Advanced model predictive control strategies for energy-efficient HVAC systems in pharmaceutical facilitiesTomažič, Simon (Avtor) Škrjanc, Igor (Avtor) model predictive controlenergy efficiencypharmaceutical cleanroomsdigital twinpredictive functional controlparticle swarm optimisationfuzzy clusteringHVAC systemsmultivariable controlpharmaceutical manufacturingThe paper presents a comprehensive study of advanced Model Predictive Control (MPC) strategies for pharmaceutical HVAC systems, subject to stringent regulatory requirements and substantial energy demands. Three control approaches–Predictive Functional Control (PFC), nonlinear MPC with Particle Swarm Optimisation (PSO) and energy-efficient MPC (EMPC)–are examined. Their ability to maintain strict temperature and humidity setpoints, reduce energy consumption and deal with system nonlinearities is evaluated. Data recorded over 445 days of HVAC operation, capturing variations in external temperature and humidity, underpins the assessment. The results demonstrate that EMPC can reduce total energy consumption by up to 20 %, while PFC offers a simpler implementation well-suited for industrial control systems. The results highlight the key trade-offs between control accuracy, computational complexity, and energy savings and provide a practical framework for the adoption of MPC-based solutions in pharmaceutical HVAC environments.20252025-09-05 13:08:33Članek v reviji172094UDK: 681.5ISSN pri članku: 1872-6178DOI: 10.1016/j.enbuild.2025.116348COBISS_ID: 247882243sl