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: 1.11). 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.

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: One of the most important processes in oil refineries is catalytic reforming unit in which high octane gasoline is produced. The catalytic reforming unit by using Hysys-refinery software was simulated. The results are validated by operating data, which is taken from the Esfahan oil refinery catalytic reforming unit. Usually, in oil refineries, flow instability in composition of feedstock can affect the product quality. The attention of this paper was focused on changes of the final product flow rate and product's octane number with respect to the changes in the feedstock composition. Also, the effects of temperature and pressure on the mentioned parameters was evaluated. Furthermore, in this study, Smith kinetic model was evaluated. The accuracy of this model was compared with the actual data and Hysys-refinery's results. The results showed that if the feed stream of catalytic reforming unit supplied with the Heavy Isomax Naphtha can be increased, more than 20% of the current value, the flow rate and octane number of the final product will be increased. Also, we found that the variations of temperature and pressure, under operating condition of the reactors of this unit, has no effect on octane number and final product flow rate.
    Petroleum & Coal. 01/2012; 54:76-84.
  • Source
    [Show abstract] [Hide abstract]
    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).
  • [Show abstract] [Hide abstract]
    ABSTRACT: The old oil refineries are largest chemical industries that are responsible for emission of several pollutants and GHGs. It is possible to minimize energy usage as well as air pollution by some process modification. The main objectives of this investigation were the minimization of air pollution and CO2 emissions in catalytic reforming unit in an oldest and largest refinery in Iran. To assess the air quality, ten sampling stations were selected for measurement of CO, H2S, SO2, and PM10 in ambient air. Also concentrations of C1–C5, H2S, and CO2 were measured in selected unit. In final, structural and process flaws were identified by analyzing real functional circumstances and they were modified. Results show that SO2, H2S, and PM10 concentrations are higher than ambient air standard levels in all seasons. Also, according to achieved results, the cold separator gas flow rate is reduced from 38,936kg/day (once-through gas process) to 9,649kg/day (recycle gas process). Beside CO2 and SO2 emission rates will be reduced 1803 and 136.5kg/day in this unit, respectively. Furthermore, the modification of this process causes prevention of 1654kg CO2 emission into the atmosphere, during each coke burning and catalytic regeneration. KeywordsNaphtha hydrotreater–Process modification–GHGs and air pollutants–Cleaner technology–Oil refinery
    Clean Technologies and Environmental Policy 01/2011; 13(5):743-749. · 1.83 Impact Factor


Available from