4.7 Article

Pyrolysis of waste tires: A modeling and parameter estimation study using Aspen Plus

期刊

WASTE MANAGEMENT
卷 60, 期 -, 页码 482-493

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.wasman.2016.10.024

关键词

Waste tire; Pyrolysis; Kinetics; Flowsheet model; Aspen Plus; Net energy

资金

  1. American University of Beirut through its University Research Board (URB)

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This paper presents a simulation flowsheet model of a waste tire pyrolysis process with feed capacity of 150 kg/h. A kinetic rate-based reaction model is formulated in a form implementable in the simulation package Aspen Plus, giving the flowsheet model the capability to predict more than 110 tire pyrolysis products as reported in experiments by Laresgoiti et al. (2004) and Williams (2013) for the oil and gas products respectively. The simulation model is successfully validated in two stages: firstly against experimental data from Olazar et al. (2008) by comparing the mass fractions for the oil products (gas, liquids (non-aromatics), aromatics, and tar) at temperatures of 425, 500, 550 and 610 degrees C, and secondly against experimental results of main hydrocarbon products (C-7 to C-15) obtained by Laresgoiti et al. (2004) at temperatures of 400, 500, 600, and 700 degrees C. The model was then used to analyze the effect of pyrolysis process temperature and showed that increased temperatures led to chain fractions from C-10 and higher to decrease while smaller chains increased; this is attributed to the extensive cracking of the larger hydrocarbon chains at higher temperatures. The utility of the flowsheet model was highlighted through an energy analysis that targeted power efficiency of the process determined through production profiles of gasoline and diesel at various temperatures. This shows, through the summation of the net power gain from the plant for gasoline plus diesel that the maximum net power lies at the lower temperatures corresponding to minimum production of gasoline and maximum production of diesel. This simulation model can thus serve as a robust tool to respond to market conditions that dictate fuel demand and prices while at the same time identifying optimum process conditions (e.g. temperature) driven by process economics. (c) 2016 Elsevier Ltd. All rights reserved.

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