un procede a boue activee dans l'Est de l'Algerie, on utilisant une methodologie developpee par Nikuet al. (1979) pour la determination du coefficient de fiabilite (CdF), pour les concentrations effluentes de la DBO 5, de la DCO, et des MES obtenus a partir des donnees de quatre ans d'operation (2009-2012). Nous avons calcule le Coefficient de fiabilite,et les normes algeriennes de rejet pour determiner le niveau quotidien de fiabilite pour les parametres consideres. Les resultats ont prouves en global un bon niveau de performances puisque les concentrations de la DBO5et la DCO des eaux traites presentent une conformite avec les normes de rejet en Algerie, par contre le niveau de performance des MES est tres mediocre dues aux problemes par l'arret du troisieme dessableur-deshuileur, et la grande variabilite des q...

In this research study, a combination of lower and upper bound finite element limit analysis (FEL... more In this research study, a combination of lower and upper bound finite element limit analysis (FELA) and artificial neural network (ANN) has been adopted in order to forecast critical seismic coefficients (kc) of homogeneous earth dams (HED) subjected to pseudo-static seismic loading. To achieve this, the results of kc obtained by OptumG2 software were used in the development of the ANN and MR models. The ANN models have shown higher prediction performance than the MR models based on the performance indices. The most appropriate architecture was found 8-14-1, as this gave the best kc predict with the minimum statistical measures of error and the high determination coefficient (> 99%). Consequently, the ANN model can be used to easily and accurately predict kc value of the HED as the best substitute for the conventional methods.

Cette étude présente une détermination du niveau quotidien de fiabilité d'une station de traiteme... more Cette étude présente une détermination du niveau quotidien de fiabilité d'une station de traitement des eaux usées résiduaires utilisant un procédé à boue activée dans l'Est de l'Algérie, on utilisant un modèle probabiliste développée par pour la détermination du coefficient de fiabilité (CdF), pour les concentrations des paramètres de qualités (DBO 5 , DCO, et MES) de l'effluent obtenus à partir des données de quatre ans d'exploitation (2009)(2010)(2011)(2012). Nous avons calculé le Coefficient de fiabilité, puis les niveaux quotidiens de fiabilité pour les paramètres considérés suivant les normes algériennes de rejet.

Cette étude présente une détermination et l'analyse du niveau quotidien de fiabilité d'une statio... more Cette étude présente une détermination et l'analyse du niveau quotidien de fiabilité d'une station de traitement des eaux usées résiduaires utilisant un procédé à boue activée dans l'Est de l'Algérie, on utilisant une méthodologie développée par pour la détermination du coefficient de fiabilité (CdF), pour les concentrations effluentes de la DBO 5 , de la DCO, et des MES obtenus à partir des données de quatre ans d'opération (2009)(2010)(2011)(2012). Nous avons calculé le Coefficient de fiabilité, et les normes algériennes de rejet pour déterminer le niveau quotidien de fiabilité pour les paramètres considérés.

Modeling Sludge volume index of activated sludge process in municipal WWTP is a difficult task to... more Modeling Sludge volume index of activated sludge process in municipal WWTP is a difficult task to accomplish due to the high nonlinearity of the plant and the non-uniformity and variability of influent quantity, quality parameters, and operation condition.
ANNs were developed for the prediction of the Sludge Volume Index using influent quality parameters and operating parameters of Batna Wastewater Treatment Plant from 2011 to 2014. The best model given by the neural network for the SVI prediction composed of one input layer with fifteen input variables, one hidden layer with thirteen nodes and one output layer with one output variable with R = 0.8784 and RMSE = 0.443. The results demonstrate the ability of the appropriate Neural Network models for the prediction of SVI.
This provides a very useful tool that can be used by WWTP operators in their daily management to increase treatment process performances and WWTP reliability.

This study presents daily performances evaluation of activated sludge wastewater treatment plant ... more This study presents daily performances evaluation of activated sludge wastewater treatment plant in Eastern of Algeria, using a probabilistic model, effluent concentrations of BOD 5 , COD, and TSS obtained from four years data operation (2009)(2010)(2011)(2012), and Algerian standards concentrations, we have evaluated a daily performances for the considered parameters.

This paper presents a determination of daily reliability level of activated sludge wastewater tre... more This paper presents a determination of daily reliability level of activated sludge wastewater treatment plant in Eastern of Algeria, using a method developed by Niku et al (1979) for determination of coefficient of reliability (COR), for effluent concentrations of BOD5, COD, and TSS obtained from four years data operation (2009-2012). We calculated COR, and using Algerian standards concentrations we have determined a daily reliability level for the considered parameters. The results showed that in global effluent BOD5 and COD performances are in terms of compliance with the Algeria standards, effluent TSS performances are not enough good due to the high variability of the quantity and quality of waste water, and problems in some components of the treatment process.

