Cyclic scheduling for an ethylene cracking furnace system using diversity learning teaching-learning-based optimization

Kunjie Yu, Lyndon While, Mark Reynolds, Xin Wang, Zhenlei Wang

    Research output: Contribution to journalArticlepeer-review

    25 Citations (Scopus)

    Abstract

    The ethylene cracking furnace system is central to an olefin plant. Multiple cracking furnaces are employed for processing different hydrocarbon feeds to produce various smaller hydrocarbon molecules, such as ethylene, propylene, and butadiene. We develop a new cyclic scheduling model for a cracking furnace system, with consideration of different feeds, multiple cracking furnaces, differing product prices, decoking costs, and other more practical constraints. To obtain an efficient scheduling strategy and the optimal operational conditions for the best economic performance of the cracking furnace system, a diversity learning teaching-learning-based optimization (DLTLBO) algorithm is used to simultaneously determine the optimal assignment of multiple feeds to different furnaces, the batch processing time and sequence, and the optimal operational conditions for each batch. The performance of the proposed scheduling model and the DLTLBO algorithm is illustrated through a case study from a real-world ethylene plant: experiments show that the new algorithm out-performs both previous studies of this set-up, and the basic TLBO algorithm.

    Original languageEnglish
    Pages (from-to)314-324
    Number of pages11
    JournalComputers and Chemical Engineering
    Volume99
    DOIs
    Publication statusPublished - 6 Apr 2017

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