Research On Modeling Simulation And Capacity Optimization Of Plate-fin Heat-exchanger WorkShop Production Logistics System Based On Modelica

As one of the important accessories of manufacturing equipment,heat exchangers are getting more and more orders under the background of global warming.How to optimize production lines and increase production capacity at low cost has become a top priority for major enterprises.On the other hand,the heat exchanger production logistics system,as a typical discrete event system,has a complex dynamic scheduling process.It is difficult to determine the actual production speed of its multiple production lines,which often leads to optimization difficulties.Therefore,this paper starts the modeling and optimization research of the heat exchanger production logistics system from the perspective of simulation.The main work of this paper is as follows:First,according to the basic characteristics of the heat exchanger production logistics system,the characteristics of the component model are extracted,and the system is divided into multiple types of components according to functions and derivation relationships.The applicability of finite state machine modeling to the heat exchanger production logistics system is analyzed,and the way of signal transmission is determined as the interaction mechanism between components.Based on Modelica language and finite state machine theory,various components are modeled and encapsulated.Reusable and extensible component models are constructed with resource scheduling component models as the core,which can support multiple resource scheduling strategies.Secondly,taking a company’s plate-fin heat exchanger production logistics system as the research object,using the production logistics system component model to build a simulation model of the system,according to the simulation results,it is found that the main reason for the increase in production capacity of the system is the difference between the feeding strategy and the resource scheduling strategy.reasonable.Therefore,this paper compares and analyzes the impact of different resource scheduling strategies and feeding strategies on the simulation results from the perspective of system capacity and production bottlenecks.The results show that using the number of non-responses as the basis for judging the priority of resource requests has the best effect on improving the production bottleneck.The feed interval difference between the two production lines increases,and the system capacity first increases and then decreases.Finally,the system capacity maximization and production equalization are taken as optimization goals to establish an optimization model.In response to the problems that occurred in the case simulation process,optimization measures were designed in the layout of workshop facilities,and particle swarm optimization was used to optimize resource scheduling strategies and feeding strategies.In the optimization process,in view of the shortcomings of the particle swarm algorithm falling into the local optimum prematurely,the concepts of similarity and mutation are introduced to improve the particle swarm algorithm without changing the complexity.The optimization results show that the monthly production capacity of the system has increased by 10.2%,which verifies the feasibility of the optimization scheme.