Control of boiler equipments during starting and stopping periods and methods of optimizing these processes through the use of a decision support system with a machine vision subsystem

The object of research is the automation of starting and stopping steam power boilers. The problem of automating the starting and stopping of steam power boilers is important for thermal power plants (TPP) and industrial enterprises. These processes require significant efforts from service personnel due to their complexity, partial automation and the need to take into account the human factor. It is emphasized that full automation of starting and stopping steam boilers is economically impractical, since most of the time the boilers operate in continuous automatic operations and only a short time is allocated for periodic procedures, which are mostly performed manually. However, the significant impact of the human factor at critical stages of boiler operation requires the introduction of new technologies that can increase the efficiency and safety of such operations. The study outlines the main challenges associated with steam boiler control and proposes new approaches to solving these problems. It is noted that operators often perform actions during boiler starting and stopping based on instructions or their own experience. This knowledge can be formalized and integrated into the database of an expert decision support system (DSS), which automates some of the manual actions and helps operators avoid errors. For this purpose, it is proposed to use machine vision subsystems that can validate the operator's actions, analyze the interaction of personnel with the equipment and signal about possible incorrect actions. This approach can not only reduce the risk of errors due to fatigue or stress of personnel, but also make the starting and stopping processes safer and more efficient. It is proposed to integrate machine vision subsystems to obtain information that is difficult to measure by traditional means, in particular, regarding the operator's interaction with manual mechanisms or its presence at the workplace. The structure of the proposed DSS also takes into account the possibility of transferring knowledge bases between different objects, which ensures the scalability and adaptability of the system. The implementation of such a system is based on modern international automation standards, in particular ISA-88, ISA-106 and VDI/VDE/VDMA 2632.