Multiperiod Optimization for Production Planning of Petroleum Refineries

Neiro, Sérgio Mauro da Silva; Pinto, José M.

doi:10.1080/00986440590473155

Cited by 82 publications

(43 citation statements)

References 16 publications

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“…Neiro and Pinto [238] presented an MINLP implementation for multiperiod refinery planning models using empirical relations. Alattas et al [239] presented a multiperiod MINLP model that utilizes a more precise representation of the crude distillation unit, using the model introduced by Geddes [240].…”

Section: Planning and Schedulingmentioning

confidence: 99%

Review of Mixed‐Integer Nonlinear and Generalized Disjunctive Programming Methods

Trespalacios

Grossmann

2014

Chemie Ingenieur Technik

152

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This work presents a review of the applications of mixed-integer nonlinear programming (MINLP) in process systems engineering (PSE). A review on the main deterministic MINLP solution methods is presented, including an overview of the main MINLP solvers. Generalized disjunctive programming (GDP) is an alternative higher-level representation of MINLP problems. This work reviews some methods for solving GDP models, and techniques for improving MINLP methods through GDP. The paper also provides a high-level review of the applications of MINLP in PSE, particularly in process synthesis, planning and scheduling, process control and molecular computing.

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Section: Planning and Schedulingmentioning

confidence: 99%

Review of Mixed‐Integer Nonlinear and Generalized Disjunctive Programming Methods

Trespalacios

Grossmann

2014

Chemie Ingenieur Technik

152

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“…In our previous work 14 , the Heaviside function was used for this disjunction, yielding the following FI equation for the distributed components, (11) or the simplified form substituting the equilibrium constant for the relative volatility 19 , (12) where (13) As opposed to our previous planning model above, the model presented in this paper corresponds to an MINLP. Therefore, we consider exact representation of the disjunction (10) with linear mixed integer constraints using convex hull as in (14) and big M formulation as in (15) and (16) …”

Section: Fi Modelmentioning

confidence: 99%

“…Multiperiod refinery planning models tend to utilize linear equations for the process units 10,11,12 . The nonlinear implementation for the process unit in multiperiod planning models rely mainly on empirical relations 13 .…”

Section: Introductionmentioning

confidence: 99%

Refinery Production Planning: Multiperiod MINLP with Nonlinear CDU Model

Alattas

Grossmann

Palou-Rivera

2012

Ind. Eng. Chem. Res.

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In a previous paper, Alattas, Grossmann & Palou--Rivera (2011) developed a single--period, nonlinear programing refinery planning model using the fractionation index (FI) for the crude distillation unit (CDU) equations. In this paper, the single period model is modified to a mixed--integer nonlinear programming (MINLP) model to determine the sequencing, changeovers and processing times of crude oils over multiple time periods. The MINLP equations include traveling salesman problem constraints to generate the crude oil sequences that maximize profit. Moreover, the disjunction for the fractionation index (FI) is formulated with mixed integer constraints as opposed to the Heaviside function formulation of the previous work. The resulting model is shown to be robust and relatively fast. When subcycles arise, they are eliminated by adding appropriate subtour elimination constraints. Examples with up to 5 crude oils and 6 weeks time horizon are presented to illustrate the application of the proposed model. 2

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“…The former is largely adopted in the literature and concerns large gross‐margins, where processes are operated at fixed conditions and recipes. The latter is typical of blend sites, refineries, oil and gas processing, and those plants are characterized by reduced margins …”

Section: Supply Chain Managementmentioning

confidence: 99%

Raising the Decision‐Making Level to Improve the Enterprise‐Wide Production Flexibility

et al. 2012

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When market demand significantly changes, as in the ongoing worldwide economic crisis, many production plants are forced to operate far from nominal conditions. In this case, the current plant-wide optimization of production sites is a myopic approach that could lead to plant inefficiencies and unconventional operation issues, thus, resulting in ineffective prevention of economic losses. A way to tackle low-demand conditions is to raise the decision-making process from the plant-wide (or business-wide) level to the enterprise-wide (or corporate) level by assigning a Boolean variable to each production site so as to manage their on/off status. By doing so, certain additional (social) constraints may become relevant. The case of operating industrial gases supply chains is considered. © 2012 American Institute of Chemical Engineers

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Multiperiod Optimization for Production Planning of Petroleum Refineries

Cited by 82 publications

References 16 publications

Review of Mixed‐Integer Nonlinear and Generalized Disjunctive Programming Methods

Review of Mixed‐Integer Nonlinear and Generalized Disjunctive Programming Methods

Refinery Production Planning: Multiperiod MINLP with Nonlinear CDU Model

Raising the Decision‐Making Level to Improve the Enterprise‐Wide Production Flexibility

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