基于DCS和工艺专家系统的压滤机组智能协调控制系统设计

    Design of an intelligent coordinated control system for filter press units based on DCS and process expert system

    • 摘要: 为解决选煤厂压滤机组现有协调控制方案难以兼顾改造难度、系统可靠性与协同水平的问题,提出一种基于DCS和工艺专家系统的压滤机组智能协调控制系统。该系统由执行机构、检测装置、核心设备、控制优化单元组成,采用Modbus TCP通信协议实现DCS与PLC的实时通信,依托DCS和工艺专家系统实现压滤机组各流程的控制逻辑设计。应用表明:该系统在乌海能源利民选煤厂投运后,单台压滤机综合工艺用时累计节省1283 s,单次生产任务平均节省3.2 h,生产效率提升31.6%,每班可减少岗位操作人员2人,单位时间生产效率提升15%,实现了压滤机组工艺参数的智能优化与全流程协调控制。基于DCS和工艺专家系统的压滤机组智能协调控制系统有效提升了压滤机组的自动化水平与生产效率,减少了人工干预,为选煤厂压滤机组的智能化协调控制提供了高可靠、易实施的技术路径,同时丰富了选煤设备智能协同控制的研究体系。

       

      Abstract: To address the difficulty of balancing retrofitting complexity, system reliability, and coordination levels in existing control schemes for filter press units in coal preparation plants, an intelligent coordinated control system for filter press units based on DCS (Distributed Control System) and a process expert system is proposed. The system consists of actuators, detection devices, core equipment, and control optimization units. It utilizes the Modbus TCP communication protocol to achieve real-time communication between the DCS and PLC, and relies on the DCS and the process expert system to implement the control logic design for each stage of the filter press unit process. Practical application results indicate that after the system was commissioned at the Limin Coal Preparation Plant of Wuhai Energy, the cumulative comprehensive process time of a single filter press was reduced by 1283 s, saving an average of 3.2 h per production task. Production efficiency increased by 31.6%, the number of operators per shift was reduced by two, and production efficiency per unit time improved by 15%. The system has achieved intelligent optimization of process parameters and full-process coordinated control for filter press units. The proposed system effectively enhances the automation level and production efficiency of filter press units, reduces manual intervention, provides a highly reliable and easy-to-implement technical path for the intelligent coordinated control of filter press units in coal preparation plants, and enriches the research framework for the intelligent collaborative control of coal preparation equipment.

       

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