Automated Real-Time Monitoring of the Variables of the Flue Gas
The first component of intelligent operations is automated, real-time, and accurate measurements of the variables of the flue gas. In Guoshun Group's flue gas treatment systems, there is a complete set of high-precision, real-time sensors for each of the variables and pollutants, as well as the temperature, pressure, and flow rate. All the sensors communicate with the supervisory and control system electronically, so that the operators are steadily aware of the changes in the treatment gas. In the event of an abrupt rise in the concentration of sulfur dioxide in the treatment gas, the system will trigger an alarm and automatically take the appropriate control actions. Therefore, real-time monitoring systems provide a basis for optimizing the efficiency of f purge gas treatment.
Automatic Modifications of Treatment Processes
Guoshun Groups smart technology makes automatic modifications to flue gas treatment processes using the latest real-time monitoring data. Their system employs AI to analyze plume gas parameters and optimally adjust the spraying of reagents, and the activity of catalysts. Unlike manual operational adjustments, which can lead to delays and mistakes, intelligent systems can make automatic adjustments in milliseconds. An example of this is in the desulfurization process where systems make real-time adjustments of absorbent supply to sulfur content in plume gas. This ensures stable and efficient desulfurization, while minimizing waste of reagents. Such a system, with the ability to make automatic modifications, enhances the versatility of the flue gas treatment system, even in complicated operating environments.
Predictive Maintenance That Lowers Downtime
The efficiency of flue gas treatment is greatly impacted by downtime that stem from equipment breakdowns. Guoshun Group’s intelligent operation system includes predictive maintenance features that assess the functioning data of core components like pumps, fans, and filters. The system develops a predictive failure model to recognize potential issues and alert users for maintenance. For example, if a filter bag is nearing the end of its service life, the system will prompt a replacement to avoid efficiency loss from a damaged bag. With this predictive maintenance, unplanned downtime is greatly reduced, and the flue gas treatment equipment operates with maximum efficiency and uninterrupted.
Optimizing Processes Through Digital Simulations
Before Guoshun Group begins a new system operation, they utilize digital simulation technology to fine-tune flue gas treatment processes. The team creates a digital twin model of the flue gas treatment system to simulate various flue gas flow scenarios and reactions under diverse operational conditions. By conducting numerous simulations, the team successfully identifies and fine-tunes the optimal operational parameters and avoids inefficiencies that often arise from onsite debugging. After a system begins operation, the digital model is updated continually based on the actual operational data to enable dynamic optimization. The integration of digital simulations with real-time operational data is what sets Guoshun Group apart. The treatment and operational efficiency of their flue gas systems is far superior to that of conventional systems.
From anywhere around the globe, the intelligent flue gas treatment system from Guoshun Group can facilitate remote management and control. Supervisory personnel can monitor and manage the system via mobile applications and computer interfaces without needing to be physically present. Moreover, for businesses with several production lines or branches, a unified platform can manage all flue gas treatment systems. This approach minimizes operational costs while adhering to standardized procedures. For instance, a steel company with three plants employs Guoshun Group’s centralized management platform to make uniform adjustments to the flue gas treatment parameters. This coordinated approach enhanced the overall treatment efficiency by 15%.