New Approaches in Engineering Research Vol. 10, 2021

This paper deals with a steady-state heat transfer analysis in a 4-pass fire-tube boiler. A compu... more This paper deals with a steady-state heat transfer analysis in a 4-pass fire-tube boiler. A computational program has been developed to study the heat transfer between the combustion gases and the boiler tube walls. On these surfaces, the energy balance was established, taking into account the heat transfer by convection and thermal radiation. The heat transfer characteristics, namely, the heat flux densities and the corresponding wall temperatures, are evaluated for different operating conditions. The modeling approach was validated by comparing the calculated outlet gas temperature against the experimental data, of the PFTA 500HP fire-tube boiler, for three types of fuels and various operating pressures. The comparison shows that the calculation results are in good agreement with the boiler's experimental data. A parametric analysis has also been conducted to investigate the working pressure effect on the boiler thermal behavior.

Volume 2: Nuclear Fuels, Research, and Fuel Cycle; Nuclear Codes and Standards; Thermal-Hydraulics, 2021

Three Hybrid artificial neural network (ANN) models namely radial basis function (RBF), generaliz... more Three Hybrid artificial neural network (ANN) models namely radial basis function (RBF), generalized regression neural networks (GRNN), and multi-layer perceptron (MLP) combined with empirical mode decomposition (EMD) are developed for CHF predictive modelling using CHF experimental databases. First, the original experimental inputs data series are decomposed into several intrinsic mode functions (IMFs) and one residual by EMD, whose components are divided into high, medium and low components. The performance parameters of the hybrid models indicates that the root mean square error (RMSE) are 0.8831, 0.6522, and 0.4149; the mean absolute error (MAE) are 0.6697, 0.4636, and 0.1935. The values of the R-square of the developed prediction approach utilizing EMD-RBF, EMD-GRNN, and EMD-MLP models are 0.8553, 0.9302, and 0.9818, and the index of agreement are 0.9464, 0.9700, and 0.9894., The value of the R-square and the index of agreement of the proposed models are much higher than those o...

Process Safety and Environmental Protection, 2020

Abstract This study proposed a novel practical framework to assess the reliability and resilience... more Abstract This study proposed a novel practical framework to assess the reliability and resilience of a wastewater treatment plant (WWTP). Such assessment is further extended to enhance the system performance with various scenarios based on fault tree analysis (FTA). Coefficient of reliability (COR) and performance curves of the resilience assessment are determined with respect to discharge standards. The effluent parameter violating the environmental standards is determined, and the causes are traced using an FTA. Monte Carlo simulations (MCs) are performed to validate the FTA results, and a sensitivity analysis is conducted to determine the dominant events that contribute to the failure of the top event. Finally, four operational scenarios are compared by taking into account the social, economic, and environmental costs. The results showed that total nitrogen (TN) concentration exceeded the threshold in the case study. Similar reliability values were obtained by the minimum cut set (21 %) and MCs (17 %) for the TN violations. Thus, four enhanced TN removal reliability scenarios were proposed considering significant impacts of dissolved oxygen, water quality, and hydraulic retention time on the TN removal. Employing supplementary chemicals integrated with scenario 3 was the optimal alternative to operate the WWTP with an econo-socio-environmental cost of 322,600 $/yr.

In this research, an ARFIMA model is proposed to forecast new COVID-19 cases in Algeria two weeks... more In this research, an ARFIMA model is proposed to forecast new COVID-19 cases in Algeria two weeks ahead. In the present study, public health database from Algeria health ministry has been used to build an ARFIMA model and used to forecast COVID-19 new cases in Algeria until May 11, 2020.BackgroundThe aim of this study is first to find the best prediction method among the two techniques used and type of memory, either short or long, of the model constructed for the daily confirmed cases in Algeria, then make forecasts of the confirmed cases in the fifteen next days.MethodsThis study was conducted based on daily new cases of COVID-19 that were collected from the official website of Algerian Ministry of Health from March 1, 2020 to April 26, 2020. Auto Regressive Integrated Moving Average (ARFIMA) model was used to predict the trend of confirmed cases. The evaluation of the fractional differentiation parameter (d) is carried out using OxMetrics 6 software.ResultsThe ARFIMA model (0, 0....

