基于EGO算法的常压塔能量优化

常减压装置能量消耗约占炼厂总用能的25%～30%,在保证产品产量与质量的条件下,优化常减压蒸馏塔操作条件,可有效降低能耗.为了避免随机优化算法对常压塔机理模型进行操作优化时,存在计算资源消耗大、效率低的问题,文中采用基于代理模型的全局优化方法优化常压塔的余热回收过程,在优化迭代过程中用Kriging代理模型来代替耗时的精确模型评估.实验表明模型调用次数相较于粒子群优化算法减少了90%,优化时间减少了85%,实现了能量优化并且保证了侧线产品之间的分离精度.

Atmospheric tower energy optimization based on EGO

Energy consumption of crude oil distillation unit accounts for about 25% to 30% of refinery total energy. Satisfying the request of product yield and quality, optimizing crude distillation column operating conditions can effectively reduce energy consumption. Using stochastic optimization algorithms directly optimizing model of atmospheric tower is time-consuming and low efficient. In this paper, efficient global optimization algorithm based on surrogate model is applied to optimization of atmospheric tower's heat recovery. Kriging surrogate model is used as a replacement to the original model which is time-consuming in iterative optimization process. The result shows that this method decreases 90% number of assessment and decreases 85% optimization time compared with particle swarm optimization and realizes energy savings and meets product separation accuracy requirements.