A study on naphtha catalytic reforming reactor simulation and analysis.

School of Petrochemical Engineering, Shenyang University of Technology, Liaoyang 111003, China.
Journal of Zhejiang University SCIENCE B (Impact Factor: 1.29). 07/2005; 6(6):590-6. DOI: 10.1631/jzus.2005.B0590
Source: PubMed

ABSTRACT A naphtha catalytic reforming unit with four reactors in series is analyzed. A physical model is proposed to describe the catalytic reforming radial flow reactor. Kinetics and thermodynamics equations are selected to describe the naphtha catalytic reforming reactions characteristics based on idealizing the complex naphtha mixture by representing the paraffin, naphthene, and aromatic groups by single compounds. The simulation results based above models agree very well with actual operation unit data.

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    ABSTRACT: In this research, a layered-recurrent artificial neural network (ANN) using the back-propagation method was developed for simulation of a fixed-bed industrial catalytic reforming unit called Platformer. Ninety-seven data points were gathered from the industrial catalytic naphtha reforming plant during the complete life cycle of the catalytic bed (about 919 days). Ultimately, 80% of them were selected as past horizontal data sets, and the others were selected as future horizontal ones. After training, testing, and validating the model with past horizontal data, the developed network was applied to predict the volume flow rate and research octane number (RON) of the future horizontal data versus days on stream. Results show that the developed ANN was capable of predicting the volume flow rate and RON of the gasoline for the future horizontal data sets with AAD% (average absolute deviation) of 0.238% and 0.813%, respectively. Moreover, the AAD% of the predicted octane barrel levels against the actual values was 1.447%, which shows the excellent capability of the model to simulate the behavior of the target catalytic reforming plant. 1. INTRODUCTION The need for transportation fuels, especially gasoline, steadily grows in the future, thus contributing to the demand for related petroleum processes. Catalytic naphtha reforming is an important process for producing high octane gasoline, aromatic feedstock, and hydrogen in the petroleum refining and petrochemical industries (Hu et al., 2002). The catalytic naphtha reforming unit uses naphtha as feedstock to produce a high octane value liquid with main by-products of hydrogen (H2) and liquefied petroleum gas (LPG) (Liang et al., 2005). To design new plants and to optimize existing ones, an appropriate mathematical model for simulating the industrial catalytic reforming process is needed (Weifeng et al., 2006). Besides kinetic-based models that are classified as deterministic or first principal models, the use of an artificial neural network (ANN)—a "black box" model—can be beneficial, especially when the former approach cannot describe a system appropriately. In particular, neural networks are nonlinear, and they learn (or train) by examples. The user of a neural network gathers representative data and subsequently invokes training algorithms to learn the structure of data (Chaturvedi, 2010). ANN has been applied previously for modeling of various refinery processes, such as hydrodesulfurization, hydrocracking, delayed coking, and thermal cracking of naphtha (Bellos et al., 2005; Arce-Medina & Paz-Paredes, 2009; Sadighi et al., 2010; Zahedi et al., 2009; Niaei et al., 2007).
    01/2013; 2:102-111. DOI:10.14716/ijtech.v4i2.106
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    ABSTRACT: Catalytic reforming of naphtha is one of the most important processes in refineries in which gasoline with highoctane number and aromatics such as benzene, toluene and xylene are produced. Simulation is normally used foroptimization and prediction of operating parameters such as octane number, LHSV, input temperature to reactorsand yield. In this paper, at first semi-regenerative catalytic reforming process of Tehran refinery was simulated byHysys-Refinery Simulator. After validating the simulation, on the basis of experiments prepared by Design expertsoftware, effect of catalyst distribution on the octane number of produced gasoline was studied whilst all otheroperating parameters were held constant. From the results, the best catalyst distribution in the reactors has beenobtained.The simulation result demonstrate that in the period of four months of plant data studied, the proposed distributioncan increase the octane number and octane barrel values to 0,8 and 0,2 percent respectively whilst the total massof the catalyst and operating conditions were keep constant.
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    ABSTRACT: One of the most important and critical processes in petroleum refineries is catalytic reforming in which highoctane gasoline and valuable aromatics such as Benzene, Toluene and Xylene (B.T.X.) are produced. In viewof the importance of this process for producing gasoline, simulation of catalytic reforming process andprediction of vital parameters such as octane number, Liquid Hour Space Velocity (LHSV), reactor inlettemperatures, yield and catalyst life aiming at process optimization is of prime importance. In this work, theoldest kinetic model mentioned for this unit is reconsidered. The accuracy of the model is compared with thecollected data from Tehran refinery and results of Petro-Sim simulator, one of the newest for simulation ofpetroleum refinery processes. The results show that this model has relatively acceptable ability to predictoctane number, outlet temperature of reactors and yield.


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