In this research, an extreme learning machine (ELM) is proposed to predict the new COVID-19 cases... more In this research, an extreme learning machine (ELM) is proposed to predict the new COVID-19 cases in Algeria. In the present study, public health database from Algeria health ministry has been used to train and test the ELM models.The input parameters for the predictive models include Cumulative Confirmed COVID-19 Cases (CCCC), Calculated COVID-19 New Cases (CCNC), and Index Day (ID).The predictive accuracy of the seven models has been assessed via several statistical parameters. The results showed that the proposed ELM model achieved an adequate level of prediction accuracy with smallest errors (MSE= 0.16, RMSE=0.4114, and MAE= 0.2912), and highest performance’s (NSE = 0.9999, IO = 0.9988, R2 = 0.9999). Hence, the ELM model could be utilized as a reliable and accurate modeling approach for predicting the new COVIS-19 cases in Algeria.The proposed ELM model, it can be used as a decision support tool to manage public health medical efforts and facilities against the COVID-19 pandemic...

Journal of Mechanical Engineering and Sciences

This study investigates the potential of a simple and Hybrid artificial neural network (ANN) to p... more This study investigates the potential of a simple and Hybrid artificial neural network (ANN) to predict dense alumina's critical thermal shock temperature (ΔTc). The predictive models have been constructed using two ANNS models (M1, M2). In the first model (M1), elaboration, physical and mechanical parameters have been exploited to build three ANNs, namely generalized linear regression (M1-GLRNN), extreme learning machine (M1-ELM), and radial basis function (M1-RBFNN). The second model (M2) has been built by the three models mentioned above incorporated by the Shannon Entropy (SE) method. To compare the performance of all the developed models, coefficient of correlation (R), root mean square error (RMSE), mean absolute percentage error (MAPE), and Nash-Sutcliffe efficiency coefficient (NSE) have been considered. It is found that M2-RBFNN model with (RMSE = 4.3526, MAPE= 0.3406, NSE = 0.9921, and R= 0.9960) had superiority to the M1-RBFNN model (RMSE = 4.7030, MAPE= 0.3003, NSE =...

This study investigates the potential of a simple artificial neural network for the prediction of... more This study investigates the potential of a simple artificial neural network for the prediction of COVID-19 New Confirmed Cases in Algeria (CNCC).Four different ANN models were built (GRNN, RBFNN, ELM, and MLP). The performance of the predictive models is evaluated based on four numerical parameters, namely root mean squared error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE), and Pearson correlation coefficient (R). Taylor diagram was also used to examine the similarities and differences between the observed and predicted values obtained from the proposed models.The results showed the potential of the multi-layer perceptron neural network (MLPNN) which exhibited a high level of accuracy in comparison to the other models.

In the stability studies of a homogeneous earth dam many critical Cases have been taken into cons... more In the stability studies of a homogeneous earth dam many critical Cases have been taken into consideration such as end of construction, steady seepage, reservoir drawdown and earthquake. Under the steady-state seepage condition, the application of a horizontal drain has been a common method to lower the free surface and dissipate the excessive pore water pressure, especially for a dam having impervious foundation.

Arabian Journal of Geosciences, 2019

The previous results of artificial neural network (ANN) prediction models in natural slope stabil... more The previous results of artificial neural network (ANN) prediction models in natural slope stability are encouraging, which gives logical hope for the practical application of these models for earth dams. In this context, this study presents an ANN model which allows the user to get four factors of safety (FS (i)) of small earth dams under long-term stability condition with static or earthquake loading immediately. Safety factors (FS (i)) for the model are calculated by carrying out two computations each for two cases, that is, earth dam subjected to full reservoir steady-state seepage condition with and without earthquake (FS (F + EQ) and FS (F)) and earth dam empty reservoir with and without earthquake (FS (Em + EQ) and FS (Em)). A database of 1372 different inputs and 4 outputs was built through strength reduction finite element method (SR-FEM). The used ANN is a feed-forward back-propagation neural network (FBNN) with three layers. The most appropriate FBNN architecture was found 11-21-4, as this gave the best FS (i) prediction with the lowest error. The relative importance of the inputs parameters is studied using both Garson's algorithm and connection weight approach. Moreover, for further verification, the developed model has been used for prediction FS (i) of new earth dam datasets. The predicted results have been compared with the obtained ones from different limit equilibrium (LE) slope stability computations. The comparison had confirmed a very satisfactory capability of the ANN model to predict the FS (i) .

