Assessment of Lagrangean decomposition for short-term planning of integrated refinery-petrochemical operations

We present an integrated methodology for optimal short-term planning of integrated refinery-petrochemical complexes (IRPCs) and demonstrate it on a full-scale industrial case study under four realistic planning sce-narios. The large-scale mixed-integer quadratically constrained optimization models are amenable to a spatial Lagrangean decomposition through dividing the IRPC into multiple subsections, which comprise crude man-agement, refinery, fuel blending, and petrochemical production. The decomposition algorithm creates virtual markets for trading crude blends and intermediate petrochemical streams within the IRPC and seeks an optimal tradeoff in such markets, with the Lagrange multipliers acting as transfer prices. The best results are obtained for decompositions with two or three subsections, achieving optimality gaps below 4% in all four planning scenarios. The Lagrangean decomposition provides tighter primal and dual bounds than the global solvers BARON and ANTIGONE, and it also improves the dual bounds computed using piecewise linear relaxation strategies.

Automated summary

This study presents an integrated methodology for optimal short-term planning of integrated refinery-petrochemical complexes (IRPCs) and demonstrates its effectiveness through a full-scale industrial case study. The methodology uses a spatial Lagrangean decomposition to divide the IRPC into multiple subsections, which facilitates the creation of virtual markets for trading crude blends and intermediate petrochemical streams. The results show that decompositions with two or three subsections achieve optimal solutions with small optimality gaps, outperforming global solvers and relaxation strategies.