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.

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.

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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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in Wiley Online Library (wileyonlinelibrary.com).Multistage material handling processes are broadly used for manufacturing various products/jobs, where hoists are commonly used to transport inline products according to their processing recipes. When multiple types of jobs with different recipes are simultaneously and continuously handled in a production line, the hoist movement scheduling should be thoroughly investigated to ensure the operational feasibility of every job inline and in the meantime to maximize the productivity if possible. The hoist scheduling will be more complicated, if uncertainties of new coming jobs are considered, that is, the arrival time, type, recipe, and number of new jobs are totally unknown and unpredictable before they join the production line. To process the multiple jobs already inline and the newly added jobs, the hoist movements must be swiftly rescheduled and precisely implemented whenever new job(s) come. Because a reschedule has to be obtained online without violating processing time constraints for each job, the solution identification time for rescheduling must be taken into account by the new schedule itself. All these stringent requisites motivate the development of real-time dynamic hoist scheduling (RDHS) targeting online generation of reschedules for productivity maximization under uncertainties. Hitherto, no systematic and rigorous methodologies have been reported for this study. In this article, a novel RDHS methodology has been developed, which takes into account uncertainties of new coming jobs and targets real-time scheduling optimality and applicability. It generally includes a reinitialization algorithm to accomplish the seamless connection between the previous scheduling and rescheduling operations, and a mixed-integer linear programming model to obtain the optimal hoist reschedule. The RDHS methodology addresses all the major scheduling issues of multistage material handling processes, such as multiple recipes, multiple jobs, multicapacity processing units, diverse processing time requirements, and even optimal processing queue for new coming jobs. The efficacy of the developed methodology is demonstrated through various case studies.

The development of the Internet-of-Things (IoT) and the Cyber-Physical System (CPS) has greatly facilitated many aspects of technological applications and development. This may lead to significant data growth, especially for small files. The analysis and processing of a large number of small files has become a crucial part of the development of IoT and CPS. Hadoop Distributed File Systems have become powerful platforms to store a larger amount of big data. However, this method has a number of issues when dealing with small files, such as substantial memory consumption and poor access. In this paper, a Dynamic Queue of Small Files (DQSF) algorithm is proposed to solve these problems. DQSF differentiates small files into different categories using an analytical hierarchal process that examines the performance of small files with different ranges across four indexes and determines the size of the dynamic queue according to the best system performance. Additionally, period classification is applied to preprocess the small files before storage, and the prefetching mechanism of the secondary index is used to process index tables. Experimental results show that this method could effectively reduce memory use and improve the storage efficiency of massive small files, which optimizes system performance.

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