Artificial neural networks (ANNs) have become widely used in various research areas where the available information is experimental. ANNs introduce an easy mathematical function approximation for any linear and nonlinear systems. Topology of the neural networks consists of input layer, hidden layers and output layer. The neural network training method develops the input–output relation for the modeled system utilizing data sets (Sato, Sha, & Palosaari, 1999). Various researchers used the ANN for exhibit the performance of adsorption systems successfully (Brasquet and Le Cloirec, 1999, Du et al., 2007, Kumar and Porkodi, 2009, Robinson et al., 2002 and Yetilmezsoy and Demirel, 2008).
On the other hand, dye contamination in aqueous wastewater from industries is a serious problem because dyes are not biodegradable and tend to suppress photosynthetic activity in aquatic habitats by preventing the sunlight penetration. Dyes have also toxicological characteristics which are the main issues for environmentalists and have been the subject of growing attention for some years. Removal of textile dyes from wastewaters is one of the major problems in wastewater treatment technology. Traditional treatment methods such as ion exchange, chemical precipitation, and membrane separation are often ineffective and very expensive when they are used for the removal of dyes.
Currently, the most widely used and effective physical method for the treatment of colored wastewater is adsorption. The most convenient method for designing adsorption systems is the use of adsorption isotherms. The theoretical adsorption capacity of the adsorbent for a particular contaminant can be determined by calculating its adsorption isotherm (Tchobanoglous, 2003). The performance of a given adsorption system can be demonstrated through the use of adsorption isotherms. The degree to which adsorption will occur and the resulting equilibrium relationships are correlated according to the empirical relationship of Freundlich and the theoretically derived Langmuir relationship (Eckenfelder, 1989). In most wastewater flowing systems, since the contact time is not sufficiently long for the attainment of equilibrium, the data obtained under batch conditions are generally not adequate. Hence, it is required to perform equilibrium studies by using columns (Zhou, Zhang, Zhou, & Guo, 2004).
Activated carbon is the most popular and widely used adsorbent. In most industries, activated carbon columns are employed for the treatment of toxic, non-biodegradable wastewaters and as a tertiary treatment following biological oxidation (Eckenfelder, 1989). However, it is expensive because of the chemicals required for its regeneration after pollutant removal; the higher the quality, the greater the cost. Some natural materials not only have excellent adsorbability of dyes, but also have biocompatibility, biodegradability, and nontoxicity. To replace activated carbon with cheaper alternatives such as natural materials mentioned above, and to utilize various waste products, many novel materials have been tested such as micro-organisms (Aksu, 2001, Aksu and Tezer, 2000, Basibuyuk and Forster, 2003, Hu, 1992 and Mohan et al., 2002), tree fern (Ho, Chiang, & Hsueh, 2005), banana pith (Namasivayam and Kanchana, 1992 and Namasivayam et al., 1998), neem sawdust (Khattri & Singh, 2000), peat (Poots, McKay, & Healy, 1978), agricultural waste residues (Robinson et al., 2002), recycled alum sludge (Chu, 2001), Fuller’s Earth (Atun, Hisarlı, Sheldrick, & Muhler, 2003), lignite (Allen & Brown, 1995), perlite (Dogan, Mahir, & Onganer, 2000), apple pomace and wheat straw (Robinson et al., 2002), bottom ash and de-oiled soy (Gupta et al., 2006 and Low et al., 1995), carbon slurry waste (Jain, Gupta, & Suhas, 2003), bamboo dust, coconut shell, groundnut shell and rice husk (Kanan & Sundaram, 2001), coir pith (Namasivayam et al., 2001), orange peel (Namasivayam, Muniasamy, Gayathri, Rani, & Ranganathan, 1996), Indian rosewood sawdust (Garg, Amita, Kumar, & Gupta, 2004), chitosan and chitin (Juang, Tseng, Wu, & Lin, 1996), biogas residual slurry (Namasivayam & Yamuna, 1992), activated carbon prepared from plum kernels (Wu, Tseng, & Juang, 1999), fly ash (Wang, Boyjoo, & Choueib, 2005), kaolinite (Ghosh & Bhattacharyya, 2002), calcinated alumite (Ozacar & Sengil, 2002), cement kiln dust (Nassar, Daifullah, Magdy, & Ebrahiem, 2002) and aquatic plants including Spirodela polyrrhiza (Waranusantigul, Pokethitiyook, Kruatrachue, & Upatham, 2003), Hydrilla verticillata (Low, Lee, & Heng, 1993), Eichornia crassipes (Low et al., 1995) and Ceratophyllum demersum and Myriophyllum spicatum (Keskinkan, 2006). Although the contribution of natural adsorbents to wastewater treatment is already known there is little literature information on the dye adsorption capacities of Eucalyptus tree barks (Mohan, Rao, Prasad, & Karthikeyan, 2002) in a batch and fixed-bed system. Moreover, there are no literatures concerning biosorption of BB41 and RB5 onto barks of Eucalyptus camaldulensis. The determination of the dye adsorption capability of E. camaldulensis barks may contribute to system design approaches to adsorption systems for dye-containing wastewaters. Data from any dye adsorption studies can also be incorporated into full-scale field applications through the determination of the adsorption characteristics of E. camaldulensis barks. In this study, important parameters to design a column packed with E. camaldulensis barks such as column bed height and initial concentration of dye solution have been investigated. The breakthrough curves for the adsorption of dyes were analyzed using BDST. Adsorption potential of dyes (BB41 and RB5) towards E. camaldulensis barks at different dye concentrations was also exhibited by using an artificial neural network.
