Modélisation de la dose de coagulant par les systèmes à base d’inférence floue (ANFIS) application à la station de traitement des eaux de Boudouaou (Algérie)

La coagulation est l’une des étapes les plus importantes dans le traitement des eaux. La difficulté principale est de déterminer la dose optimale de coagulant à injecter en fonction des caractéristiques de l’eau brute. Un mauvais contrôle de ce procédé peut entraîner une augmentation importante des coûts de fonctionnement et le non-respect des objectifs de qualité en sortie de la station de traitement. Le sulfate d’aluminium (Al2SO4.18H2O) est le réactif coagulant le plus généralement utilisé. La détermination de la dose de coagulant se fait au moyen de l’essai dit de « Jar Test » conduit en laboratoire. Ce type d’approche a le désavantage d’avoir un temps de retard relativement long et ne permet donc pas un contrôle automatique du procédé de coagulation.Le présent article décrit un modèle neuro flou de type Takagi Sugeno (TK), développé pour la prédiction de la dose de coagulant utilisée lors de la phase de clarification dans la station de traitement des eaux de Boudouaou qui alimente la ville d’Alger en eau potable. Le modèle ANFIS (système d’inférence flou à base de réseaux de neurones adaptatifs), qui combine les techniques floues et neuronales en formant un réseau à apprentissage supervisé, a été appliqué durant la phase de calage et testé en période de validation. Les résultats obtenus par le modèle ANFIS ont été comparés avec ceux obtenus avec un réseau de neurones de type perceptron multicouche (MLP) et un troisième modèle à base de regression linéaire multiple (MLR). Un coefficient de détermination (R2) de l’ordre de 0,92 en période de validation a été obtenu avec le modèle ANFIS, alors que pour le MLP, il est de l’ordre de 0,75, et que pour le modèle MLR, il ne dépasse pas 0,35. Les résultats obtenus sont d’une grande importance pour la gestion de l’installation.Coagulation is an important component of water treatment. Determining the optimal coagulant dosage is vital, as insufficient dosage will result in undesirable treated water quality. A number of chemicals have been used successfully in coagulation, particularly alum (Al2SO4•18H2O). Traditionally, jar tests are used to determine the optimum coagulant dose. However, this is expensive and time-consuming and does not enable responses to changes in raw water quality in real time. An optimal modeling approach can be used to overcome these limitations.The main purpose of this study was to investigate the applicability and capability of Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Neural Network (ANN) methods for modeling coagulant dose. To verify the application of this approach, Boudouaou surface water, located in the northern part of Algeria, was chosen as the case study area. The data used for the determination of the models included six (6) input variables describing the raw water characteristics (temperature, pH, turbidity, conductivity, dissolved oxygen and the ultraviolet absorption). The data set was divided into two (2) subgroups, calibration and validation periods. Coagulant models having various input structures were trained and tested to investigate the applicability of the used methods. To obtain a more accurate evaluation of the results for the ANFIS models, the best fit model structures were also tested by artificial neural network (ANN) and multiple linear regression (MLR) methods. The results of three methods were compared, and it was observed that the ANFIS is preferable and can be applied successfully because it provides high accuracy and reliability for coagulant dosage modelling, according to the following performance evaluation criteria: determination coefficient (R2), Root Mean Square Error (RMSE) and bias (B). The results are of practical importance: the coagulant dose changes according to the six variables representing the raw water characteristics, as there is no one dominant variable

Erratum to: Comparative assessment between GR model and tank model for rainfall-runoff analysis using Kalman filterapplication to Algerian basins-.

Modeling the rainfall-runoff relationship with conceptual models has always been a fascinating subject for hydrologists in view of its practical importance and complexity. This study presents a comparative assessment of the performance of two well established rainfall-runoff conceptual models. A first model called: Model ‘Genie Rural’ (i.e. Agricultural Engineering) and abbreviated GR, developed by Cemagref has been extensively tested in the Mediterranean watersheds and some basins in African countries. When applied to Algerian basins, the different version of the GR model gave satisfactory results, particularly for long time steps (monthly and annual data). In this work, the tank Model by Sugawara using Kalman filter for adaptive calibration is developed and tested for the first time to assess rainfall-runoff in Algerian basins. The results appear to be very prominent and far better than those given by the GR models including daily time steps. Indeed, a comparison between the two models established for daily and monthly data was performed on the three (03) Algerian Basins (i.e. Isser, Zardezas basin and Cheffia). Calibration of the Tank model parameters was performed by Kalman filter. Furthermore, the structure of tank model (i.e: number of tanks, number of outlets in each tank, and their location) was determinated for the studied basins

Erratum to: Comparative assessment between GR model and tank model for rainfall-runoff analysis using Kalman filterapplication to Algerian basins-.

Modeling the rainfall-runoff relationship with conceptual models has always been a fascinating subject for hydrologists in view of its practical importance and complexity. This study presents a comparative assessment of the performance of two well established rainfall-runoff conceptual models. A first model called: Model ‘Genie Rural’ (i.e. Agricultural Engineering) and abbreviated GR, developed by Cemagref has been extensively tested in the Mediterranean watersheds and some basins in African countries. When applied to Algerian basins, the different version of the GR model gave satisfactory results, particularly for long time steps (monthly and annual data). In this work, the tank Model by Sugawara using Kalman filter for adaptive calibration is developed and tested for the first time to assess rainfall-runoff in Algerian basins. The results appear to be very prominent and far better than those given by the GR models including daily time steps. Indeed, a comparison between the two models established for daily and monthly data was performed on the three (03) Algerian Basins (i.e. Isser, Zardezas basin and Cheffia). Calibration of the Tank model parameters was performed by Kalman filter. Furthermore, the structure of tank model (i.e: number of tanks, number of outlets in each tank, and their location) was determinated for the studied basins