Modeling Sludge volume index of activated sludge process in municipal WWTP is a difficult task to... more Modeling Sludge volume index of activated sludge process in municipal WWTP is a difficult task to accomplish due to the high nonlinearity of the plant and the non-uniformity and variability of influent quantity, quality parameters, and operation condition. ANNs were developed for the prediction of the Sludge Volume Index using influent quality parameters and operating parameters of Batna Wastewater Treatment Plant from 2011 to 2014. The best model given by the neural network for the SVI prediction composed of one input layer with fifteen input variables, one hidden layer with thirteen nodes and one output layer with one output variable with R = 0.8784 and RMSE = 0.443. The results demonstrate the ability of the appropriate Neural Network models for the prediction of SVI. This provides a very useful tool that can be used by WWTP operators in their daily management to increase treatment process performances and WWTP reliability. DJEDDOU M. & al. / Larhyss Journal, 24 (2015), 351-370

Cette etude presente une determination et l'analyse duniveau quotidien de fiabilite d'une... more Cette etude presente une determination et l'analyse duniveau quotidien de fiabilite d'une station de traitement des eaux usees residuaires utilisant un procede a boue activee dans l'Est de l'Algerie, on utilisant une methodologie developpee par Nikuet al. (1979) pour la determination du coefficient de fiabilite (CdF), pour les concentrations effluentes de la DBO 5, de la DCO, et des MES obtenus a partir des donnees de quatre ans d'operation (2009-2012). Nous avons calcule le Coefficient de fiabilite,et les normes algeriennes de rejet pour determiner le niveau quotidien de fiabilite pour les parametres consideres. Les resultats ont prouves en global un bon niveau de performances puisque les concentrations de la DBO5et la DCO des eaux traites presentent une conformite avec les normes de rejet en Algerie, par contre le niveau de performance des MES est tres mediocre dues aux problemes par l'arret du troisieme dessableur-deshuileur, et la grande variabilite des q...

In this research study, a combination of lower and upper bound finite element limit analysis (FEL... more In this research study, a combination of lower and upper bound finite element limit analysis (FELA) and artificial neural network (ANN) has been adopted in order to forecast critical seismic coefficients (kc) of homogeneous earth dams (HED) subjected to pseudo-static seismic loading. To achieve this, the results of kc obtained by OptumG2 software were used in the development of the ANN and MR models. The ANN models have shown higher prediction performance than the MR models based on the performance indices. The most appropriate architecture was found 8-14-1, as this gave the best kc predict with the minimum statistical measures of error and the high determination coefficient (> 99%). Consequently, the ANN model can be used to easily and accurately predict kc value of the HED as the best substitute for the conventional methods.

Cette étude présente une détermination du niveau quotidien de fiabilité d'une station de traiteme... more Cette étude présente une détermination du niveau quotidien de fiabilité d'une station de traitement des eaux usées résiduaires utilisant un procédé à boue activée dans l'Est de l'Algérie, on utilisant un modèle probabiliste développée par pour la détermination du coefficient de fiabilité (CdF), pour les concentrations des paramètres de qualités (DBO 5 , DCO, et MES) de l'effluent obtenus à partir des données de quatre ans d'exploitation (2009)(2010)(2011)(2012). Nous avons calculé le Coefficient de fiabilité, puis les niveaux quotidiens de fiabilité pour les paramètres considérés suivant les normes algériennes de rejet.

Cette étude présente une détermination et l'analyse du niveau quotidien de fiabilité d'une statio... more Cette étude présente une détermination et l'analyse du niveau quotidien de fiabilité d'une station de traitement des eaux usées résiduaires utilisant un procédé à boue activée dans l'Est de l'Algérie, on utilisant une méthodologie développée par pour la détermination du coefficient de fiabilité (CdF), pour les concentrations effluentes de la DBO 5 , de la DCO, et des MES obtenus à partir des données de quatre ans d'opération (2009)(2010)(2011)(2012). Nous avons calculé le Coefficient de fiabilité, et les normes algériennes de rejet pour déterminer le niveau quotidien de fiabilité pour les paramètres considérés.

Modeling Sludge volume index of activated sludge process in municipal WWTP is a difficult task to... more Modeling Sludge volume index of activated sludge process in municipal WWTP is a difficult task to accomplish due to the high nonlinearity of the plant and the non-uniformity and variability of influent quantity, quality parameters, and operation condition.
ANNs were developed for the prediction of the Sludge Volume Index using influent quality parameters and operating parameters of Batna Wastewater Treatment Plant from 2011 to 2014. The best model given by the neural network for the SVI prediction composed of one input layer with fifteen input variables, one hidden layer with thirteen nodes and one output layer with one output variable with R = 0.8784 and RMSE = 0.443. The results demonstrate the ability of the appropriate Neural Network models for the prediction of SVI.
This provides a very useful tool that can be used by WWTP operators in their daily management to increase treatment process performances and WWTP reliability.

This study presents daily performances evaluation of activated sludge wastewater treatment plant ... more This study presents daily performances evaluation of activated sludge wastewater treatment plant in Eastern of Algeria, using a probabilistic model, effluent concentrations of BOD 5 , COD, and TSS obtained from four years data operation (2009)(2010)(2011)(2012), and Algerian standards concentrations, we have evaluated a daily performances for the considered parameters.