Performance evaluation and prediction analysis of a novel catalytic combustion heating technology in an open cold emergency environment / Qin Mingyuan

• Main Author: Qin , Mingyuan
• Format: Thesis
• Published: 2024
• Subjects: TJ Mechanical engineering and machinery
• Online Access: http://studentsrepo.um.edu.my/15384/2/Qin_Ming_Yuan.pdf; http://studentsrepo.um.edu.my/15384/1/Qin_Mingyuan.pdf; http://studentsrepo.um.edu.my/15384/
• Institution: Universiti Malaya

Providing comfortable heating solutions for people working, living, and engaging in activities in open, semi-open, and large cold spaces presents numerous challenges, including enhanced resistance to severe weather, the facilitation of efficient heat transfer, and minimizing energy consumption. Traditional indoor heating technologies are often impractical due to their heavy reliance on the power grid. Catalytic combustion technology, with low-temperature operation, high efficiency, and cleanliness, is seen as a solution to above challenges. Therefore, based on disaster-induced cold scenarios, human comfort levels, energy utilization methods, and emergency heating requirements, this study proposes an innovative catalytic combustion emergency heater (HC). Initially, the experimental studies assessed the catalytic and combustion performance of the heater. Compared to commercial catalysts, this heater demonstrated significant enhancements in catalyst activity and particle size uniformity. At an optimal gas flow rate of 1 L/min, the combustion efficiency reached as high as 99%. The emissions of pollutants such as nitrogen oxides and carbon monoxide were below the detection limit of 0.01%. The heater still could successfully cold start at an ambient temperature of -30°C. Subsequently, the heater’s continuous heating performance was evaluated by collecting skin temperature and subjective thermal evaluations. In a wind-chilled environment of -10°C to 15°C, the mean skin temperature was maintained between 30.5°C and 35.6°C, with marked improvements in subjective evaluation of 0.65 units. Comparative analyses were further conducted among the HC, a porous medium infrared heater (HPM), and a quartz tube infrared heater (HQT). Only the HC's infrared spectrum matches the absorption peaks of human skin and clothing, leading to a more significant elevation in skin temperature. Moreover, HC's energy consumption and carbon emissions were just 13% and 34% of HPM's, and 4% and 0.02% of HQT's, respectively. Furthermore, an intermittent heating strategy was developed using the orthogonal testing. By establishing the 'Corrective Power' index for intermittent heating, CPinterm, the optimal strategy was determined to have a heating distance of 40 cm, a heating time of 5 min, and an intermittent time of 5 min. This approach achieves thermal comfort while further reducing energy consumption by 50%. Finally, to enable the heater to be more intelligent, a human-machine interactive heating strategy was established. Five machine learning algorithms were compared to construct predictive models. The results indicated that the Random Forest model ensured the shortest computation time and achieved the highest accuracy of 0.84. This study not only presents a method for maintaining vital signs and comfort through a heat source but also lay the groundwork in modeling details and theoretical basis for future intelligent control devices.