Tianjin UniversitySchool of Future Technology
你好, Hello, こんにちは, Bonjour, Привет, Hallo, مرحبا, नमस्ते, 안녕하세요, Ciao. I'm Liu Zenghui. I am currently completing my master's studies at the School of Future Technology, Tianjin University, primarily engaged in fire dynamics, building fire analysis, and intelligent fire protection. Previously, I conducted a one-year research on the spatiotemporal prediction of infection risks in waiting rooms. In the future, I will devote myself to the research on resilience, sustainability, and equity of urban complex systems. Additionally, I am currently a student member of the Architectural Society of China, the Fire Engineers Association, and the International Building Performance Simulation Association. I also serve as a peer reviewer for journals such as Humanities and Social Sciences Communications and Journal of Safety Science and Resilience. In my spare time, I play basketball, table tennis, Guan Dan (a kind of card game), listen to music, and play the guitar (for indie music lovers).
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Tianjin UniversitySchool of Future Technology
Master of Civil Engineering. StudentSep. 2023 - present -
Shenyang Jianzhu UniversityB.S. in Engineering ManagementSep. 2019 - Jul. 2023
Experience
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China Construction Eighth Engineering DivisionProject Manager AssistantJul. 2022 - Sep. 2022
Honors & Awards
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National Scholarship for Graduate Students2024
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Tianjin University merit student2024
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Tianjin University entrance senior scholarship2024
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Outstanding graduate of Shenyang Jianzhu University2024
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Honorary nomination for the American College Student Mathematical Contest in Modeling2022
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First prize of Liaoning Province in the National College Students Mathematical Contest in Modeling2021
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Liaoning Provincial Government Scholarship2021
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Second prize in the National Mathematics Competition for College students2020
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Tianjin University advanced individual in science and technology innovation2024
Research interests
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Urban AI
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Energy transition equity
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Impact assessment of climate adaptation technologies
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Transformative solutions at the nexus of humans, infrastructure, energy, and urban systems.
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Smart fire fighting
News
Selected Publications (view all )
Advancing just transition: The role of biomass co-firing in emission reductions and employment for coal regions
Mingyu Zhai,Xuelin Tian,Zenghui Liu ,Yincheng Zhao,Yating Deng,Weiyao Yang*.
Sustainable Energy Technologies and Assessments 2025
As efforts to mitigate carbon emissions intensify, the issue of justice in the transition process has gained significant attention. The environmental impacts of low-carbon technologies, such as renewable energy, have been widely evaluated. This study develops an integrated model to quantify the employment effects of biomass co-firing retrofitting for coal plants and to explore its optimal ratio. We also modified a plant-level carbon emission calculation method to analyze the trade-off between carbon emissions and social benefits. Our results indicate that when the blend rate of coal and biomass exceeds 90%, there are noticeable changes in job creation. However, the maximum job creation value is negative, suggesting that biomass co-firing technology is not an effective choice for achieving a just transition during the large-scale coal phase-out process, and it is less competitive compared to renewable energy. Additionally, we find that generation efficiency is linearly negatively correlated with the blend rate. Moreover, carbon emission intensity and job intensity are positively correlated linearly, while there is a nonlinear negative correlation between job intensity and the blend ratio. Overall, biomass co-firing appears insufficient for a just transition in coal phase-out, indicating a need for regionally adaptive blend ratios for optimal operation.
Advancing just transition: The role of biomass co-firing in emission reductions and employment for coal regions
Mingyu Zhai,Xuelin Tian,Zenghui Liu ,Yincheng Zhao,Yating Deng,Weiyao Yang*.
Sustainable Energy Technologies and Assessments 2025
As efforts to mitigate carbon emissions intensify, the issue of justice in the transition process has gained significant attention. The environmental impacts of low-carbon technologies, such as renewable energy, have been widely evaluated. This study develops an integrated model to quantify the employment effects of biomass co-firing retrofitting for coal plants and to explore its optimal ratio. We also modified a plant-level carbon emission calculation method to analyze the trade-off between carbon emissions and social benefits. Our results indicate that when the blend rate of coal and biomass exceeds 90%, there are noticeable changes in job creation. However, the maximum job creation value is negative, suggesting that biomass co-firing technology is not an effective choice for achieving a just transition during the large-scale coal phase-out process, and it is less competitive compared to renewable energy. Additionally, we find that generation efficiency is linearly negatively correlated with the blend rate. Moreover, carbon emission intensity and job intensity are positively correlated linearly, while there is a nonlinear negative correlation between job intensity and the blend ratio. Overall, biomass co-firing appears insufficient for a just transition in coal phase-out, indicating a need for regionally adaptive blend ratios for optimal operation.
