Pub Date : 2025-09-01DOI: 10.1016/j.gerr.2025.100140
Ruiying Wang , Tao Qi , Hongfeng Ji , Gang Du , Canhua Li , Shujing Zhu , Jiamao Li , Chen Zhao
Currently, more and more industrial carbon emissions lead to a significant increase in greenhouse gases, which has a significant impact on global climate change. Therefore, the storage and reuse of carbon dioxide is an important issue in modern society. In this paper, calcium based CO2 absorbent was prepared from converter slag by acetic acid extraction and modification of steel slag. The study investigated the effects of parameters in indirect acetic acid leaching, including acetic acid concentration, leaching time, solid-to-liquid ratio, and temperature, on the elemental content in the adsorbent. It also compared the cyclic adsorbent stability of calcium-based adsorbents with commercial calcium oxide. The results indicated that the optimal technical parameters were: acetic acid concentration 1 mol/L, leaching time 40 min, solid-liquid ratio of 1:10, leaching temperature of 40°C, achieving an extraction rate of 88.05% for calcium elements. Its initial CO2 adsorbent capacity is 0.51 gCO2/gadsorbent, and the CO2 adsorbent capacity after 20 cycles is 0.202 gCO2/gadsorbent, and the inactivation rate is 60.39%. Compared with AR CaO, the adsorbent has more ideal CO2 capture ability.
{"title":"Study on preparation technology and properties of calcium based CO2 absorbent from acid leaching steel slag","authors":"Ruiying Wang , Tao Qi , Hongfeng Ji , Gang Du , Canhua Li , Shujing Zhu , Jiamao Li , Chen Zhao","doi":"10.1016/j.gerr.2025.100140","DOIUrl":"10.1016/j.gerr.2025.100140","url":null,"abstract":"<div><div>Currently, more and more industrial carbon emissions lead to a significant increase in greenhouse gases, which has a significant impact on global climate change. Therefore, the storage and reuse of carbon dioxide is an important issue in modern society. In this paper, calcium based CO<sub>2</sub> absorbent was prepared from converter slag by acetic acid extraction and modification of steel slag. The study investigated the effects of parameters in indirect acetic acid leaching, including acetic acid concentration, leaching time, solid-to-liquid ratio, and temperature, on the elemental content in the adsorbent. It also compared the cyclic adsorbent stability of calcium-based adsorbents with commercial calcium oxide. The results indicated that the optimal technical parameters were: acetic acid concentration 1 mol/L, leaching time 40 min, solid-liquid ratio of 1:10, leaching temperature of 40°C, achieving an extraction rate of 88.05% for calcium elements. Its initial CO<sub>2</sub> adsorbent capacity is 0.51 g<sub>CO2</sub>/g<sub>adsorbent</sub>, and the CO<sub>2</sub> adsorbent capacity after 20 cycles is 0.202 g<sub>CO2/</sub>g<sub>adsorbent</sub>, and the inactivation rate is 60.39%. Compared with AR CaO, the adsorbent has more ideal CO<sub>2</sub> capture ability.</div></div>","PeriodicalId":100597,"journal":{"name":"Green Energy and Resources","volume":"3 3","pages":"Article 100140"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145094870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01DOI: 10.1016/j.gerr.2025.100142
Fu-cheng Wang , Wei Wang , Jia-mei Wen , Jia-bing Tian , Jin-qi Zhao , Yaqoob Majeed
To investigate the warming effect of rice straw ash (RSA) cement mortar facing on sunning water pools, this study focuses on a sunning water pool with a 5% substitution rate of RSA in its cement mortar facing. A temperature control test was conducted to compare it with a conventional cement mortar-faced sunning water pool. Additionally, finite element software was employed to create models for both the RSA and conventional cement mortar-faced sunning water pools, facilitating an analysis of the variations in water temperature within these systems.The results indicate that the RSA cement mortar facing can enhance the daily average water temperature of the sunning water pools by 0.1–0.6°C compared to those featuring conventional cement mortar facing. Simulation data reveal that the water temperature in the sunning water pool utilizing RSA cement mortar facing is approximately 0.46°C higher than that observed in its counterpart with standard cement mortar facing. The trends identified through theoretical calculations, experimental data, and simulation results are largely consistent, suggesting that RSA cement mortar facing effectively improves the thermal performance of sunning water pools.These findings provide valuable theoretical support for implementing RSA cement mortar in agricultural facilities.
