• DocumentCode
    65514
  • Title

    Short-Term Scheduling of Crude-Oil Operations: Enhancement of Crude-Oil Operations Scheduling Using a Petri Net-Based Control-Theoretic Approach

  • Author

    NaiQi Wu ; Mengchu Zhou ; Zhiwu Li

  • Author_Institution
    Macau Univ. of Sci. & Technol., Macau, China
  • Volume
    22
  • Issue
    2
  • fYear
    2015
  • fDate
    Jun-15
  • Firstpage
    64
  • Lastpage
    76
  • Abstract
    To effectively operate a refinery and make it competitive, efficient short-term scheduling techniques that utilize commercial software tools for practical applications need to be developed. However, cumbersome details make it difficult to solve the short-term scheduling problem (STSP) of crude-oil operations, and mathematical programming models fail to meet the industrial needs. This article proposes an innovative control-theoretic and formal model-based method to tackle this long-standing issue. This method first models the STSP as a hybrid Petri net (PN) and then derives critically important schedulability conditions. The conditions are used to decompose a complex problem into several tractable subproblems. In each subproblem, there are either continuous variables or discrete variables. For subproblems with continuous variables, this work proposes a linear programming-based method to solve them; while, for subproblems with discrete variables, this work adopts efficient heuristics. Consequently, the STSP is efficiently resolved, and the application of the proposed method is well illustrated via industrial case studies.
  • Keywords
    Petri nets; crude oil; linear programming; scheduling; STSP; commercial software tool; crude-oil operation; formal model-based method; hybrid PN; hybrid Petri net-based control theoretic approach; innovative control-theoretic approach; linear programming-based method; mathematical programming model; refinery; short-term scheduling problem; Control theory; Heuristic algorithms; Job shop scheduling; Refineries; Scheduling;
  • fLanguage
    English
  • Journal_Title
    Robotics & Automation Magazine, IEEE
  • Publisher
    ieee
  • ISSN
    1070-9932
  • Type

    jour

  • DOI
    10.1109/MRA.2015.2415047
  • Filename
    7108023