Nonlinear Dynamic Operability Analysis of Methyl Tertiary Butyl Ether Reactive Distillation Column

  • Mr Herry Santoso, School of Chemical Sciences and Engineering, The University of New South Wales, Australia
  • Mr William Alley, School of Chemical Sciences and Engineering, The University of New South Wales, Australia
  • Jie Bao, School of Chemical Sciences and Engineering, The University of New South Wales, Australia
  • Prof Peter Lee, Chancellery, Level 2, 160 Currie Street, The University of South Australia, Australia
  • 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 system may have a significant impact on the overall process operability. Ignoring the operability aspect while making decisions 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) process system is presented. Two different configurations of the MTBE system, i.e. the traditional reactor-separator system and the more complex reactive distillation system, are analyzed and rated according to their operability. Furthermore, for the MTBE reactive distillation system, two different design variables, i.e. the tray holdup and the reboiler duty, are also studied.

    Process operability in this study 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 for this analysis. Based on this approach, an optimal controller for this highly nonlinear process is determined by solving a linear matrix inequality (LMI) optimization problem.