Optimization distillation towers

Complete simulation models have been formulated for cascade and Stratco sulfuric acid alkylation units and studies have confirmed the accuracy of the models. Application studies include cases in which model usage Identified profitable unit modifications, determined optimal unit capacity and optimal distillation tower operation, and compared the performance of cascade and Stratco units. "Isostripper" deisobutanizer operation was determined to be relatively unprofitable for sulfuric acid alkylation units and acid consumption on a modified cascade unit was found to be 36% below that expected for a Stratco unit. The examples presented suggest the broad applicability of the simulation models for improving alkylation unit operation. Use of the models not only pinpoints areas where significant improvements are possible, but also quantifies incentives needed to get them implemented quickly. 

Analysts must recognize the above sensitivity when identifying which measurements are required. For example, atypical use of plant data is to estimate the tray efficiency or HTU of a distillation tower. Certain tray compositions are more important than others in providing an estimate of the efficiency. Unfortunately, sensor placement or sample port location are usually not optimal and, consequently, available measurements are, all too often, of less than optimal use. Uncertainty in the resultant model is not minimized. 

Similar kinds of constraints involve the reflux ratio in distillation, which must exceed the minimum value for the required separation. If the distillation tower pressure is adjusted, the minimum reflux ratio will change and the actual ratio must be maintained above the minimum value. Even when optimization is not performed, the decision variable values must be selected to avoid violating the inequality constraints. In some cases, the violations can be detected when examining the simulation results. In other cases, the imit subroutines are unable to solve the equations as, for example, when the reflux ratio is adjusted to a value below the minimum value for a specified split of the key components. 

Here, the equality constraints are augmented by the tear equations, h[x = 0, which must be satisfied as well at the minimum of/[i. For this and similar flowsheets, the decision variables include the residence times in the reactors, the reflux ratio of the distillation tower, and the purge/recycle ratio. In one-dimensional space (i.e., with one decision variable), as d varies, the objective function can be displayed as shown in Figure 18.10a. Clearly, the optimizer seeks to locate the minimum efficiently, a task that is complicated when multiple minima exist and it is desired to locate the global minimum. 

EXAMPLE JS.S Optimization of a Distillation Tower with Sidedraws... 

What criteria should be used for assessing a distillation tower How does one know if a tower operates under stable operation or near the best performance What turnkey options are available to operators to simultaneously optimize product separation and energy use How can the best performance be sustained These are the questions that engineers constantly ask and these are the focus of this chapter. 

Cold reheat gas (sulfur recovery), 116 Combination head, 63 Combination tower, 35, 38, 71,83-89 bottoms screen, 35, 38 overhead condenser, 82 delayed coking process, 83-89 explosion-proof trays, 84-85 energy savings, 85-86 coke drum cycles, 86-89 coke drum yields, 88-89 Combustion air supply (process heaters), 317—325 trimming burner operation, 318 excess air benefits, 318 optimizing heater draft, 318— 321 insufficient air, 321-322 optimizing excess air, 322-325 Combustion chamber, 315 Composition instability (distillation tower), 381-382 temperature controller, 381-382 condensing capacity, 382... 

Figure 3.13. Crude oil vacuum tower. Pumparound reflux is provided at three lower positions as well as at the top, with the object of optimizing the diameter of the tower. Cooling of the side streams is part of the heat recovery system of the entire crude oil distillation plant. The cooling water and the steam for stripping and to the vacuum ejector are on hand control.

Computer modeling of the crude process is worthwhile for many reasons including 1) initial design 2) economic optimization of operation and 3) control and adjustment of product compositions and operating costs. The first models were based on reducing the process to a combination of two-product distillation calculations because of the available computer power. More recently, the computer calculation is able to handle the crude tower as is however, the two-product combinations are useful to guide technical supervision of the operation (Figs. 4 and 5). 

For a simple tower that does not have any pump-arounds or side draws, there are two sections, namely, rectification section, which is above the feed tray, and stripping section below the feed tray. The rectification section has a vapor rate higher than the liquid rate, whereas it is reversed for the stripping section. The UV ratio is the indication of distillation that could take place. For most towers, the L/V ratio is 0.3-3.0. L/V ratios outside this range may give sloppy or too easy distillation. Determination of UV ratio is a major part of energy optimization, which will be discussed in detail. 

About 8,000 Ib/hr of steam was consumed in the atmospheric tower bottom s stripper. This rate had been optimized many years ago to minimize the production of vacuum distillate (see Fig. 1-6). 

For a typical flowsheet, such as the DME (dimethyl ether) PFD in Figure B.1.1 i Appendix B), there are many decision variables. The temperature and pressure of each unit can be varied. The size of each piece of equipment involves decision variables (usually several per unit). The reflux in tower T-201 and the purity of the distillate fromT-202 are decision variables. There are many more. Clearly, the simultaneous optimization of all of these decision variables is a difficult problem However, some subproblems are relatively easy. If Stream 4 (the exit from the methanol preheater) must be at 154°C, for example, the choice of which heat source to use (Ips, mps, or hps) is easy. There is only a sin e decision variable, there are only three discrete choices, and the choice has no direct impact on the rest of the process. The problem becomes more difficult if the temperature of Stream 4 is not constrained. 

In practice, the question of optimizing the relative distillate yield between the atmospheric and vacuum towers will be settled on an economic basis and must be resolved prior to commencing definitive design work. This type of analy will depend upon economic factors within the particular company and/or plant site under study and is outside the scope of this work. 

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