Computers & Chemical Engineering ( IF 3.9 ) Pub Date : 2020-09-20 , DOI: 10.1016/j.compchemeng.2020.107100 Debashish Panda , Feleke Bayu , Manojkumar Ramteke
Gasoline blending is an important downstream operation in the refinery. The operation is susceptible to uncertainties such as fluctuation in component quality, fluctuation in demand, and a combination of both. These problems naturally involve multiple objectives and non-linear terms corresponding to the mixing of the components for which the genetic algorithm-based approach is more suitable compared to traditional mathematical programming. In this study, such graphical genetic algorithm-based reactive scheduling approach is developed which can handle dynamic changes in component quality and demand as an additional layer of decision making over the nominal scheduling. Three industrial-scale examples are solved using the developed approach for both single- and two-objective optimizations while handling the uncertainty of 10% increase in demand and 5% decrease in component quality. In single-objective optimization, the production cost is minimized whereas in two-objective optimization additionally the fluctuation in blending processing rate is minimized.
中文翻译:
使用图形遗传算法在存在需求和组件不确定性的情况下汽油混合和产品交付的离散时间反应性调度
汽油混合是炼厂的重要下游操作。操作容易受到不确定性的影响,例如组件质量的波动,需求的波动以及二者的结合。这些问题自然涉及多个目标和对应于组分混合的非线性项,与传统的数学编程相比,基于遗传算法的方法更适合这些组分的混合。在这项研究中,开发了基于图形遗传算法的反应式调度方法,该方法可以处理组件质量和需求的动态变化,作为名义调度之外的决策层。使用已开发的方法对单目标和两个目标进行了优化,解决了三个工业规模的示例,同时处理了需求增加10%和组件质量减少5%的不确定性。在单目标优化中,生产成本降至最低,而在二目标优化中,混合加工速率的波动也降至最低。