Piuleac, 2010, Environmental Modelling and Software1

Ten steps modeling of electrolysis processes by using neural networks C.G. Piuleac a , M.A. Rodrigo b, * , P. Can ˜ izares b , S. Curteanu a , C. Sa ´ ez b a Department of Chemical Engineering, Faculty of Chemical Engineering and Environmental Protection, ‘‘Gh. Asachi’’ Technical University Iasi, Bd. D. Mangeron, No. 71A, 700050, Iasi, Romania b Department of Chemical Engineering, Faculty of Chemistry, Universidad de Castilla La Mancha, Campus Universitario s/n., 13071 Ciudad Real, Spain a r t i c l e i n f o Article history: Received 24 October 2008 Received in revised form 14 July 2009 Accepted 18 July 2009 Available online 14 August 2009 Keywords: Ten step modeling Stacked neural networks Batch electrolysis process Wastewater a b s t r a c t Neural networ ks hav e been developed to model the elec tro lys is of was tes pol lut ed with phe noli c compounds, including phenol, 4-chlorophenol, 2,4-dichlorophenol, 2,4,6-trichlorophenol, 4-nitrophenol and 2,4-dinitrophenol. They enable the prediction the Chemical Oxygen Demand of a treated waste as a function of the initial characteristics (pollutant concentration, pH), operation conditions (temperature, curren t densit y) and curren t charge passe d. A consist ent set of exper imental data was obtained by electrochemical oxidation with conductive diamond electrodes, used to treat synthetic aqueous wastes. Several modeling strategies based on simple and stacked neural networks, with different transfer functions into the hidden and output layers, have been considered to obtain a good accuracy of the model. Global errors during the training stage were under 3% and those of the validation stage were under 4%, demonstrat ing that the neural network based techniqu e is appropriate for modelin g the system. The generalization capability of the neural networks was also tested in realistic conditions where Chemical Oxygen Demand was predicted with errors around 5%. Therefore, the developed neural models can be used in industry to determine the required treatment period, to obtain the discharge limits in batch electrolysis processes, and it is a first step in the development of process control strategies. The ten step methodology was applied to the neural network based process modeling. Ó 2009 Elsevier Ltd. All rights reserved. 1. Introduction Artificial neural networks are becoming a promisin g alternative tool for classical processes modeling techniques. A phenomeno- logical model based on a chemical process is difficult to obtain, especially when a limited knowledge about it is available. Neural networks could overcome the modeling difficulties, having a few adva ntag es: the poss ibil ity to appl y it on comp lex non-li near pro cesses, the ease in obtainin g and using neural models, the possibility to substitute experiments with predictions. Neural network needs good quality data (large amount of data whi ch cover the whole ran ge of the pr ocess var iab le) for its trai nin g, whic h is normally difficul t to obtai n in practic e. An y appl icati ons pro ve that if prop erl y trained and validated, the neura l network models can be used to accu rate ly pred ict the proc ess beha vior, hen ce, lead ing to improve pro cess opt imiz ation and control performance (Xiong and Zhang, 2005a). The use of neural networks has become increasingly recom- mended for applications where the mechanistic description of the inte rdep ende nce between vari ables is eith er unkn own or very complex. They are one of the most popular artificial tools with appl icati ons in areas such as patte rn reco gnit ion, clas sific atio n, process control, optimization ( Xiong and Zhang, 2005b; Ng and Hussain, 2004; Zhang, 2004). Recently, many works have been published concerning the use of electrolysis in the treatment of synthetic wastewaters: phenolic (Can ˜ izares et al., 2004a, 2005, 2007a), carboxylic acids ( Can ˜ izares et al ., 2008a ), heter ocy clic (Can ˜ iz ares et al., 2007b; Saez et al., 2007) and actua l wast ewaters (fine -che mica ls (Can ˜ izares et al., 2006), door-manufacturing processes ( Can ˜ izares et al., 2008b), olive-oil mills (Can ˜ izares et al., 2007c ). Within these tech nologies , the use of dia mon d ano des has giv en an imp or tan t adv antage due to the great re duc tio n of COD (Ch emical Oxy gen Demand ) (no ref ra ct ory comp oun ds are forme d) and to the ener gy efficienc ies of thes e processes. Consequently, many recent studies have focussed on the scaling up of these treatment processes, in order to evaluate its possible use in industrial scale. The main goal of the present paper is to evaluate individual neural networks or stacked neural networks with different archi- tect ures as mode l of the elec troc hemi cal was tewater trea tmen t process. This modeling methodology is presented as a promising * Corresponding author. Tel.: þ34 902 204 100; fax: þ34 926 295 256. E-mail address: [email protected] (M.A. Rodrigo). Contents lists available at ScienceDirect Environmental Modelling & Software journal homepage: www.elsevier.com/locate/envsoft 1364-8152/$ – see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.envsoft.2009.07.012 Environmental Modelling & Software 25 (2010) 74–81

Piuleac, 2010, Environmental Modelling and Software1

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