Cracking furnaces of ethylene plants are capable of processing multiple feeds to produce smaller hydrocarbon molecules, such as ethylene, propylene, and ethane. The best practice for handling the produced ethane is to recycle it as an internal feed and conduct the secondary cracking in a specific furnace. As cracking furnaces have to be periodically shut down for decoking, when multiple furnaces processing different feeds under various product values and manufacturing costs are considered, the operational scheduling for the entire furnace system should be optimized to achieve the best economic performance. In this paper, a new MINLP (mixedinteger nonlinear programming) model has been developed to optimize the operation of cracking furnace systems with the consideration of secondary ethane cracking. This model is more practical than the previous study and can simultaneously identify the allocation of feeds with their quantity, time, and sequence information for each cracking furnace. A case study has demonstrated the efficacy of the developed scheduling model.

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Multistage compression systems (MSCS) are the most important and valuable facilities in chemical plants, whose failure may cause severe accidents and/or tremendous economic loss. Thus, operation for MSCS needs sufficient care under various situations, especially during plant startup. This paper employs rigorous pressuredriven dynamic simulations to examine and improve operation safety of the cracked gas compression system during an ethylene plant startup. For safety consideration, antisurge process design and control strategies are dynamically evaluated along with startup procedures. Operating point trajectory for each compressor and their potential safety problems are identified. Assisted by the rigorous dynamic simulation, the plant startup procedure is improved with better safety performance.

The cracking furnace system is crucial for an olefin plant. Its operation needs to follow a predefined schedule to process various feeds continuously, meanwhile conducting a periodically decoking operation for each furnace when its performance apparently decreases. In practice, because the feed supply changes dynamically, the routine furnace scheduling is better performed in a dynamic and reactive way, through which the furnace operations can be smartly rescheduled with respect to any delivery of new coming feeds. Thus, the feeds from the new delivery and the leftover inventories can be timely, feasibly, and optimally allocated to different furnaces for processing to obtain the maximum average net profit per time. Facing this challenge, this paper develops a new MINLP-based reactive scheduling strategy, which can dynamically generate reschedules based on the new feed deliveries, the leftover feeds, and current furnace operating conditions. It simultaneously addresses all the major scheduling issues of a cracking furnace system, such as semicontinuous operation, nonsimultaneous decoking, secondary ethane cracking, and seamless rescheduling. The efficacy of the study and its significant economic potential are demonstrated by a comprehensive case study.

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