Intelligent Management

By establishing an intelligent control model for a single chlorine distillation column, the process can quickly reach optimal energy consumption conditions, resulting in improved stability and enhanced energy-saving performance.

Image recognition (AOI) combined with multiple equipment and processes within the facility allows us to monitor and alert operators to ensure a safe working environment.

By utilizing intelligent models to provide optimal set points (SP) and applying model-based optimization control, the process can quickly reach optimal energy consumption conditions. This enhances process stability and achieves energy-saving benefits, and has been implemented in the unit chlorine distillation column.

Establish a model to monitor heat exchanger efficiency and provide recommendations on when to switch to backup units, thereby reducing energy consumption and lowering carbon emissions.

Introduction of the intelligent image recognition for smart weighing scales and tank truck loading, we can monitor and alert operators in the workflow, creating a safe operating environment.

By introducing the situation room and production management system at the Toufen main factory, most information can be monitored in real time to establish production management indicators. Based on the needs of each unit, various indicators are listed, relevant variables and corresponding management countermeasures are formulated, and intelligent functions are integrated to optimize production management.

By using intelligent models to identify the optimal set points (SP) from past operations and to automatically calculate real-time optimal PID parameters through control loop optimization, the system can automatically assign process conditions and PID parameters via programming. This enhances process stability and reduces steam consumption per unit, achieving energy-saving benefits. This approach has been implemented in the PVC dryer.

A license plate recognition system has been introduced to manage and control the entry and exit of vehicles within the plant.

Through industry-academia collaboration, intelligentization was introduced to a single chlorine distillation column. By utilizing intelligent models to provide optimal set points (SP) and applying model-based optimization control, the process can quickly reach optimal energy consumption conditions, enhancing stability and achieving energy-saving benefits. The solution was then replicated and applied in-house to three chlorine distillation columns.

Based on historical operating data, models are established to define corresponding normal operating ranges and control boundaries. Process equipment is monitored, and alarms are triggered in case of abnormalities, enabling personnel to take preventive actions in advance and maintain control. This helps reduce process and safety anomalies and also lowers energy consumption.

An AOI detection and alarm system has been established in the second and third tank truck loading/unloading areas. Using image recognition technology, the system monitors the safety behavior of operators and provides warnings. Infrared cameras are also used to detect gas leaks and abnormal temperatures, creating a safer working environment.

By using intelligent models to identify optimal process conditions from historical data and to optimize control loops for the best PID parameters, the process can maintain stable production and quality while reducing steam consumption per unit in the evaporation tank.

A tank truck unloading image recognition system has been implemented to monitor operator procedures during unloading operations. The system detects any deviation from the SOP and issues real-time alerts,helping to establish standardized workflows and a safe working environment.

A smoke detection system has been introduced to monitor chimney emissions within the plant. It checks whether emissions are occurring and whether the smoke color is within the appropriate range. In case of improper emissions, management personnel are notified immediately to coordinate with production units for early response and resolution.

New CCTV cameras have been installed, and electronic fences have been added to existing cameras in the plant to monitor personnel movements in high-risk operations or restricted access areas. This ensures that operating zones remain clear or that personnel are present at their designated positions.

By collecting long-term vibration data from critical equipment, maintenance schedules can be predicted in advance. Anomaly detection models are established for real-time comparison and monitoring, with unified display screens showing monitoring data. Personnel can view equipment status directly through the monitoring interface, reducing the workload of on-site inspections and the cost of performing maintenance on a fixed schedule. Model-based diagnostics help reduce the occurrence of abnormalities, enhance energy efficiency, and improve industrial safety.

In the future, with the introduction of intelligent systems and smart learning control, AIoT technology will bring new vitality to traditional industries. In terms of hardware and software, technological integration enables intelligent monitoring and automated control, effectively improving production efficiency. Through energy management systems, energy use can be maximized while minimizing environmental impact. For safety enhancement, intelligent monitoring systems provide real-time hazard detection and offer rapid and accurate emergency responses.

The upgrade to smart manufacturing not only strengthens hardware-software integration and enhances safety protection, but also accelerates the transformation of traditional industries toward a low-carbon future, supporting energy conservation and carbon reduction goals and contributing to environmental sustainability.