Abstract
This note deals with aggregate models for complex distillation systems in large-scale flowsheets. Group methods were originally devised for simple absorber and stripper calculations with no major extensions for handling distillation. In this work, group methods are systematically analyzed and further improved by modifying some of the previously proposed approximations. As a result, the improved group method exhibits accurate predictions and this is demonstrated using simulation and optimization case studies for a variety of chemical systems and operating conditions. It is observed that the prediction of output variables is in close agreement with that of the rigorous equilibrium stage model. In case of optimization problems, the optimal number of trays and feed locations differ by only one or two trays. The aggregate model can be applied in a sequence of steps in order to improve the reliability and robustness of the solution procedure. A rounding heuristic is also proposed which can provide near-optimal solutions with a significant reduction in computational time.
Highlights
Countercurrent gas liquid operations are an important part of many chemical engineering applications
We investigate the potential of group methods for process simulation and optimization and propose further improvements for modeling distillation columns
This work has addressed the use of aggregate models for complex distillation columns with the aim of simplifying or reducing computational effort without significant loss of accuracy and reliability of the desired objective
Summary
Countercurrent gas liquid operations are an important part of many chemical engineering applications. Synthesis problems that determine the optimal number of trays and feed locations for a distillation column have been addressed using rigorous models (Viswanathan and Grossmann, 1990, 1993a,b; Yeomans and Grossmann, 2000; Lang and Biegler, 2002; Barttfeld et al, 2003). Some of them are based on shortcut/design (Kremser, 1930; Fenske, 1932; Underwood, 1948; Gilliland, 1940), heat/mass transfer (Bagajewicz and Manousiouthakis, 1992; Papalexandri and Pistikopoulos, 1996) and pinch/design (Caballero and Grossmann, 1999) Most of these models cannot be directly used in process simulation and optimization mostly because of two issues. A rounding heuristic is proposed which provides reasonably good solutions and significantly reduces the computational time for solving mixed integer nonlinear programming (MINLP) problems
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