Development Of Intelligent Experiment Management System For Twin Screw Pelletizing Unit

In recent years,technologies such as the Internet,digital communication,and artificial intelligence have accelerated the construction of informatization and intelligent laboratories in colleges and universities.Among them,the rapid development of technologies such as the Internet of Things,data collection,and mobile communication has shined brightly in the process of informatization transformation of traditional laboratories,providing a solution for the informatization management of laboratories.In this paper,based on the Internet of Things technology,the traditional twin-screw extrusion and granulation unit in the laboratory is transformed into information,and the supporting experiment management system is developed using cloud platform,Node.js,Android and other technologies.On this basis,the related research on the energy consumption distribution of the unit and the data flow fault diagnosis analysis are also carried out.(1)The information-based upgrade of the unit is carried out through various sensors and data transmission modules such as current transmitter,voltage transmitter,intelligent temperature control instrument,analog acquisition module,485 hub,and PLC-based data is built.Acquisition System.In addition,the application of gateway,cloud platform and other technologies realizes cloud storage of operating data such as the temperature of the unit barrel,motor current and voltage,and the on-off status of solenoid valves.(2)Based on the data stored in the acquisition system,the energy consumption distribution in the preheating stage and the normal extrusion process of the unit is studied.In the preheating link,the relationship between the set temperature of the barrel and the energy consumption of the unit and the influence of the laboratory environment on the energy consumption of the barrel heating are mainly studied.For the normal extrusion process,the influence of three process parameters on the energy consumption distribution of the unit is mainly studied.(3)Characterize 8 common faults during the use of the unit by collecting data streams,mainly including main motor current overload,abnormal heating and cooling system which including the failure of insufficient heating capacity of the barrel,the failure of insufficient cooling capacity of the barrel,and the failure of excessive cooling capacity of the barrel,the feeding system is abnormal and the current of the pelletizing motor is overloaded.In addition,different diagnostic procedures are designed for various faults,and specific maintenance suggestions for corresponding faults are sorted out in the expert system.(4)Developed a Node.js-based switching guidance system and device management system in Android and cloud server mode.The power-on-off guidance system mainly realizes the functions of the power-on-off operation of the unit and the monitoring of the process of the unit’s preheating process.The equipment management system mainly realizes the functions of remote monitoring of the unit,fault alarm and diagnosis,energy consumption distribution calculation,and user registration and login.