Parameter Optimization Method Of Polyester Fiber Production Process Based On Industrial Internet Of Things Service

Industrial Internet of Things（IIo T）service system is an integrated man-machine service system,which collects,transmits,processes and stores the information of equipment and field environment through edge computing gateway,intelligent data acquisition equipment and intelligent sensors.It aims at enterprise strategic competition,improving efficiency,and supports high-level decision-making,middle-level management and grass-roots operation.It helps enterprises to carry out equipment management and establish an information-based management system.The industrial Internet of things service system not only reduces the work of enterprise managers,but also improves the operation efficiency of enterprise work.From the customer’s point of view,the access to machine productivity visualization data creates a new level of awareness for the stakeholders involved in the supply chain.From the perspective of equipment manufacturers,through real-time access to the internal condition data of the machine,equipment manufacturers can help customers shift from regular preventive maintenance to condition based predictive maintenance.The industrial Internet of things service system can be applied to fiber production,refrigeration industry,steel industry,etc.Polyester fiber production process is composed of many links,which involves a large number of chemical and physical changes.It is an industrial process with high complexity.Different industrial production environments will cause differences in the quality of fiber products.How to make the quality of fiber products meet the needs of customers and realize the personalized customization of products and the switching between batch products has become an urgent technical difficulty in the fiber industry.The introduction of intelligent control and collaborative optimization in industrial process will help to reduce the cumulative error in the production process and obtain higher quality products.Understanding the changes in industrial processes through the interpretation of biological molecules,cells and systems can effectively improve process efficiency and data mining ability.In this thesis,through the in-depth study of the overall production process of polyester fiber（polymerization process-melt transport process-spinning process）,intelligent modeling,control and optimization ideas are introduced to further improve the stability and anti-interference of the control system,and realize the overall process optimization of the whole process.The main contents are as follows:（1）An immune algorithm of endocrine regulation is proposed to perform heterogeneous data calibration.First,the polyester fiber production process data are processed and analyzed,and then the database model is integrated into the expert system for online connection with the production line.Then,the real-time production data is calibrated synchronously,the database cluster is updated,and the parameters of the spinning production line are configured uniformly.Finally,the operation of the production line is analyzed,and the forward or reverse error compensation is carried out for the fiber production process,so as to adjust the collected process parameters in real time,optimize the production process and improve the production quality of polyester fiber.（2）A service optimization method for polyester fiber production process is proposed.According to the production batch and production specifications,the method considers the service cost as the optimization objective,and uses data model to determine the specific process parameters in the polyester fiber production process.First,two options for the overall process of polyester fiber are introduced: on-demand manufacturing and product development.Second,the impact of different batch request tasks on the performance index of each stage is determined.Finally,the service optimization measures of different batches are proposed.By comparing the similarity between the current data samples and the overall data,the optimal production plan of the overall production process is formed.Simulation results show that the immune algorithm inspired from endocrine regulation（AIE）has the best performance on the optimal decisionmaking combination,which is helpful for the development of new polyester products.We investigate how to reduce energy consumption of system resources,and how to choose the best service from a large number of candidate services.In the overall polyester fiber production process,users are not only consumers,but also designers and producers,achieving the real "integration of production and consumption".（3）Inspired by the collaborative mechanism among biological nervous,endocrine and immune systems,this paper proposes a multi-objective optimization method based on an adaptive evolutionary algorithm.This method can solve the dynamic multi-objective optimization problem of Internet of Things（Io T）services to reduce the total service cost and service time.The adaptive evolutionary algorithm based on the intelligent model of biological mechanism cooperation（BCAE）is divided into its bottom level and high level.In the bottom level,different Pareto frontiers are obtained by coevolution of multiple subpopulations.Based on the solution obtained from the bottom level,multifactorial evolutionary algorithm（MFEA-II）is used at the high level to further increase the diversity of solutions.The communication between different skill groups is realized by random mating probability（rmp）of online learning and the offspring’s imitation of parents’ skills,so that genetic material can be transferred between different skill groups.On the basis of single service strategy and collaborative service strategy,the industrial Io T services with dynamic requests are studied under different tasks.The obtained simulation results show that the performance of BCAE is better than the performance of the four existing algorithms,especially when solving high-dimensional problems.（4）Inspired by immune endocrine system,a biological comprehensive optimization algorithm（BCOA）based on industrial Internet of Things is proposed to solve the industrial Internet of Things service evaluation.The BCOA algorithm is arranged into two levels.The bottom level improving global optimization immune algorithm（GOIA）produces optimal similarity.The top level adopts the cooperative parallel genetic algorithm based on coarsegrained model（CGPGA）to increase the diversity of antibody population.First,the optimal individuals of each subpopulation are stored according to the adaptive coefficient,and then the search space of each subpopulation is reduced by using the number of optimal individuals.When the search space accuracy reaches a certain threshold,the optimal solution is obtained.The relationship between top level and the bottom level is established by transferring the optimal similarity.Simulation results show that BCOA algorithm can find the optimal solution faster due to the reduction of search space,and get the closest CV% value of the average physical property index,thus production management achieves overall optimization,and the best performance in terms of total service time mean and standard deviation.A set of evaluation framework mechanism,which uses the information and data provided by historical requestor and service projects in industrial Internet of Things evaluation to help provide more perfect evaluation information.（5）A polyester fiber production process parameter optimization system is designed based on industrial Internet of Things service.Firstly,the system calls the production batch and production specification in the historical database according to the performance index of the target product to determine the initial value of the process parameters in the production process of the product,and then uses the immune algorithm based on endocrine regulation to adjust the initial value of the input process parameters according to the process parameter values of the target product collected in real time.By modifying process parameters online,customized requirements can be met and deep integration of products and information can be realized.This method will be further applied to analysis of energy saving and consumption reduction in a short time under the premise of diversified demand and competitive differentiation of enterprises.Finally,the research content of this paper is summarized and prospected,and the shortcomings in the research are pointed out,and the follow-up research content and related work are prospected.