Scheduling of crude oil operations under demand uncertainty: A robust optimization framework coupled with global optimization

Computational Experience with Piecewise Linear Relaxations for Petroleum Refinery Planning

Refinery planning optimization is a challenging problem as regards handling the nonconvex bilinearity, mainly due to pooling operations in processes such as crude oil distillation and product blending. This work investigated the performance of several representative piecewise linear (or piecewise affine) relaxation schemes (referred to as McCormick, bm, nf5, and nf6t) and de (which is a new approach proposed based on eigenvector decomposition) that mainly give rise to mixed-integer optimization programs to convexify a bilinear term using predetermined univariate partitioning for instances of uniform and non-uniform partition sizes. The computational results showed that applying these schemes improves the relaxation tightness compared to only applying convex and concave envelopes as estimators. Uniform partition sizes typically perform better in terms of relaxation solution quality and convergence behavior. It was also seen that there is a limit on the number of partitions that contribute to relaxation tightness, which does not necessarily correspond to a larger number of partitions, while a direct relationship between relaxation size and tightness does not always hold for non-uniform partition sizes.

Uncertainty in chemical process systems engineering: a critical review

Uncertainty or error occurs as a result of a lack or misuse of knowledge about specific topics or situations. In this review, we recall the differences between error and uncertainty briefly, first, and then their probable sources. Then, their identifications and management in chemical process design, optimization, control, and fault detection and diagnosis are illustrated. Furthermore, because of the large amount of information that can be obtained in modern plants, accurate analysis and evaluation of those pieces of information have undeniable effects on the uncertainty in the system. Moreover, the origins of uncertainty and error in simulation and modeling are also presented. We show that in a multidisciplinary modeling approach, every single step can be a potential source of uncertainty, which can merge into each other and generate unreliable results. In addition, some uncertainty analysis and evaluation methods are briefly presented. Finally, guidelines for future research are proposed based on existing research gaps, which we believe will pave the way to innovative process designs based on more reliable, efficient, and feasible optimum planning.