Optimization Of Resin Impurity Removal Temperratuer Control System

In the coal fields of the Midwest of Inner Mongolia,the north of Shanxi,and Ningxia,there are abundant high-alumina coal resources.The alumina content of the fly ash produced by this kind of high-alumina coal combustion,which is close to that of the low-grade bauxite in China,has great potential for development.Z alumina factory dissolves the available components in the fly ash by the acid method,and then undergoes filtration,separation and purification to obtain alumina and a series of by-products associated with the coal resources.What this article studies is one of the ion separation processes-resin removal system.After the raw materials go through the upper-level process flow,and each standard meets the requirements of entering the resin impurity removal system,the raw material tank enters the ion exchange resin system,and the iron ions in the raw materials are adsorbed and separated by the resin so as to achieve the final iron content in the finished alumina product reaching the factory standard.In this paper,a brief introduction to the process and control process of the resin removal system currently used in the Z alumina factory is presented.The current several control principles and the problems encountered in the production of the existing resin removal system have been elaborated and analyzed in depth.Secondly,Add a temperature control system to optimize the design of the current problems in the resin removal system,which is achieved and controlled through the configuration software,with the hardware selection and addition,and the software designed and edited.Finally,the optimization of the entire resin impurity removal system was completed,and analyze the correlation between control items and system impactfactors by data analysis software.And do the simulation experiments to compare the performance of the two temperature control methods by MATLAB.Through the discussion of the experimental results,it is found that the modelless adaptive control scheme with neural network compensation tried is stable and only had small error to the PLC control system which was designed.