{"title":"Study on the influence of rice straw ash cement mortar finish on the temperature pattern of sunning water pool in cold regions","authors":"Fu-cheng Wang , Wei Wang , Jia-mei Wen , Jia-bing Tian , Jin-qi Zhao , Yaqoob Majeed","doi":"10.1016/j.gerr.2025.100142","DOIUrl":"10.1016/j.gerr.2025.100142","url":null,"abstract":"<div><div>To investigate the warming effect of rice straw ash (RSA) cement mortar facing on sunning water pools, this study focuses on a sunning water pool with a 5% substitution rate of RSA in its cement mortar facing. A temperature control test was conducted to compare it with a conventional cement mortar-faced sunning water pool. Additionally, finite element software was employed to create models for both the RSA and conventional cement mortar-faced sunning water pools, facilitating an analysis of the variations in water temperature within these systems.The results indicate that the RSA cement mortar facing can enhance the daily average water temperature of the sunning water pools by 0.1–0.6°C compared to those featuring conventional cement mortar facing. Simulation data reveal that the water temperature in the sunning water pool utilizing RSA cement mortar facing is approximately 0.46°C higher than that observed in its counterpart with standard cement mortar facing. The trends identified through theoretical calculations, experimental data, and simulation results are largely consistent, suggesting that RSA cement mortar facing effectively improves the thermal performance of sunning water pools.These findings provide valuable theoretical support for implementing RSA cement mortar in agricultural facilities.</div></div>","PeriodicalId":100597,"journal":{"name":"Green Energy and Resources","volume":"3 3","pages":"Article 100142"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145094872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01DOI: 10.1016/j.gerr.2025.100141
Miguel Esteban Pardo Gómez, Evan Park, Ying Zheng , Amarjeet Bassi, Tianlong Liu
Bioelectrochemical systems (BES) offer promising solutions for sustainable energy production and wastewater treatment. However, their complex biological and electrochemical dynamics pose significant challenges for traditional modeling approaches. This review explores the recent advancements in applying artificial intelligence (AI) techniques to enhance the performance and scalability of BES technologies. We detailed the roles of machine learning (ML) algorithms, such as artificial neural networks (ANNs), support vector regression (SVR), and random forest regression (RFR), in predicting critical BES performance metrics. Additionally, we discussed metaheuristic optimization techniques that have improved system design and operational parameters, yielding significant gains in energy recovery and stability. The integration of real-time monitoring and adaptive control systems, powered by AI, is highlighted for its potential to dynamically adjust BES operations in response to fluctuating environmental conditions. Despite these advancements, challenges remain, particularly in data standardization and modeling biological complexity within BES. We outline current limitations and future directions, emphasizing the need for robust datasets, standardized methodologies, and advanced AI frameworks to further unlock the potential of AI-driven BES systems in achieving sustainable bioenergy solutions.
