基于自适应差分进化的常压塔轻质油产量多目标优化

常压塔轻质油产量最大化是提高企业效益的重要途径之一.为了适应市场需求和价格变化,生产高需求与高价值的轻质油产品,提出一种基于自适应差分进化的常压塔轻质油产量多目标优化算法.该算法采用惩罚边界交叉法的分解方法,在种群变异阶段引入择优学习算子来改进传统变异算子随机选取个体或者单纯选取最好个体的随机性和盲目性,利用自适应策略逐渐改变交叉变异算子.将改进算法应用于3种测试函数和实际炼油厂常压塔轻质油产量优化,结果表明所提出的算法在测试函数上具有明显优势,并能有效提高常压塔轻质油产量,验证了所提算法的有效性.

Multi-objective optimization of light oil production in atmospheric distillation column based on self-adaptive differential evolution

Maximizing the production of light oil in atmospheric distillation column is one of the important ways to improve the benefit of enterprises. In order to adapt to the change of market demand and price, and to produce high-demand and high-value light oil, a multi-objective optimization algorithm for light oil production of atmospheric distillation column based on self-adaptive differential evolution is proposed. The algorithm adopts the decomposition method of the penalty boundary intersection method, and introduces the perferred learning operator in the stage of population mutation to decrease the randomness and blindness brought about by the random selection or the simple selection of the best individual by the traditional operator. The self-adaptive strategy is used to gradually change the crossover mutation operator. The improved algorithm is applied to three kinds of test functions and the actual refinery atmospheric distillation column light oil production optimization. The results show that the proposed algorithm has obvious advantages in testing functions and can effectively improve the atmospheric distillation column light oil production, which verifies the effectiveness of the proposed algorithm.