Multi-objective Optimization Of Short-term Scheduling For Dynamic Mixed Crude Oil

Crude oil can generate various crude oil products through industrial processes.As ”industrial blood”,crude oil plays a wide range of roles in the social economy.The demand for the quantity and variety of crude oil in different countries in the world is increasing day by day,and the severe challenges faced by the global ecological environment are also more severe.As a result,there are more stringent demands on the production of refineries.It is not only necessary to improve the utilization rate of crude oil and enterprise efficiency,but also to meet the needs of energy saving and environmental protection.Due to the complexity of such problems,there is currently a lack of effective tools and software to formulate short-term detailed scheduling plans.Most enterprises use the method of manual repeated debugging,which is difficult to ensure the optimization and feasibility of scheduling.Therefore,finding effective technologies for short-term scheduling and formulating detailed optimization strategies can provide feasible solutions for production planning with less labor consumption and time.In this way,the development of the refining industry is further promoted.For the short-term production plan of crude oil processing,some scholars use the linear programming method to solve the problem.Although this method can theoretically obtain the optimal solution to the scheduling problem,the computational cost will increase with the size of the problem during the solution process,which will make it difficult to solve,and the linear programming problem needs to know the events in the whole cycle.However,this requirement cannot be met in actual production,so this method cannot be directly used to solve the short-term scheduling problem of crude oil.Due to the complexity of the crude oil scheduling problem,the oil scheduling problem is divided into upper and lower layers based on the hierarchical idea of control theory.The upper layer solves the relevant objectives of production plan optimization according to the schedulable conditions,and the lower layer realizes the upper layer refining plan through detailed scheduling.Based on the idea of stratification,this paper studies the low-level detailed scheduling problem under the assumption that the upper-level refining plan is known.The details of the research are as follows:（1）Crude oil processing short-term production planning is a multi-objective optimization problem.By establishing a multi-objective optimization model for this problem,different multi-objective evolutionary algorithms are used to optimize the different costs generated in the entire scheduling process.The cost includes the number of oil charging tanks,the switching times of oil charging tanks,the cost of mixing crude oil in pipelines,the cost of mixing oil charging tanks at the bottom of the tank,and the cost of energy consumption.To improve the algorithm solution performance,based on the traditional SPEA2 algorithm,an extreme point-based ESPEA2 algorithm is proposed.The concept of extreme value archive set is introduced into the algorithm,combined with the idea of MOGWO algorithm to guide the update of extreme value archive set,which further improves the global search ability of the algorithm.Finally,the proposed ESPEA2 algorithm is applied to a refinery production example,and several representative multi-objective optimization algorithms are compared to verify the superiority of the proposed algorithm.（2）To meet production requirements and reduce costs,and further consider the situation of crude oil blending demand scheduling.In the case of mixing,the volume of crude oil per transfer changes dynamically according to the mixing ratio.This paper analyzes multiple constraints in the process of crude oil scheduling to ensure the safety of the system when crude oil is transported.Considering the demand for crude oil blending,in order to optimize the five cost objectives mentioned above,this paper proposes a multi-objective optimization model corresponding to this problem.Then,the modified model is solved by using multiple multiobjective optimization algorithms.The selection method of the standard NSGA-II algorithm will lead to the existence of more duplicate individuals in the solution process and reduce the population diversity.This paper proposes the INSGA-II algorithm,in which a new individual selection method is proposed,and a calculation formula specially used to select the dominant individual is designed.This selection method improves the diversity of the population by increasing the selection probability of the dominant individual while ensuring the selection of better individuals,and improves the defect of poor population diversity in the optimization problem of the standard NSGA-Ⅱ algorithm.