Dynamic Scheduling for Optimal Decoking Operation of Cracking Furnace System

Tuesday, April 3, 2012: 9:40 AM
382 (George R. Brown Convention Center)
Qiang Xu, Chuanyu Zhao and Kuyen Li, Dan F. Smith Department of Chemical Engineering, Lamar University, Beaumont, TX

Thermal cracking process employs multiple cracking furnaces in parallel to convert various hydrocarbon feedstocks to multiple smaller-molecular hydrocarbon products.  The operation is a typical semi-continuous dynamic process, where cracking furnaces have to be periodically shut down for hot maintenance (e.g., decoking).  Given the data of multiple feed characteristics, different furnace performances, various sale income of products and manufacturing costs, as well as specified operational constraints, an optimal decoking policy for the entire cracking system has to be determined to achieve the best economic performance of an ethylene cracking furnace system.  In practice, because of the feed supply and maintenance uncertainties, the furnace decoking activities are better to be conducted in a dynamic and reactive way, by which the entire furnace system operations can be timely reconciliated once the impact of an uncertainty has triggered the projection of necessary operational change.  Thus, the feeds from the new delivery and the leftover inventories can be timely, feasibly, and optimally allocated to available cracking furnaces to obtain the maximum average net profit for the coming operating time period. 

Facing this challenge, this paper develops a novel reactive scheduling model to help generating dynamic decoking strategy for the entire furnace system.  It can optimize multiple-feed assignments, running length of each batch operation, and decoking sequence for every furnace in the system based on the new feed deliveries, the leftover feeds, and current furnace operating conditions.  It also simultaneously addresses major scheduling issues for real cracking furnace operations, such as non-simultaneous decoking, secondary ethane cracking, and seamless rescheduling.  The developed model is solved by an effective solving strategy.  The efficacy of the study and its significant economic potential are demonstrated by comprehensive case studies

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