Impact of mathematical model selection on prediction of steady state and dynamic behaviour of a reactive distillation column

2009 ◽  
Vol 33 (3) ◽  
pp. 788-793 ◽  
Author(s):  
Zuzana Švandová ◽  
Juraj Labovský ◽  
Jozef Markoš ◽  
Ľudovít Jelemenský
2011 ◽  
Vol 65 (2) ◽  
Author(s):  
Zuzana Švandová ◽  
Jozef Markoš

AbstractSteady state analysis of a combined hybrid process consisting of a reactive distillation column, pervaporation unit, and a distillation column is presented. This process configuration was first presented by Steinigeweg and Gmehling (2004) for the transesterification of methyl acetate and butanol to butyl acetate and methanol. This system is characteristic for its low reaction rate and complex phase equilibrium. Steinigeweg and Gmehling (2004) have shown that the combination of reactive distillation and pervaporation is favourable since conversions close to 100 % can be reached with a reasonable size of the reactive section in the reactive distillation column. The aim of this paper is to show that although high conversion can be achieved, very complicated steady state behaviour must be expected. The presented analysis is based on mathematical modelling of a process unit, where the steady-state analysis, including continuation and bifurcation analyses, was used. Multiple steady states were predicted for the studied system; three steady states with conversions higher than 98 %. However, not all predicted steady states met the maximal allowed temperature condition in the reactive section (catalyst maximal operation temperature of 393 K). The presence of multiple steady states reduces the operability and controllability of the reactive distillation column during its start-up and during the occurrence of any variation of operating parameters because the system can be shifted from one steady state to another one (concurrent exceeding the maximal allowed temperature) with unwanted consequences, e.g. production loss. Therefore, design and subsequent operation of such a complicated system is an ambitious task requiring knowledge of any possible system behaviour.


2006 ◽  
Vol 60 (6) ◽  
Author(s):  
Z. Švandová ◽  
J. Markoš ◽  
L’. Jelemenský

AbstractComparison of equilibrium and nonequilibrium models of a CSTR with total condenser focused on the multiple steady states and dynamic behaviour was carried out. The steady-state behaviour of the model system, MTBE synthesis from methanol and isobutene in a reactive distillation column, was studied in terms of the input parameters, i. e. feed flow rate of methanol or butenes, reflux ratio, and mass of catalyst. The dynamic behaviour of the system during the start-up was investigated and perturbations of manipulated variables were found to cause transitions between the parallel steady states.


1999 ◽  
Vol 16 (3) ◽  
pp. 251-260 ◽  
Author(s):  
Fabrizio Bezzo ◽  
Alberto Bertucco ◽  
Anna Forlin ◽  
Massimiliano Barolo

Author(s):  
Herry Santoso ◽  
Jie Bao ◽  
Peter L Lee

It has been understood for decades that process operability does not depend entirely upon the control system but also on the inherent properties of the process itself. For example, the decision on the size of equipment or the use of a highly integrated process may have a significant impact on the overall operability. Ignoring operability during process design may lead to a very difficult to control process. In this paper, a dynamic operability analysis of a Methyl Tertiary Butyl Ether (MTBE) reactive distillation column is presented. The effects of two design parameters, i.e. the reboiler duty and the reflux ratio, on the operability of the reactive distillation system are studied. Process operability is defined as the ability of the process to return to the steady-state in spite of unknown but bounded disturbances. The nonlinearity of the process is represented using a Hammerstein model, which can be easily obtained during process design from the steady-state model combined with some limited information on the process dynamics. The recent operability analysis method proposed by Rojas et al. (2007) is extended such that it can be implemented conveniently as one extra step after the flowsheet simulation using a process simulator. Based on this approach, an optimal controller for this highly nonlinear process is determined by solving a linear matrix inequality (LMI) optimization problem.


Sign in / Sign up

Export Citation Format

Share Document