Planning and scheduling for petroleum refineries using mathematical programming

Abstract

-The objective of this paper is the development and solution of nonlinear and mixed-integer (MIP) optimization models for real-world planning and scheduling problems in petroleum refineries. Firstly, we present a nonlinear planning model that represents a general refinery topology and allows implementation of nonlinear process models as well as blending relations. The optimization model is able to define new operating points, thus increasing the production of the more valuable products and simultaneously satisfying all specification constraints. The second part addresses scheduling problems in oil refineries, which are formulated as MIP optimization models and rely on both continuous and discrete time representations. Three practical applications closely related to the current refinery scenario are presented. The first one addresses the problem of crude oil inventory management of a refinery that receives several types of crude oil delivered exclusively by a single oil pipeline. Subsequently, two optimization models intended to define the optimal production policy, inventory control and distribution are proposed and solved for the fuel oil and asphalt plant. Finally, the planning model of is extended in order to sequence decisions at the scheduling level in the liquefied petroleum gas (LPG) area for maximization of the production of petrochemical-grade propane and product delivery.

Lessons learned from developing and implementing refinery production scheduling technologies

“…1). In other words, RPS should "unfold" optimal guidelines into feasible orders, which must be operationally executable (Joly and Pinto, 2003;Joly et al, 2002). Typically, distinctions between modeling approaches may comprise differentiated representations of refinery resources (e.g., aggregated tankage in planning replaced with individual tanks in scheduling) and process models (e.g., approximated linear models in planning replaced with nonlinear or rigorous process models in scheduling).…”

Chemical Engineering and Operations Research: A Passing Flirtation or Marriage?

“…As far as possible, M.Sc. programs should address real-world industrial problems which may be part of larger OR projects associated with scientific/ technological collaborations between the academy and the industry (e.g., JOLY et al, 2002). These technological partnerships have valuable advantages, such as shortening the learning curve of young researchers, since they establish tight collaboration with experienced engineers with profound knowledge of the business.…”

Tactical level planning in float glass manufacturing with co-production, random yields and substitutable products

“…Bitran and Leong [6] propose a dynamic programming algorithm for solving a planning problem, and investigate approximate solution techniques. Joly et al [13] consider a planning problem in petroleum refineries, where several products are produced simultaneously. They model the problem as a mixed-integer nonlinear program, assuming that types and ratios of co-products are known in advance.…”

“…Semiconductor manufacturing involves a highly complex set of processes, some of which cannot be kept under precise control, resulting in co-production of products having several quality levels. On the contrary to [13], the authors of [5] model co-production rates as (uncontrollable) random variables, which follow a probability distribution. Unlike these applications, the co-production structure in float glass manufacturing allows for the type and ratio of products produced simultaneously to be controlled within certain bounds.…”