Artificial intelligence methodologies for agile refining: an overview

被引:11
|
作者
Srinivasan, Rajagopalan [1 ]
机构
[1] Natl Univ Singapore, Dept Chem & Biomol Engn, Singapore 119260, Singapore
[2] Inst Chem & Engn Sci, Singapore, Singapore
关键词
petroleum refining; supply chain management; decision support; enterprise-wide optimization; process supervision; fault diagnosis; pattern recognition;
D O I
10.1007/s10115-006-0057-z
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Agile manufacturing is the capability to prosper in a competitive environment of continuous and unpredictable changes by reacting quickly and effectively to the changing markets and other exogenous factors. Agility of petroleum refineries is determined by two factors - ability to control the process and ability to efficiently manage the supply chain. In this paper, we outline some challenges faced by refineries that seek to be lean, nimble, and proactive. These problems, which arise in supply chain management and operations management are seldom amenable to traditional, monolithic solutions. As discussed here using several examples, methodologies drawn from artificial intelligence - software agents, pattern recognition, expert systems - have a role to play in this path toward agility.
引用
收藏
页码:129 / 145
页数:17
相关论文
共 50 条
  • [1] Artificial intelligence methodologies for agile refining: an overview
    Rajagopalan Srinivasan
    Knowledge and Information Systems, 2007, 12 : 129 - 145
  • [2] An Overview of Artificial Intelligence Applications for Power Electronics
    Zhao, Shuai
    Blaabjerg, Frede
    Wang, Huai
    IEEE TRANSACTIONS ON POWER ELECTRONICS, 2021, 36 (04) : 4633 - 4658
  • [3] An Overview of Artificial Intelligence-Based Techniques for PEMFC System Diagnosis
    Sharma, Priynka
    Cirrincione, Maurizio
    Mohammadi, Ali
    Cirrincione, Giansalvo
    Kumar, Rahul R.
    IEEE ACCESS, 2024, 12 : 165708 - 165735
  • [4] Fault Tolerant Control, Artificial Intelligence and Predictive Analytics for Aerospace Systems: An Overview
    Kumar, Krishna Dev
    Muthusamy, Venkatesh
    INFORMATION, COMMUNICATION AND COMPUTING TECHNOLOGY, 2017, 750 : 351 - 362
  • [5] Artificial intelligence techniques for stability analysis and control in smart grids: Methodologies, applications, challenges and future directions
    Shi, Zhongtuo
    Yao, Wei
    Li, Zhouping
    Zeng, Lingkang
    Zhao, Yifan
    Zhang, Runfeng
    Tang, Yong
    Wen, Jinyu
    APPLIED ENERGY, 2020, 278
  • [6] Sources of artificial intelligence
    Sargent, Thomas J.
    JOURNAL OF ECONOMIC DYNAMICS & CONTROL, 2025, 172
  • [7] Artificial intelligence and headache
    Stubberud, Anker
    Langseth, Helge
    Nachev, Parashkev
    Matharu, Manjit S.
    Tronvik, Erling
    CEPHALALGIA, 2024, 44 (08)
  • [8] The SP Theory of Intelligence: An Overview
    Wolff, J. Gerard
    INFORMATION, 2013, 4 (03) : 283 - 341
  • [9] Artificial Intelligence Methodologies in Smart Grid-Integrated Doubly Fed Induction Generator Design Optimization and Reliability Assessment: A Review
    Behara, Ramesh Kumar
    Saha, Akshay Kumar
    ENERGIES, 2022, 15 (19)
  • [10] APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNIQUES IN PROCESS FAULT DIAGNOSIS
    Hussain, M. A.
    Hassan, C. R. Che
    Loh, K. S.
    Mah, K. W.
    JOURNAL OF ENGINEERING SCIENCE AND TECHNOLOGY, 2007, 2 (03) : 260 - 270