基于物联网云平台的矿热炉电极运行监控系统设计

为了远程监控矿热炉三相电极的电压、电流、移动距离等冶炼参数,对电极运行数据进行深入分析,及时对设备故障进行报警,满足安全生产、节能降耗的需要,设计实现了一种基于物联网云平台(WISE-PaaS云平台)的矿热炉电极运行监控系统。首先在现场底层使用ADAM 5510M作为控制器,结合ADAM系列模块、罗氏线圈、磁阻传感器、编码器等设备,实现对矿热炉电极运行数据的采集,并在网际组态软件WebAccess上建立工程节点和设定MQTT协议,将采集数据上传至云端。通过WISE-PaaS云平台中的Dashboard、SaaS Composer、APM、AFS等功能,整合矿热炉电极运行数据,采用云计算技术,开发了电极运行实时监控、电极状态报警、电极具体参数显示、移动终端人机交互、能耗预测、电极位置预测等一系列功能。实验结果表明,设计的监控系统实现了对矿热炉电极运行状态的实时监测和故障报警,为预测性维护和诊断提供数据支撑,具有较好的实际应用潜力。

Design of Electrode Operation Monitoring System Based on IoT Cloud Platform of Submerged Arc Furnace

In order to remotely monitor the voltage, current, displacement and other smelting parameters of the three-phase electrodes of the submerged arc furnace, deeply analyse electrode operation data, timely alarm equipment failures, meet the needs of safety production, energy saving and consumption reduction, an electrode operation monitoring system based on IoT cloud platform (WISE-PaaS cloud platform) was designed and realized in the paper. First of all, ADAM 5510M was used as the controller on the ground floor, combined with ADAM series modules, Rogowski coils, magnetoresistive sensors, encoders and other equipments to realize the collection of the operation data of the submerged arc furnace electrodes. After establishing the project node and setting the MQTT protocol on the Internet configuration software WebAccess, the collected data were uploaded to the cloud. Through Dashboard, Composer, APM, AFS and other functions in the WISE-PaaS cloud platform, integrating the operation data of the submerged arc furnace electrodes, adopting cloud computing technology, a series of functions including real-time monitoring of electrode operation, electrode status alarming, electrode specific parameter displaying, mobile terminal human-computer interaction, energy consumption prediction, electrode position prediction, etc, were developed. The experimental results show that the monitoring system designed in this paper realizes real-time monitoring and fault alarming for the operation state of the submerged arc furnace electrodes, providing data support for predictive maintenance and diagnosis, which has great potential for practical application.