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Chemical Product and Process Modeling

Ed. by Sotudeh-Gharebagh, Rhamat / Mostoufi, Navid / Chaouki, Jamal

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CiteScore 2016: 0.94

SCImago Journal Rank (SJR) 2016: 0.270
Source Normalized Impact per Paper (SNIP) 2016: 0.928

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1934-2659
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Operability Analysis of MTBE Reactive Distillation Column using a Process Simulator

Herry Santoso / Jie Bao / Peter L Lee
Published Online: 2009-08-30 | DOI: https://doi.org/10.2202/1934-2659.1376

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.

Keywords: nonlinear dynamic operability analysis; Hammerstein model; reactive distillation column; process simulator software

About the article

Published Online: 2009-08-30


Citation Information: Chemical Product and Process Modeling, ISSN (Online) 1934-2659, DOI: https://doi.org/10.2202/1934-2659.1376.

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