Cleanroom environmental control systems: review of research landscape and control strategies

• Identify the overarching research landscape for energy efficiency in cleanrooms. • Summarize the control variables, objectives, control methods for cleanrooms. • Propose future directions for advanced controls, integrating AI technologies. • Airflow and contamination dominate studies; optimization and efficiency are rising. • Ventilation control is most common; air change rate is the key control variable. Cleanroom is an emerging and high-energy–density building type. The heating, ventilation and air conditioning (HVAC) systems of cleanrooms can consume up to 100 times more energy than those used in commercial spaces of the same size. This study presents a systematic literature review on the overall research landscape of HVAC systems in cleanrooms, analyzing trends, advances, and gaps in this area of research. In addition, the review provides an in-depth analysis of control strategies for cleanroom HVAC systems. A comprehensive examination of 235 papers was conducted to provide an overarching research landscape, with 55 papers specifically focused on control strategies selected for an in-depth review. The analysis reveals that contamination control consistently emerges as the predominant research topic in the field, while optimization and energy efficiency have gained increasing prominence in recent years, as evidenced by keyword evolution mapping. Semiconductor cleanrooms received more research attention compared to other cleanroom applications. Among the various control strategies utilized in cleanroom HVAC systems, ventilation controls are the most widely studied, with air change rate serving as the most frequent control variable. Ventilation controls, such as demand flow control, exhibit a higher energy saving rate. Cleanroom control strategies remain highly conservative, with the majority utilizing rule-based control, and only 23.6% of studies focusing on optimization-based approaches. Nearly half of the reviewed control-related works incorporated experimental evaluations to validate their proposed control methods. The application of advanced technologies in cleanrooms is still limited. Future research directions could emphasize investigating the potential of artificial intelligence, machine learning, and big data analytics to improve cleanroom performance and control.