粒子群优化
催化裂化
多目标优化
数学优化
多群优化
遗传算法
炼油厂
替代模型
石脑油
过程(计算)
盈利能力指数
进化算法
计算机科学
工程类
开裂
数学
材料科学
化学
废物管理
经济
催化作用
复合材料
操作系统
生物化学
财务
作者
José Fernando Cuadros Bohórquez,Laura Plazas Tovar,Maria Regina Wolf Maciel,Delba C. Melo,Rubens Maciel Filho
标识
DOI:10.1080/00986445.2019.1613230
摘要
This article provides a concise multiobjective optimization methodology for an industrial fluid catalytic cracking unit (FCCU) considering stochastic optimization techniques, genetic algorithms (GA) and particle swarm optimization (PSO), based on surrogates or meta-models in order to approximate the objective function. A FCCU was considered and simulated in an AspenONE process simulator. In addition the article examines the claim that PSO has the same effectiveness (finding the optimal global solution) as GA, but with significantly better computational efficiency (fewer function evaluations). The optimization results obtained with the PSO technique, based on the evaluation of less functions and adjustment of less parameters, showed a 3% increase in yield of naphtha as compared to results obtained with the GA technique. Finally, the results of the optimization obtained with the stochastic optimization techniques were compared and analyzed with a deterministic one. The performance targets of the multiobjective operational optimization supported the FCCU design and production planning to ensure refinery profitability and a regulatory environment.
科研通智能强力驱动
Strongly Powered by AbleSci AI