Petrochemical robust

The flexibility in the petrochemical industry production and the availability of many process technologies require adequate strategic planning and a comprehensive analysis of all possible production alternatives. Therefore, a model is needed to provide the development plan of the petrochemical industry. The model should account for market demand variability, raw material and product price fluctuations, process yield inconsistencies, and adequate incorporation of robustness measures. 

This chapter addresses the planning, design and optimization of a network of petrochemical processes under uncertainty and robust considerations. Similar to the previous chapter, robustness is analyzed based on both model robustness and solution robustness. Parameter uncertainty includes process yield, raw material and product prices, and lower product market demand. The expected value of perfect information (EVPI) and the value of the stochastic solution (VSS) are also investigated to illustrate numerically the value of including the randomness of the different model parameters. 

The remainder of this Chapter is organized as follows. In Section 8.2 we will discuss model formulation for petrochemical network planning under uncertainty and with uncertainty and risk consideration, referred to as robust optimization. Then we will briefly explain the concept of value of information and stochastic solution, in Section 8.3. In Section 8.4, we will illustrate the performance of the model through an industrial case study. The chapter ends with concluding remarks in Section 8.5. 

In this study, operational risk was accounted for in terms of variance in both projected benefits, represented by first stage variables, and forecasted demand, represented by the recourse variables. The variability in the projected benefit represents the solution robustness where the model solution will remain close to optimal for all scenarios. On the other hand, variability of the recourse term represents the model robustness where the model solution will almost be feasible for all scenarios. This approach gives rise to a multiobjective analysis in which scaling factors are used to evaluate the sensitivity due to variations in each term. The projected benefits variation was scaled by 0i, and deviation from forecasted demand was scaled by 02, where different values of 0i and 02 were used in order to observe the sensitivity of each term on the final petrochemical complex. The objective function with risk consideration can be written as follows ... 

C. Pasquini, E.V. Aquino, M.V. Rebougas, F.B. Gonzaga, Robust flow-batch coulo-metric/biamperometric titration system Determination of bromine index and bromine number of petrochemicals, Anal. Chim. Acta 600 (2007) 84. 

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