Research On Key Technology For Predictive Operation And Maintenance Of Recirculating Cooling Water System

Recirculating cooling water system is a system which uses water as the cooling process medium and is recycled.Water quality monitoring is the core of operation and maintenance work.In the context of automation control as the main mode of operation and maintenance,improving the ability to maintain stable water quality,saving water resources,and reducing maintenance costs are the development direction of system operation and maintenance work.Therefore,this dissertation conducted optimization work on the operation and maintenance of the recirculating cooling water system for the central air conditioning of subway platforms,and proposes an operation and maintenance plan based on digital twin technology.Based on the actual monitoring of operation and maintenance data,the construction of digital twin models for various operation and maintenance elements was studied,and the state prediction of each operation and maintenance element was achieved.An operation and maintenance software platform was developed.This dissertation firstly clarifiled the optimization requirements for operation and maintenance work based on the problem existing in the operation of the recirculating cooling water system.Then,according to the characteristics of the operation and maintenance data,a digital twin model of each element of the operation and maintenance is proposed based on the Long short-term memory(LSTM)neural network as the main method,and then the overall structure of the operation and maintenance of the recirculating cooling water system with the digital twin model as the core is designed,and the composition and function of the architecture are described.Then,according to the correlation of operation and maintenance data,single factor and multi factors LSTM neural network prediction models for p H,conductivity,oxidation reduction potential(ORP),fluorescent tracer and sewage volume were constructed.The optimal structure and hyperparameter of each model were selected through experiments.By comparing with the prediction results of Gated Recurrent Unit(GRU)neural network model,the accuracy and effectiveness of the method were verified.Finally,the model was deployed on the developed software platform and the visualization of operation and maintenance data was achieved through a human-machine interaction interface,verifying the feasibility of the proposed solution.The construction methods of various operation and maintenance element models proposed in this dissertation effectively reduce the feature requirements of neural network models,and can be well applied to the state prediction of virtual and real combination under the digital twin mechanism.The developed software platform provides prediction methods for dynamically changing water quality status,provides guidance for system dosing and pollution control,and improves the reliability and economic benefits of the operation of the recirculating cooling water system.