Energy-efficient time-delay compensated ventilation control system for sustainable subway air quality management under various outdoor conditions

Abstract Modern subway transport has become the primary mode of travel for millions of commuters, while indoor air quality (IAQ) in subway stations is becoming a major public health concern. The IAQ management in subway stations is governed by a rule-based ventilation strategy, consisting on the introduction of fresh air from outside. However, this strategy does not consider the IAQ dynamics under time-variant outdoor conditions. Alongside, the sizeable inherent time-delay of the subway IAQ system limits the performance of feedback controllers in the ventilation system. Therefore, a time-delay compensated gain-scheduled (TDC-GS) ventilation system based on the Smith Predictor (SP) is proposed to address the time-delay of the IAQ process. This strategy was assessed for various outdoor air conditions. The performance of the control systems was analyzed under two case studies consisting of good, moderate and unhealthy outdoor air conditions. The results demonstrated that the proposed control system can maintain the IAQ at healthy levels in both cases. Under good and moderate outdoor conditions, the TDC-GS system achieved an energy savings of 10.33% compared to the rule-based ventilation system, representing an emission reduction of 132.9 kgCO2/day. Additionally, a more stable control response was achieved by the implementation of the SP technique. In comparison to the stand-alone GS control system, the errors were reduced up to 67%.

[1]  Shanjun Li,et al.  Does subway expansion improve air quality? , 2019, Journal of Environmental Economics and Management.

[2]  Tito L.M. Santos,et al.  Control of a class of second-order linear vibrating systems with time-delay: Smith predictor approach , 2018 .

[3]  Bin Xu,et al.  Air quality inside subway metro indoor environment worldwide: A review. , 2017, Environment international.

[4]  Chi Nyon Kim,et al.  Spatial distribution of particulate matter (PM10 and PM2.5) in Seoul Metropolitan Subway stations. , 2008, Journal of hazardous materials.

[5]  Richard D. Braatz,et al.  Indoor air quality control for improving passenger health in subway platforms using an outdoor air quality dependent ventilation system , 2015 .

[6]  Thomas F. Edgar,et al.  Process Dynamics and Control , 1989 .

[7]  Kwang Ho Lee,et al.  Cooling energy performance analysis depending on the economizer cycle control methods in an office building , 2016 .

[8]  ChangKyoo Yoo,et al.  Multi-objective optimization of indoor air quality control and energy consumption minimization in a subway ventilation system , 2013 .

[9]  R. Xie,et al.  The effect of traffic density on smog pollution: Evidence from Chinese cities , 2019, Technological Forecasting and Social Change.

[10]  A. Campbell,et al.  Inflammation, Neurodegenerative Diseases, and Environmental Exposures , 2004, Annals of the New York Academy of Sciences.

[11]  Jeong Tai Kim,et al.  Monitoring and prediction of indoor air quality (IAQ) in subway or metro systems using season dependent models , 2012 .

[12]  R. Burnett,et al.  Cardiovascular Mortality and Long-Term Exposure to Particulate Air Pollution: Epidemiological Evidence of General Pathophysiological Pathways of Disease , 2003, Circulation.

[13]  Wankeun Oh,et al.  Driving forces of rapid CO2 emissions growth: A case of Korea , 2015 .

[14]  Yun Li,et al.  PID control system analysis, design, and technology , 2005, IEEE Transactions on Control Systems Technology.

[15]  L. Morawska,et al.  Airborne particles in indoor environment of homes, schools, offices and aged care facilities: The main routes of exposure. , 2017, Environment international.

[16]  Kurt Straif,et al.  The carcinogenicity of outdoor air pollution. , 2013, The Lancet Oncology.

[17]  ChangKyoo Yoo,et al.  Soft sensor validation for monitoring and resilient control of sequential subway indoor air quality through memory-gated recurrent neural networks-based autoencoders , 2020 .

[18]  Jingjing Jiang,et al.  Peak of CO2 emissions in various sectors and provinces of China: Recent progress and avenues for further research , 2019, Renewable and Sustainable Energy Reviews.

[19]  ChangKyoo Yoo,et al.  Logistic Regression Based Multi-objective Optimization of IAQ Ventilation System Considering Healthy Risk and Ventilation Energy , 2014 .

[20]  D. R. Coughanowr,et al.  Process systems analysis and control , 1965 .

[21]  Alessandro Beghi,et al.  Control of Second Order Processes with Dead Time: the Predictive PID Solutions , 2018 .

[22]  Kamel Ghali,et al.  Increasing energy efficiency of displacement ventilation integrated with an evaporative-cooled ceiling for operation in hot humid climate , 2015 .

[23]  T. Moreno,et al.  Improving air quality in subway systems: An overview. , 2018, Environmental pollution.

[24]  Atul K. Jain,et al.  Global Carbon Budget 2016 , 2016 .

[25]  Ekezie Dan Dan,et al.  STATISTICAL ANALYSIS/METHODS OF DETECTING OUTLIERS IN A MULTIVARIATE DATA IN A REGRESSION ANALYSIS MODEL , 2013 .

[26]  Soon-Bark Kwon,et al.  Inhalation cancer risk from PM10 in the metropolitan subway stations in Korea , 2019, Journal of Transport & Health.

[27]  M. Minguillón,et al.  Factors controlling air quality in different European subway systems. , 2016, Environmental research.

[28]  Richard D. Braatz,et al.  Economical control of indoor air quality in underground metro station using an iterative dynamic programming-based ventilation system , 2016 .

[29]  Jin Wen,et al.  Review of building energy modeling for control and operation , 2014 .

[30]  G. Jasienska,et al.  Are we safe inside? Indoor air quality in relation to outdoor concentration of PM10 and PM2.5 and to characteristics of homes , 2019, Sustainable Cities and Society.

[31]  ChangKyoo Yoo,et al.  Gain scheduling based ventilation control with varying periodic indoor air quality (IAQ) dynamics for healthy IAQ and energy savings , 2017 .

[32]  Nora El-Gohary,et al.  A review of data-driven building energy consumption prediction studies , 2018 .

[33]  Duckshin Park,et al.  A multivariate study for characterizing particulate matter (PM(10), PM(2.5), and PM(1)) in Seoul metropolitan subway stations, Korea. , 2015, Journal of hazardous materials.

[34]  In-Beum Lee,et al.  Process Identification and PID Control , 2009 .

[35]  Mahmoud Gamal,et al.  Delay compensation using Smith predictor for wireless network control system , 2016 .

[36]  A. Saramak Comparative analysis of indoor and outdoor concentration of PM10 particulate matter on example of Cracow City Center , 2019, International Journal of Environmental Science and Technology.

[37]  Yi Cao,et al.  Smith predictor for slug control with large valve stroke time , 2017 .

[38]  ChangKyoo Yoo,et al.  A dynamic gain-scheduled ventilation control system for a subway station based on outdoor air quality conditions , 2018, Building and Environment.

[39]  ChangKyoo Yoo,et al.  Flexible real-time ventilation design in a subway station accommodating the various outdoor PM10 air quality from climate change variation , 2019, Building and Environment.