Multiobjective optimization of ethylene cracking furnace system using self-adaptive multiobjective teaching-learning-based optimization
Multiobjective optimization of ethylene cracking furnace system using self-adaptive multiobjective teaching-learning-based optimization
复制标题
基于自适应多目标教学优化的乙烯裂解炉系统多目标优化
DOI：
10.1016/j.energy.2018.01.159
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发表时间：
2018
期刊：
影响因子：
9
通讯作者：
Wang Zhenlei
中科院分区：
文献类型：
--
作者：
Yu Kunjie;While Lyndon;Reynolds Mark;Wang Xin;Liang J J;Zhao Liang;Wang Zhenlei
乙烯裂解炉系统是烯烃装置的关键设备。多个裂解炉用于将各种烃原料转化为更小的烃分子，并且这些炉的操作条件显著地影响产品产率和燃料消耗。本文建立了一个工业裂解炉系统的多目标操作模型，该模型描述了基于当前原料分配的每个裂解炉的操作，并使用该模型来优化两个重要且相互冲突的目标：关键产品收率最大化和单位乙烯燃料消耗最小化。该模型结合了与物料平衡和输送线换热器出口温度有关的约束条件。改进了基于教与学的自适应多目标优化算法，并将其用于求解所设计的多目标优化问题，得到了具有多种解的Pareto前沿。一个真实的工业案例进行了调查，以说明所提出的模型的性能：返回的解决方案集提供了多种可能的实施方案，包括几个解决方案，既显着提高产品收率和较低的燃料消耗，与典型的操作条件相比。
The ethylene cracking furnace system is crucial for an olefin plant. Multiple cracking furnaces are used to convert various hydrocarbon feedstocks to smaller hydrocarbon molecules, and the operational conditions of these furnaces significantly influence product yields and fuel consumption. This paper develops a multiobjective operational model for an industrial cracking furnace system that describes the operation of each furnace based on current feedstock allocations, and uses this model to optimize two important and conflicting objectives: maximization of key products yield, and minimization of the fuel consumed per unit ethylene. The model incorporates constraints related to material balance and the outlet temperature of transfer line exchanger. The self-adaptive multiobjective teaching-learning-based optimization algorithm is improved and used to solve the designed multiobjective optimization problem, obtaining a Pareto front with a diverse range of solutions. A real industrial case is investigated to illustrate the performance of the proposed model: the set of solutions returned offers a diverse range of options for possible implementation, including several solutions with both significant improvement in product yields and lower fuel consumption, compared with typical operational conditions.