TY - JOUR
T1 - Multiobjective optimization of ethylene cracking furnace system using self-adaptive multiobjective teaching-learning-based optimization
AU - Yu, Kunjie
AU - While, Lyndon
AU - Reynolds, Mark
AU - Wang, Xin
AU - Liang, J. J.
AU - Zhao, Liang
AU - Wang, Zhenlei
PY - 2018/4/1
Y1 - 2018/4/1
N2 - 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.
AB - 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.
KW - Ethylene cracking furnace
KW - Fuel consumption
KW - Multiobjective optimization
KW - Product yield
KW - Teaching-learning-based optimization
UR - http://www.scopus.com/inward/record.url?scp=85041748366&partnerID=8YFLogxK
U2 - 10.1016/j.energy.2018.01.159
DO - 10.1016/j.energy.2018.01.159
M3 - Article
AN - SCOPUS:85041748366
VL - 148
SP - 469
EP - 481
JO - Energy
JF - Energy
SN - 0360-5442
ER -