Dual-agent intelligent fire detection method for large commercial spaces based on numerical databases and artificial intelligence
GANG Liu#,Zenghui Liu#,Guanhua Qu*,Lei Ren*.
Process Safety and Environmental Protection 2024
This study combines distributed fiber optic temperature sensing systems with deep learning algorithms to develop a dual-agent intelligent fire detection method for rapidly and accurately predicting key fire information in large commercial spaces, including the location of the fire source, the intensity of fire development, and the distribution of carbon monoxide on critical planes. This work not only improves the accuracy and response speed of fire alarms but also provides data support for emergency evacuation and rescue operations, reducing casualties and property loss caused by fires, and has significant practical importance for the safety protection systems of modern commercial buildings.
Dual-agent intelligent fire detection method for large commercial spaces based on numerical databases and artificial intelligence
GANG Liu#,Zenghui Liu#,Guanhua Qu*,Lei Ren*.
Process Safety and Environmental Protection 2024
This study combines distributed fiber optic temperature sensing systems with deep learning algorithms to develop a dual-agent intelligent fire detection method for rapidly and accurately predicting key fire information in large commercial spaces, including the location of the fire source, the intensity of fire development, and the distribution of carbon monoxide on critical planes. This work not only improves the accuracy and response speed of fire alarms but also provides data support for emergency evacuation and rescue operations, reducing casualties and property loss caused by fires, and has significant practical importance for the safety protection systems of modern commercial buildings.
Intelligent generation method of infection risk map and management system in hospital waiting room for respiratory infectious diseases
Guanhua Qu#,Zenghui Liu#,Lei Ren*,Gang Liu*.
Journal of Building Engineering 2024
The innovation of this study is to generate infection risk map by intelligent method and manage the system, so as to achieve accurate control of airborne infection risk in waiting room. The results of this study not only help to select hospital design scheme, but also optimize the operation control strategy of ventilation system, so as to strengthen the control of infectious diseases and reduce the threat of cross-infection of respiratory infectious diseases to public health safety during peak periods in hospitals..
Intelligent generation method of infection risk map and management system in hospital waiting room for respiratory infectious diseases
Guanhua Qu#,Zenghui Liu#,Lei Ren*,Gang Liu*.
Journal of Building Engineering 2024
The innovation of this study is to generate infection risk map by intelligent method and manage the system, so as to achieve accurate control of airborne infection risk in waiting room. The results of this study not only help to select hospital design scheme, but also optimize the operation control strategy of ventilation system, so as to strengthen the control of infectious diseases and reduce the threat of cross-infection of respiratory infectious diseases to public health safety during peak periods in hospitals..
An investigation using resampling techniques and explainable machine learning to minimize fire losses in residential buildings
Zenghui Liu*,Yingnan Zhuang.
Journal of Building Engineering 2024
This study not only improves the emergency response strategies for urban residential fires, but also provides customized fire safety policies for different urban environments, effectively reducing fire risk. Through the introduction of resampler technology and interpretable machine learning, this study not only improves the prediction accuracy of the model, but also enhances the transparency and interpretability of the model, providing a more scientific and reliable tool for fire risk management..
An investigation using resampling techniques and explainable machine learning to minimize fire losses in residential buildings
Zenghui Liu*,Yingnan Zhuang.
Journal of Building Engineering 2024
This study not only improves the emergency response strategies for urban residential fires, but also provides customized fire safety policies for different urban environments, effectively reducing fire risk. Through the introduction of resampler technology and interpretable machine learning, this study not only improves the prediction accuracy of the model, but also enhances the transparency and interpretability of the model, providing a more scientific and reliable tool for fire risk management..