استفاده از مدل زمان سرویس بستر عمق و مدل شبکه عصبی مصنوعی برای پیش بینی بهره وری جذب رنگ از رنگ اکالیپتوس بارکس کمالدونیس در سیستم بستر ثابت
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|4222||2011||8 صفحه PDF||سفارش دهید||3984 کلمه|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 38, Issue 1, January 2011, Pages 949–956
In this study, the Bohart and Adams’ model taking into account bed depth, and influent dye concentration was studied to exhibit adsorption process of textile dyes (Basic Blue 41 – BB41 and Reactive Black 5 – RB5) in glass columns using tree barks (Eucalyptus camaldulensis). Adsorption capacity coefficient values are determined using the Bohart and Adams’ bed depth service model. The model indicated that adsorption properties of E. camaldulensis barks conform for tertiary treatment for textile BB41 and RB5 containing wastewaters. An artificial neural network (ANN) based model for determining dye adsorption capability of bed system is also developed. The breakthrough curves of adsorption are also exhibited by this model. Results showed that ANN model could describe present system. Results showed that with the increases of bed height, and the decreases of influent dye concentrations, the breakthrough time was delayed.
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.
نتیجه گیری انگلیسی
•The present study has shown that the textile dyes BB41 and RB5 can be removed by adsorption onto the E. camaldulensis barks. The BDST model was successfully used to analyze column performance and evaluate the model parameters. The BDST model was used in an attempt to obtain a mathematical description of the adsorption of BB41 and RB5 onto E. camaldulensis barks. •Characteristic parameters for each dye and related correlation coefficients were determined from the linear plots of Bohart–Adams’ equations. The correlation coefficients for the plots were in the range ca. 0.9760–0.9980. •On the basis of the BDST model, maximum adsorption capacities (Q0.1) of 162.2 and 4.8 mg/g were obtained for BB41 and RB5, respectively onto E. camaldulensis barks. •In this study it was observed that the higher influent concentration and the lower bed depth could result in poor effluent quality. According to results E. camaldulensis barks can be used for tertiary treatment of the effluents of conventional treatment units, especially basic dye-containing effluents. • ANN predicted results are very close to the experimental values. Mean square errors of BB41 and RB5 test data are 0.00620594 and 0.00119229, respectively which are within ±1% error range. The present work suggests that neural network can be used as an effective technique in modeling, estimation and prediction of adsorption process. Also, neural network can be considered as an effective supplement for the conventional and complicated mathematical models in the prediction of bioprocess parameters.