Robust MINLP Optimization Model for Petrochemical Network Design under Uncertainty

This paper addresses the strategic planning, design and optimization of a network of petrochemical processes under uncertainty and risk considerations. The problem was formulated as a two-stage stochastic mixed-integer nonlinear programming model (MINLP) with parameter uncertainty considered in process yield, raw material and product prices, and upper and lower product market demand. Risk was accounted for in terms of deviation in both projected benefits in the first stage variables and process yield and forecasted demand in terms of the recourse variables. For each term, a different scaling factor was used to analyze the sensitivity of the petrochemical network due to variations of each component. The study showed that the final petrochemical network bears more sensitivity to variations in product demand for scaling parameters values that maintain the final petrochemical structure obtained form the stochastic model. The concept of Expected Value of Perfect Information (EVPI) and Value of the Stochastic Solution (VSS) are also investigated to numerically illustrate the value of including the randomness of the different model parameters. Modeling uncertainty in the process parameters provided a more robust analysis and practical perspective of this type of problems in the chemical industry.