Refinery Planning

Most refineries develop iadividual octane blending equations which do a good job of predicting that refinery s blending behavior. In order to use these equations ia refinery planning and operations, these may be linearized ia a piecewise fashion. 

In this section, we present two examples with different scenarios. The first example illustrates the performance of the model on a single site total refinery planning problem where we compare the results of the model to an industrial scale study from Favennec et al. (2001). This example serves to validate our model and to make any necessary adjustments. The second example extends the scale of the model application to cover three complex refineries in which we demonstrate the different aspects of the model. The refineries considered are of large industrial-scale refineries and actually mimic a general set-up of many areas around the world. The decisions in this example include the selection of crude blend combination, design of process integration network between the three refineries, and decisions on production units expansion options and operating levels. 

We slightly adjusted our model to allow for spot market buying and selling of heavy naphtha, vacuum gas oil and all products in order to demonstrate actual total site refinery planning and compare our results with those of Favennec et al. (2001). The model results and a comparison are shown in Table 3.2. 

Table 3.1 Major refinery capacity constraints for single refinery planning.

Table 3.2 Model results and comparison of single refinery planning.

Table 3.3 Major refineries capacity constraints for multisite refinery planning, Scenario-1 and 2.

Table 3.4 Model results of multisite refinery planning Scenario-1.

Therefore, in this approach, we develop Risk Model III as a reformulation of Risk Model II by employing the mean-absolute deviation (MAD), in place of variance, as the measure of operational risk imposed by the recourse costs to handle the same three factors of uncertainty (prices, demands, and yields). To the best of our knowledge, this is the first such application of MAD, a widely-used metric in the area of system identification and process control, for risk management in refinery planning. 

It is desirable to demonstrate that the proposed stochastic formulations provide robust results. According to Mulvey, Vanderbei, and Zenios (1995), a robust solution remains close to optimality for all scenarios of the input data while a robust model remains almost feasible for all the data of the scenarios. In refinery planning, model robustness or model feasibility is as essential as solution optimality. For example, in mitigating demand uncertainty, model feasibility is represented by an optimal solution that has almost no shortfalls or surpluses in production. A trade-off exists... 

We demonstrate the implementation of the proposed stochastic model formulations on the refinery planning linear programming (LP) model explained in Chapter 2. The original single-objective LP model is first solved deterministically and is then reformulated with the addition of the stochastic dimension according to the four proposed formulations. The complete scenario representation of the prices, demands, and yields is provided in Table 6.2. 

In Chapter 3 of this book we discussed the problem of multisite refinery integration under deterministic conditions. In this chapter, we extend the analysis to account for different parameter uncertainty. Robustness is quantified based on both model robustness and solution robustness, where each measure is assigned a scaling factor to analyze the sensitivity of the refinery plan and integration network due to variations. We make use of the sample average approximation (SAA) method with statistical bounding techniques to generate different scenarios. 

This example illustrates the performance of the proposed approach on a single site total refinery planning problem. The refinery scale, capacity and configuration mimic an existing refinery in the Middle East. Figure 7.1 is a state equipment network (SEN)... 

Figure 7.2 Single refinery planning optimality gap variations with sample size.

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