2025
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.
2024
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.
The passive optimization mechanism of winter thermal performance in commercial complex based on coupled multi-spatial parameters
Lei Ren#,Guanhau Qu#* ,Gang Liu*,Zenghui Liu.
Journal of Building Engineering 2024
The innovation of this study is to reveal the passive optimization potential of commercial complex thermal performance in winter through coupling analysis of multi-spatial parameters. The research results not only provide a scientific basis for architectural design, but also provide a new idea for realizing low energy consumption and high efficiency architectural design. The thermal performance of commercial complexes can be significantly improved by optimizing the spatial parameters, thus reducing the dependence on active heating systems and reducing operating costs and carbon emissions.
The passive optimization mechanism of winter thermal performance in commercial complex based on coupled multi-spatial parameters
Lei Ren#,Guanhau Qu#* ,Gang Liu*,Zenghui Liu.
Journal of Building Engineering 2024
The innovation of this study is to reveal the passive optimization potential of commercial complex thermal performance in winter through coupling analysis of multi-spatial parameters. The research results not only provide a scientific basis for architectural design, but also provide a new idea for realizing low energy consumption and high efficiency architectural design. The thermal performance of commercial complexes can be significantly improved by optimizing the spatial parameters, thus reducing the dependence on active heating systems and reducing operating costs and carbon emissions.
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..
Combinatorial machine learning approaches for high-rise building cost prediction and their interpretability analysis
Zenghui Liu,Jing Lin*.
Journal of Asian Architecture and Building Engineering 2024
The innovation of this study is the combination of combinatorial machine learning methods with explanatory analysis, which not only improves the accuracy of cost predictions, but also enhances the transparency of the model. By analyzing the interaction between features, the researchers found that there is a negative correlation between the "expected project duration" and the "building structure", while the "expected project duration" and the "interior decoration" may cancel each other out. This research result is of great significance to construction management. It not only helps investors assess the profitability of projects more accurately, but also improves the efficiency and quality of investment decisions.
Combinatorial machine learning approaches for high-rise building cost prediction and their interpretability analysis
Zenghui Liu,Jing Lin*.
Journal of Asian Architecture and Building Engineering 2024
The innovation of this study is the combination of combinatorial machine learning methods with explanatory analysis, which not only improves the accuracy of cost predictions, but also enhances the transparency of the model. By analyzing the interaction between features, the researchers found that there is a negative correlation between the "expected project duration" and the "building structure", while the "expected project duration" and the "interior decoration" may cancel each other out. This research result is of great significance to construction management. It not only helps investors assess the profitability of projects more accurately, but also improves the efficiency and quality of investment decisions.
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..
Cross-physical field prediction method for smoke field distribution in commercial building fire based on distributed optical fiber sensor
Guanhua Qu#,Zenghui Liu#,Lei Ren*,Gang Liu*.
Journal of Building Engineering 2024
This study uses a single fiber optic sensor to achieve dynamic prediction of different physical fields, and expands the application range of fiber optic sensors to monitor gas and visibility fields in commercial building fires. This not only improves the level of fire monitoring, but also effectively reduces the cost of equipment. It provides effective data support for planning individual evacuation routes and firefighter rescue arrangements, further ensuring personnel safety.
Cross-physical field prediction method for smoke field distribution in commercial building fire based on distributed optical fiber sensor
Guanhua Qu#,Zenghui Liu#,Lei Ren*,Gang Liu*.
Journal of Building Engineering 2024
This study uses a single fiber optic sensor to achieve dynamic prediction of different physical fields, and expands the application range of fiber optic sensors to monitor gas and visibility fields in commercial building fires. This not only improves the level of fire monitoring, but also effectively reduces the cost of equipment. It provides effective data support for planning individual evacuation routes and firefighter rescue arrangements, further ensuring personnel safety.