Refinery operations, optimization with

Plant operations deal with the allocation of raw materials on a daily or weekly basis. One classical optimization problem, which is discussed later in this text, is the allocation of raw materials in a refinery. Typical day-to-day optimization in a plant minimizes steam consumption or cooling water consumption. 

In a petroleum refinery a large number of different products are produced, and the demand for some of these products is seasonal. For instance, there is not much need for residential fuel oil in the summer. The price of products also varies from day to day. To optimize the company s profit, it is therefore necessary periodically to vary the amount of each product produced. This can be done by changing the amounts of material sent to cracking units and reformers and by changing the conditions in these and other process steps. Some petroleum companies provide a computer with the data on market prices, current inventories, and crude oil compositions. The computer output then specifies the operating conditions that will yield the greatest profit for the company. The computer could then make the changes in these conditions directly, or this could be done manually. 

Some recent applications have benefited from advances in computing and computational techniques. Steady-state simulation is being used off-line for process analysis, design, and retrofit process simulators can model flow sheets with up to about a million equations by employing nested procedures. Other applications have resulted in great economic benefits these include on-line real-time optimization models for data reconciliation and parameter estimation followed by optimal adjustment of operating conditions. Models of up to 500,000 variables have been used on a refinery-wide basis. 

Linear programming can be used to solve very large problems, with thousands of variables and constraints. The method is widely used in operations, particularly in optimization of oil refineries and petrochemical plants. It is used a lot less in design, as design problems almost inevitably contain many nonlinear equations. 

The proposed approach is illustrated with a supply network to deliver gasoline from a refinery to one or two terminals through various routes by two means of transportation, i.e., pipelines and tanker-trucks. The optimal and near-optimal networks in the ranked order of cost are obtained for the two examples, one depicted in Figure 1 and the other depicted in Figure 2. Conventional and P-graph representations of an operating unit identified are provided in Table 1 as an example. 

A refinery aqueous stream flows at 600 gal/min and contains 15 Ib/hr benzene. The benzene is to be removed at a stripping efficiency of 99.95%. A tray column is to be used. Consider two operating pressures, 2.0 psia and 14.7 psia. With no attempt at optimization, evaluate the parameters that should be considered for design. In keeping with refinery practice, use conventional English units. 

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