АПАРАТНО-ПРОГРАМНИЙ МОДУЛЬ ІНТЕЛЕКТУАЛЬНОГО КЕРУВАННЯ МІКРОКЛІМАТОМ У ВИРОБНИЧИХ ПРИМІЩЕННЯХ

The article addresses the problem of automated and intelligent microclimate control in industrial premises, where the stability of the temperature and humidity regime directly affects the efficiency of technological processes, energy efficiency, and equipment reliability. It is shown that traditional control systems based on simplified linear models do not provide the required accuracy under conditions of multifactor disturbances and dynamic changes in environmental parameters. The purpose of the study is to develop a hardware–software module capable of providing adaptive, stable, and energy-efficient control of microclimate parameters based on the coordinated operation of sensor, computational, and executive subsystems. The research methodology involves the development of a structural architecture of the hardware–software module, integration of a sensor system, a controller with embedded HMI, and executive mechanisms, as well as the formation of algorithmic logic for regulating temperature, humidity, air exchange, and internal pressure. The study applies methods of analysis and synthesis, mathematical modeling, and experimental testing under real industrial conditions. The module operation was verified through continuous data acquisition, real-time parameter logging, and evaluation of the response of executive mechanisms to changes in external and internal factors. The test results confirmed the module’s ability to maintain stable microclimate parameters within specified setpoints, ensuring smooth switching between heating, cooling, and ventilation modes. The recorded temperature dynamics demonstrate the absence of sharp fluctuations, effective operation of hysteresis mechanisms, and rapid system response to load changes. The practical applicability of the module is confirmed by its stable operation under daily variations in outdoor temperature, the presence of thermal disturbances, and variations in air exchange. The selected combination of equipment (KSP-08.L controller, data acquisition modules, and sensor devices) provided the required performance, accuracy, and flexibility. Prospects for further research include expanding the functionality of the hardware–software module through the integration of predictive models, in particular neural network structures of the NNARX type, which will increase the accuracy of microclimate dynamics assessment and optimize control logic in complex industrial scenarios. The obtained results form a basis for improving intelligent control systems in industrial cyber-physical complexes and for their implementation in various industrial sectors.