鼓泡流化床反应器中NH3–NO–SCR反应的人工智能模拟

article info
Article history: Received 6 August 2010 Received in revised form 16 September 2011 Accepted 19 September 2011 Available online 1 October 2011
Fuel 93 (2012) 245–251
Contents lists available at SciVerse Sage: /locate/fuel
Modeling of NH3–NO–SCR reaction over CuO/c-Al2O3 catalyst in a bubbling
fluidized bed reactor using artificial intelligence techniques
Muhammad Faisal Irfan a,⇑, Farouq S. Mjalli b, Sang Done Kim c
a Department of Chemical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia b Petroleum and Chemical Engineering Department, Sultan Qaboos University, 123 Muscat, Oman c Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, 335 Gwahangno, Yuseong-gu, 305-701 Daejeon, Republic of Korea
Ó 2011 Elsevier Ltd. All rights reserved.
1. Introduction
Selective catalytic reduction process is a well known process for the NO reduction using nitrogen containing compounds such as ammonia or urea. Different catalysts have been studied and used for this process such as Cu-zeolite and oxide based catalysts [1–7], vanadium based catalysts [8,9], cobalt based catalysts [10]. The vanadium based catalysts have been used for more than 20 years for the SCR process [11]. However, porous anatase required was found difficult to prepare and it has also high cost [12]. On the contrary, CuO is cheaper in cost, highly active and can simultaneously remove SO2 and NOx and thus provide a wider roam for further study [13,14]. Otto et al. [15] reported that the CuO based catalysts containing Cu-zeolite have a high activity for the reaction of NO with NH3. Chmielarz et al. [2,3] studied the NO reduction with NH3 process over Al2O3 and TiO2 pillared montmorillonites modified with Cu or Co active metals. They claimed that Ti-modified clays were found to be significantly more active in DeNOx process than Al-sample. Moreover, introduction of Cu into Ti and Al samples activates them much more effectively in the low temperature region (25–450 °C) than Co based samples which are much better at high temperature region (T > 400 °C).
Keywords: SCR NO removal ANN Mechanistic model
abstract
Comparative study of the artificial neural network and mechanistic model was carried out for NO removal in a bubbling fluidized bed reactor. The effects of temperature, superficial gas velocity and ammonia/nitric oxide ratio on the NO removal efficiency were determined and their optimum conditions were estimated by the experimental study, the artificial neural network and mechanistic models as well. The optimum values of ammonia/nitric oxide ratio, temperature and superficial gas velocity for the maximum NO removal efficiency were found to be 1.5, 300 °C and 0.098 m/s, respectively. A mechanistic model was implemented in our previous study [Muhammad F. Irfan, Sang Done Kim and Muhammad R. Usman, 2009] and it was found that this model fitted well only at specific condition i.e. maximum conversion temperature (300 °C). However, it failed to perfectly match with rest of the experimental data points at other temperatures and parametric conditions as well. To improve this, an artificial neural network modeling strategy was applied and its predictions were evaluated which were favorably matched with the experimental data rather than the mechanistic model.