(440h) Plantwide Process Design with Automatic Column Optimization, Sequencing and Stacking Using a Rigorous Process Simulator

Column sequencing refers to the problem of determining the optimal column sequence of distillation columns for multicomponent separation. For example, for the separation of three components, ABC, there are two possible sequences: the direct split (A/BC followed by B/C) and the indirect split (AB/C followed by B/C). The number of possible sequences grows rapidly as the number of components to be separated grows. The column sequencing problem has been widely studied in the literature [1–11], although most researchers use shortcut equations (e.g. the Fenske-Underwood-Gilliland column design method) for the column design in order to reduce the computational load.

Column stacking refers to the process of adjusting the operating pressure of one or more columns so that the reboiler of one or more columns can be powered by condenser of another column. Depending on the temperature difference between the top and bottom of the columns and the temperatures of the utilities, three or more columns can be stacked on top of each other. This type of heat integration can significantly reduce energy consumption. Column stacking has also been widely studied in the literature [12–16], although again mostly using shortcut models.

Finally, it is well known that for many problems of practical interest, there is a tradeoff between the design of the reactor or reactor network and the cost of separation [17]. Specifically, for many problems of practical interest, selectivity decreases as per-pass conversion increases, so there is a tradeoff between selectivity losses at high conversion and separation cost (for recycle of unconsumed reactant) at low per-pass conversion. Thus in order to determine the optimal plant-wide process design, it is necessary to consider the design of the reactor or reactor network and the separation system simultaneously. For greater accuracy, it would be desirable to use a rigorous process simulator rather than shortcut column design equations.

In the present contribution, these problems are approached using a rigorous process simulator (Aspen Plus) coupled with a high-level procedural programming language (Matlab). The two programs are connected using a client-server architecture, in which Matlab is the client and Aspen Plus is the server. A process flowsheet consisting of a single distillation column is constructed in Aspen Plus, and this flowsheet is used to model each column in every sequence by changing the feed conditions. The reflux rate and boilup rate are manipulated (varied) in order to achieve desired specifications on the top and bottom product distributions. The optimal number of trays and feed tray location are determined by optimization using simulated annealing programmed in Matlab.

The code determines the optimal design of each column in every sequence and adds up the cost to determine the optimal column sequence. Once the optimal sequence is identified, the code automatically implements column stacking by adjusting the operating pressures of the columns within the limits of feasibility (given temperatures for the utilities and a minimum temperature driving force). Finally, this entire process (automatic column design, sequencing and stacking) is coupled with a model of a chemical reactor (in a separate Aspen flowsheet) to determine the optimal plantwide process design by adjusting reactor network design parameters (temperature and residence time) to find the optimal tradeoff between selectivity losses and separation costs using the optimized separation system design in each case.

References

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