INTELLIGENT ENERGY-EFFICIENT MICROCLIMATE CONTROL SYSTEM FOR OPERATING ROOMS IN HEALTHCARE FACILITIES UNDER LIMITED ENERGY SUPPLY

Abstract

The paper presents the concept and mathematical framework of an intelligent energy-efficient microclimate control system for operating rooms in healthcare facilities under conditions of limited energy supply—an urgent issue for Ukrainian hospitals during wartime, caused by systematic damage to energy infrastructure, rolling blackouts, and the need for uninterrupted operation of critical surgical units. It is substantiated that traditional approaches to controlling heating, ventilation, and air conditioning (HVAC) systems, focused on maintaining fixed parameters around the clock, do not meet modern challenges and require fundamental reconsideration, taking into account the dynamics of the surgical cycle, variable room occupancy, and instability of energy supply.

A multi-level system architecture is proposed, which includes: IoT-based monitoring of air environment parameters (temperature, relative humidity, air velocity, CO₂ and PM2.5 concentrations) using a distributed sensor network; a fuzzy controller based on PMV/PPD indices that ensures adaptive control of climatic parameters considering the nonlinearity of heat exchange processes and subjective thermal comfort perception by medical staff; an energy priority manager for operation under limited power conditions with hierarchical ranking of consumers based on clinical criticality; and a multi-criteria optimizer based on the NSGA-II genetic algorithm, which minimizes electricity consumption while ensuring compliance with sanitary and hygienic requirements according to DBN B.2.2-10 and ASHRAE 170, as well as minimizing the risk of surgical site infections (SSI) through control of air exchange rates and aerosol particle concentrations.

A mathematical model has been developed that describes the relationship between microclimate parameters, HVAC system energy consumption, and an integrated epidemiological risk indicator, with constraints formalized as membership functions for fuzzy inference. Mathematical modeling and computational experiments based on a real pilot facility in the Vinnytsia region demonstrate that the implementation of the proposed system ensures a reduction in HVAC energy consumption by 38–62% compared to the traditional 24/7 operation mode, while maintaining PMV within ±0.5 for at least 95% of operating time and meeting regulatory requirements for bacterial air contamination. The obtained results have practical significance for the design and modernization of hospital engineering systems in the context of Ukraine’s critical infrastructure recovery.