Prediction of Electrocoagulation Removal of Trivalent Chromium Using Neural Networks with a Bayesian Regularization Technique Mohamed NOHAIRa, Hassan CHAAIRb, Khalid DiGUAb, Mohssine Elmorrakchia , M. AZZIc Laboratoire Catalyse, Chimiométrie & Environement, Faculté des Sciences et Techniques de Mohammedia, Morocco a
la boratoire de génie des procédés, Faculté des Sciences et Techniques de Mohammedia, Morocco
b c
laboratoire d’interface, matériaux et environnement, Faculté des Sciences Ain Chok, Casablanca
nohairmohamed@yahoo.fr Abstract Physical processes influencing the ability of the electrocoagulation process to remove Cr(III) from aqueous solutions are highly complex and uncertain, and it is difficult to elaborate a deterministic model because many factors influence the process such as the pH, potential, time and temperature. Accurate model of chromium removal form water is important, as it has implications on the quality of water and the lives depend on it. Here a model based on the neural network modelling is formulated to develop a quantitative relationship model between the elctrocoagulation technique to remove Cr(III) and influencing variables. The results show that the elaborated model is robust and reliable and gives satisfied results. It allows us to predict the elctrocoagulation removal Cr(III) with high success. The statistical method used for deriving the model was a classical threelayer feedforward neural network trained by the back-propagation method and the Levenberg-Marquardt’s algorithm implemented in the neural network MATLAB’s toolbox. The predictive ability of the ANN model was tested by -20%-out (L20%O) cross-validation method, demonstrating the superior quality of the neural model. The established model allows us the prediction of the removal Cr (III) with success. The neural network possessed a 4:4:1 architecture with a sigmoïd shape as a activation function. The model produced a cross-validation standard coefficient r between calculated and observed values about 0.99, while the cross-validation standard deviation s is equal to 1.7. Keywords Electrocoagulatio; Cr(III) Removal; Neural Network Modelling; The Levenberg-Marquardt’s Algorithm; Cross Validation Method
Introduction The relationships between variables in chemistry are almost always very complicated and highly non linear. One of the most appropriate methods to illustrate this seems to be Artificial Neural Networks (ANNs) [1, 2]. In fact, this method is very powerful in dealing with non linear relationships. A large number of publications have underlined the interest of using the ANNs instead of linear statistical models. In fact, starting from input variables, ANNs have the capacity to predict the output variable but the mechanisms that occur within the network are often ignored, it for what ANNs are considered as black boxes. The principles of this approach are widely explained through a case study dealing with the design of a robust model allowing the simulation of the removal of Cr(III) by using electrocoagulation method based on aluminium anode as electrode material. The influencing factors considered in this study are the same studied in a previous work published here [3], in which the pH, electrolysis potential, electrolysis time and temperature are related to the chromium (CrIII) removal by means of a fractional central composite design. The Energy consumption and aluminium remaining in solution are also considered in the study. A large of work has underlined the interest of using of electrocoagulation to remove the CrIII from aqueous solutions [4-7]. It has been successfully used to treat a variety of industrial wastewater. The goal of this method is to form flocs of metal hydroxides within the effluent to be cleaned by elctrodissollution of soluble anodes. Three main processes occur during electrodissolution: electrolytic reactions at surface of electrodes, formation of coagulations in aqueous phase, adsorption of soluble or colloidal pollutants on coagulants, and removal by sedimentation of flotation.
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