基于智能算法的多品质多性质原油调合与优化

BLENDING AND OPTIMIZATION OF CRUDE OIL WITH MULTI-QUALITY AND MULTI-PROPERTIES BASED ON INTELLIGENT ALGORITHM

针对炼油厂依靠传统经验调合原油难以满足加工原油质量要求和企业利润较低的问题,建立了基于原油相似度最大化和生产利润最大化的多目标优化数学模型,在比较5种鲁棒性较强的算法性能基础上,选择融合莱维飞行和随机游动策略的灰狼算法(LRGWO)对模型进行求解。结果表明:在LRGWO算法、灰狼算法(GWO)、第二代非支配排序遗传算法(NSGA-Ⅱ)、第三代非支配排序遗传算法(NSGA-Ⅲ)和粒子群算法(PSO)中,LRGWO算法所得最优解集的覆盖率、算法的整体性能最优;经过优化求解后,调合原油T1~T4与对应目标原油W1~W4的平均相似度达到95.82%,说明利用所建原油选择和调合模型可以得到与目标原油物性相近的调合原油;根据10种备选原油的价格,在最大采购量为175000 t的前提下,通过生产利润最大化模型优化调合原油T1~T4的加工量依次为50000,55000,40000,30000 t,企业生产利润最大为3358.19万元。

Aiming at the problems of refineries relying on traditional experience to blend crude oil to meet the quality requirements of processed crude oil and the low profit of enterprises,a multi-objective optimization mathematical model based on the maximization of crude oil similarity and the maximization of production profit was established,and the Gray Wolf Algorithm,which combined the Levy Flight and Randomized Swimming Strategies(LRGWO),was chosen to solve the model after comparing the performance of five robust algorithms.The results showed that in LRGWO,Gray Wolf Algorithm(GWO),second-generation Non-dominated Sorting Genetic Algorithm(NSGA-Ⅱ),third-generation Non-dominated Sorting Genetic Algorithm(NSGA-Ⅲ)and Particle Swarm Algorithm(PSO),the coverage of the optimal solution set obtained from the LRGWO algorithm and the overall performance of algorithm were optimal.After optimization,the average similarity between the blended crude oils T1-T4 and the corresponding target crude oils W1-W4 reached 95.82%,which indicated that the blended crude oils with similar physical properties to the target crude oils could be obtained by using the constructed crude oil selection and blending model.According to the prices of the 10 kinds of crude,under the premise of the maximum purchasing quantity of 175 kt,the production profit maximization model optimizes the processing quantity of blended crude T1-T4 to be 50,55,40 and 30 kt,respectively.The maximum production profit of the enterprise is 33.5819 million Yuan.