Crude distillation units

In light ends fractionation it is usually just as important to remove light material from the heavier cut as it is to keep heavy material out of the lighter cut sidestreams are seldom withdrawn. The desired purity (expressed as per cent of impurity) of the overhead and bottoms is determined by product specifications or by the requirements of subsequent processing units. To meet these purity requirements, higher reflux ratios and greater numbers of plates between cuts are required than in crude distillation units. 

Usually, product specifications for a crude distillation unit are expressed in terms of the products 15/5 or ASTM distillation curves. The prediction of a product 15/5 distillation is accomplished simply by blending the quantities of the pseudo components in the stream so as to form a true boiling point, 15/5 equivalent, distillation curve. This curve can then be converted to an ASTM type distillation using an empirical method. Figure 5 illustrates how a typical ASTM curve compares to the 15/5 curve for the same material. 

Fig. 9.4 Corrosion control in the overheads system of a crude distillation unit...

Figure 5.37 Crude distillation unit (CDU) application of PAT using ZrF4 and silica fiber optics.

PJ. Giammatteo and J.C. Edwards, On-line analysis of crnde feeds and distillation prodncts ntilization of on-line NMR on a refinery s crude distillation unit. Presented at Eastern Analytical Symposinm, Somerset, New Jersey, 2002. 

Complex process units such as catalytic crackers, hydrocrackers, ethylene units, hydrotreaters, or large crude distilling units typically containing high fire potential equipment. 

Constraint (3.1) below illustrates the refinery raw materials balance in which throughput to each refinery crude distillation unit p P at plant i I from each crude type cr C CR is equal to the available supply SRe(. 

The most advanced primary crude distillation units of this period, however, were constructed with continuous pipe stills for initial heating and partial vaporization, the effluent from the pipe still heater being discharged into a large evaporator, where the vapors were separated from the residual liquid. 

In the second step, the preprocessor builds and prints out tables showing the extracted crude assay data. The preprocessor also prints out in tabular form all information which the user has specified by card input for the following process units o Crude distillation unit o Propane deasphalter o FCC... 

These data are linearly extrapolated to the heavy naphtha cut specified for the crude distillation unit. Again, midpoint between initial and final boiling points is the independent variable. 

For the motor and BTX naphthas from the base crude mix, the preprocessor (1) determines the amount of each component present in the crude, (2) computes the fraction distilled overhead for each component at the naphtha cutpoints specified for the crude distillation unit, (3) constructs for each naphtha the composition of the material remaining between the initial and final boiling points, and (4) computes the blended compositions of the motor and BTX naphthas from the base crude mix. 

Activities in the crude distillation unit submatrix represent the base crude mix operation and up to five incremental crude operations. 

A typical flow diagram is shown in Fig. 18.13 The desalter operation is incorporated into the preheat train of the crude distillation unit to conserve energy. Depending on the characteristics of the hydrocarbon feedstock,... 

Reprinted from Correlation of Corrosion in a Crude Distillation Unit with Chemistry of the Crudes," Corrosion, June 1960.)... 

More accurate estimates by this method are obtained by subdividing the process plant into various process units, such as crude distillation units, reformers, alkylation units, etc., and applying the best available data from similar previously installed process units separately to each subdivision. Table 19 lists some typical process unit capacity-cost data and exponents useful for making this type of estimate. 

The core component of the oil supply chain is the refinery, where the received oil batches are managed to feed the crude distillation units in proportions that give origin to the desired cuts and products. However, the oil supply and the oil products distribution have to answer in agreement to their predicted demands. For this reason, fliere is the need to build decision support tools to manage inventory distribution. This work focuses on the development of a MILP model that describes the oil products distribution through a pipeline that connects one refinery to one tank farm. In order to supply die local market, the model represents the interaction between the pipeline schedule and the internal restrictions at the tank farm. Real world data from CLC (a Portuguese company) validate the model formulation. 

In this work, new developments were achieved through the use of new examples, one of which the optimisation of a real crude distillation unit involving 19 decision variables. The performance of the metamodel-based optimisation is compared with results obtained with the optimisation based on a first-principles model, embedded in a sequential-modular process simulator. It is shown that metamodel-based optimisation with adaptation of the metamodels during the optimisation procedure provides results with good accuracy and significant reduction of computational effort. The performance comparison between neural networks and kriging models for chemical processes is another contribution of this work. 

In this work, the highlights of the proposed methodology are presented, along with the results obtained with the optimisation of a real, industrial-scale crude distillation unit. 

In order to validate the proposed methodologies, a real industrial problem has been addressed the optimisation of the Crude Distillation Unit (CDU) and the Solvents Units of RECAP, a Brazilian refinery of PETROBRAS. 

The methodology was tested with an example involving the optimisation of a crude distillation unit, using the first-principles models of a sequential-modular process simulator. The solution of the corresponding optimisation problem with this rigorous model required considerable computational effort. 

In this contribution, first, a short review of the steady-state identification methods most used is presented. Then, a comparison of the behavior of these methods based on benchmark data is performed in order to develop and calibrate the methodologies that are further applied to a case study based on real data from a crude distillation unit. 

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