| The energy consumption in the steel production process is huge.In order to promote the high-quality development of the steel industry,improving the intelligence level of each process is an important way to achieve energy saving and consumption reduction.Combining nternet of Things and cloud platform technologies with the iron and steel industry can realize intelligent monitoring,prediction and analysis of energy consumption in iron and steel processes,and lay the foundation for the subsequent formulation of energy-saving and emission-reduction strategies.The data acquisition terminal is composed of sensor nodes and controller nodes.The intelligent gateway uses Modbus protocol to communicate with the data acquisition terminal equipment,and uses the MQTT protocol to upload data to the cloud platform;the cloud platform is constructed by Alibaba Cloud server and cloud database,and the backend The back-end Web application is designed using the Express framework,and the front-end part uses Admin LTE,Ajax and Echarts technology to design the humancomputer interaction interface to realize real-time monitoring of energy consumption.The energy consumption prediction model is established by support vector machine,multiple linear regression and Gaussian process regression algorithm,and the consumption of blast furnace gas in the steelmaking plant is predicted according to the correlation analysis table,and the four indicators of R2,RMES,MAE and standard deviation are used to evaluate The prediction accuracy of the three models.The results show that the R2 of the Gaussian process regression is 0.83,the REMS is 0.81,the MAE is 0.85,and the SD is 0.90,and the evaluation indicators are the best among the three models.The test shows that the energy consumption prediction and monitoring system works stably,realizes the functions of remote monitoring,prediction and energy consumption statistics of energy medium consumption.Figure 48;Table 19;Reference 79... |
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