Optimization of a multiperiod refinery planning problem under uncertainty

Abstract

Uncertainty in refinery planning presents a significant challenge in determining the day-to-day operations of an oil refinery. Deterministic modeling techniques often fail to account for this uncertainty, potentially resulting in reduced profit. The stochastic programming framework explicitly incorporates parameter uncertainty in the problem formulation, thus giving preference to robust solutions. In this work, a nonlinear, multiperiod, industrial refinery problem is extended to a two-stage stochastic problem, formulated as a mixed-integer nonlinear program. A crude-oil sequencing case study is developed with binary scheduling decisions in both stages of the stochastic programming problem. Solution via a decomposition strategy based on the generalized Benders decomposition (GBD) algorithm is proposed. The binary decisions are designated as complicating variables that, when fixed, reduce the full-space problem to a series of independent scenario subproblems. Through the application of the GBD algorithm, a feasible mixed-integer solution is obtained that is more robust to uncertainty than its deterministic counterpart.

Modeling the Production Process of Fuel Gas, LPG, Propylene, and Polypropylene in a Petroleum Refinery Using Generalized Nets

“…In the literature, partial models of the diverse processes taking place in the chemical industry and in petroleum refining are made. For example, reference [1] deals with risk assessment in the chemical industry; reference [2] investigates the integration of engineering models with planning models; reference [3] focuses on the refinery-wide planning operations under uncertainties in product demand and price; reference [4] studies a nonlinear, multiperiod, industrial refinery problem extended to a two-stage stochastic problem, formulated as a mixed-integer nonlinear program; reference [5] employs a product trisection crude distillation unit model to build an accurate refinery model and determines the optimal crude selection using two-stage stochastic programming; reference [6] formulates a large-scale nonconvex mixed-integer nonlinear programming model and applies robust optimization for the multi-period operational planning of a real-world integrated refinery-petrochemical site in China under uncertain product demands and crude oil price; reference [7] deals with a refinery planning model that utilizes simplified empirical nonlinear process models with considerations for crude characteristics, product yields, and qualities, etc. ; reference [8] makes an assessment of refinery efficiency using linear programming; references [9][10][11][12][13][14] present studies related to planning and scheduling; and reference [15] investigates the efficiency of the process of crude oil dewatering and desalting.…”