Dynamic Optimization And Scheduling Of Refining Process

Refinery production enterprise is a typical representative of process industry, which meets the growing pressure of improving profits and reducing cost from market competition. As the intermediate links of a Computer Integrated Processing System (CIPS), refinery production scheduling is the core of enterprise information integration and task integration, as well as the core of business operations. Also, scheduling plays an important role in improving resource utilization, reducing energy consumption, saving costs, improving efficiency and enhancing competitiveness for the enterprises in current conditions. At present, the long transition process of refinery unit, results from changes in production, and is often overlooked in refining scheduling, which results in an extensive scheduling result. Therefore, it is important to research on dynamic optimization and scheduling of the refining process to improve the scheduling quality and business benefits.There are many salient features in the refining process, such as continuous, smooth, high energy consumption, installation of complex and long transition process, etc. And transition process will be brought on when the material switches, devices stop or interruption of raw materials, equipment failures and other unplanned occurrence of random events. This paper studies the device transition process caused by the planned events, and achieves organic convergence of scheduling and control through including the device transition process in the scheduling model. Generally speaking, it is difficult to globally optimize scheduling combined the easily experience of experts effectively when unforeseen events happen, which is also a research point in this paper.This article includes the following aspects:(1) It is pointed out that steady-state optimization is the point of combining scheduling with control after introducing the roles of scheduling. Current modeling and optimization methods of refinery production scheduling are compared and their research status is outlined in various refining process. The key issue of dynamic optimal scheduling is to combine mathematical programming, heuristic methods and artificial intelligence together to find a satisfactory balance point between optimization and real-time. The main problems in production scheduling modeling are pointed out after the characteristics of the refining process are analyzed.(2) The general form of the transition process model for scheduling is achieved based on the analysis of the transition process unit. Analysis shows that the transition process model can realize the different time-domain conversion between scheduling and control, optimize the control settings, get the dynamic yield that scheduling requires, and fully be able to meet the scheduling requirements of the model input and output. The transition process model of fluid catalytic cracking unit (FCC) is achieved based on the seven lumped model, whose validity is verified by an example. Part of the optimal control instruction has been obtained through the transition process model of FCC, which lays foundation for the combination between scheduling and control.(3) A long transition process often happens when a production activity is changed for oil refining unit. According to the characteristics of the transition process, the production scheduling model including the transition process is established as the goal of maximum profit, using continuous-time modeling approach. Through the switch variable settings, scheduling model will be simplified owing to a lot of restrictions in a stable state, which will not increase the complexity of the model. The simulation results show that the model can achieve a more refined scheduling and effectively improve the economic efficiency of enterprises. According to the model's characteristics, an improved two-stage PSO algorithm (TSPSO) is designed based on the improved strategy of inertia weight and acceleration coefficients. Simulation results show that the algorithm has a good stability and robustness ability in solving the TSP problem.(4) As it is difficult to acquire rules in dynamic scheduling, the rough set was applied to production scheduling based on its unique advantages in the knowledge acquisition. The process of acquiring production scheduling rules from a large number of data based on rough set is described and an example is given. A decision-making table decomposition method is proposed for the causal relationship between decision attribute, which solved the co-existence of multi-attribute in scheduling decision table. Applications for the rough set decision table attribute reduction problem, we introduced the reduction algorithm under instruction. Found the scheduling rules in the historical production data solve the problems of rule-based scheduling. Scheduling rules were summed up and concluded which were obtained from on-site research and production data, and illustrates the application of rules in dynamic scheduling.(5) Since the production process is fixed basically, and there are no frequent device switches, the order of material flow is generally fixed in the process industry. The mathematical programming, heuristic methods, and artificial intelligence are effectively integrated to design dynamic scheduling optimization system, based on the thought of process simulation and modern computer software technology. At a certain extent, the system can solve the problem of modeling for dynamic scheduling in oil refining industry, and ensure to obtain better optimization and satisfy the real-time requirement. According to the optimization model embedded in the software, the system ensures optimal scheduling on a viable basis, after determining the device choice and material sequence based on personnel experience. Dynamic scheduling optimization system has been developed into trial software in the Qilu Petrochemical refinery and received high praise.