{"title":"Exploring the application of artificial intelligence for bioelectrochemical systems: A review of recent research","authors":"Miguel Esteban Pardo Gómez, Evan Park, Ying Zheng , Amarjeet Bassi, Tianlong Liu","doi":"10.1016/j.gerr.2025.100141","DOIUrl":"10.1016/j.gerr.2025.100141","url":null,"abstract":"<div><div>Bioelectrochemical systems (BES) offer promising solutions for sustainable energy production and wastewater treatment. However, their complex biological and electrochemical dynamics pose significant challenges for traditional modeling approaches. This review explores the recent advancements in applying artificial intelligence (AI) techniques to enhance the performance and scalability of BES technologies. We detailed the roles of machine learning (ML) algorithms, such as artificial neural networks (ANNs), support vector regression (SVR), and random forest regression (RFR), in predicting critical BES performance metrics. Additionally, we discussed metaheuristic optimization techniques that have improved system design and operational parameters, yielding significant gains in energy recovery and stability. The integration of real-time monitoring and adaptive control systems, powered by AI, is highlighted for its potential to dynamically adjust BES operations in response to fluctuating environmental conditions. Despite these advancements, challenges remain, particularly in data standardization and modeling biological complexity within BES. We outline current limitations and future directions, emphasizing the need for robust datasets, standardized methodologies, and advanced AI frameworks to further unlock the potential of AI-driven BES systems in achieving sustainable bioenergy solutions.</div></div>","PeriodicalId":100597,"journal":{"name":"Green Energy and Resources","volume":"3 3","pages":"Article 100141"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145157437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-01DOI: 10.1016/j.gerr.2025.100144
Tianlong Liu, Ying Zheng
{"title":"Editorial: AI-driven green revolution","authors":"Tianlong Liu, Ying Zheng","doi":"10.1016/j.gerr.2025.100144","DOIUrl":"10.1016/j.gerr.2025.100144","url":null,"abstract":"","PeriodicalId":100597,"journal":{"name":"Green Energy and Resources","volume":"3 3","pages":"Article 100144"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145094871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01DOI: 10.1016/j.gerr.2025.100132
Dongjie Pang , Cristina Moliner , Tao Wang , Jin Sun , Xinyan Zhang , Yingping Pang , Xiqiang Zhao , Zhanlong Song , Ziliang Wang , Yanpeng Mao , Wenlong Wang
The advent of novel waste disposal methodologies, which are energy-efficient and environmentally benign, has created opportunities for the deployment of artificial intelligence technologies in the management of solid waste treatment. This review examines the deployment of AI-optimized control algorithms in processes including pyrolysis, incineration, and gasification. The application of machine learning models, including linear regression (LR), genetic algorithm (GA), support vector machine (SVM), artificial neural networks (ANN), decision trees (DT), and Extreme Gradient Boosting (XGBoost), enables real-time monitoring of performance and dynamic adjustment of parameters to enhance energy recovery and minimize pollution. The implementation of AI-based solutions enables the optimization of key characteristics, such as temperature and oxygen levels, with the objective of achieving optimal energy efficiency while minimizing the emission of harmful substances, including CO, NOx, and dioxins. Notwithstanding these advancements, challenges remain in hyperparameter tuning, probabilistic assessments, and feature generation. A comprehensive understanding of future technologies will necessitate a synthesis of knowledge and data-oriented approaches, the design of autonomous control systems, and the integration of digital twin technologies to bridge the gap between theory and practice.
{"title":"A mini review on AI-driven thermal treatment of solid Waste: Emission control and process optimization","authors":"Dongjie Pang , Cristina Moliner , Tao Wang , Jin Sun , Xinyan Zhang , Yingping Pang , Xiqiang Zhao , Zhanlong Song , Ziliang Wang , Yanpeng Mao , Wenlong Wang","doi":"10.1016/j.gerr.2025.100132","DOIUrl":"10.1016/j.gerr.2025.100132","url":null,"abstract":"<div><div>The advent of novel waste disposal methodologies, which are energy-efficient and environmentally benign, has created opportunities for the deployment of artificial intelligence technologies in the management of solid waste treatment. This review examines the deployment of AI-optimized control algorithms in processes including pyrolysis, incineration, and gasification. The application of machine learning models, including linear regression (LR), genetic algorithm (GA), support vector machine (SVM), artificial neural networks (ANN), decision trees (DT), and Extreme Gradient Boosting (XGBoost), enables real-time monitoring of performance and dynamic adjustment of parameters to enhance energy recovery and minimize pollution. The implementation of AI-based solutions enables the optimization of key characteristics, such as temperature and oxygen levels, with the objective of achieving optimal energy efficiency while minimizing the emission of harmful substances, including CO, NOx, and dioxins. Notwithstanding these advancements, challenges remain in hyperparameter tuning, probabilistic assessments, and feature generation. A comprehensive understanding of future technologies will necessitate a synthesis of knowledge and data-oriented approaches, the design of autonomous control systems, and the integration of digital twin technologies to bridge the gap between theory and practice.</div></div>","PeriodicalId":100597,"journal":{"name":"Green Energy and Resources","volume":"3 2","pages":"Article 100132"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144239406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01DOI: 10.1016/j.gerr.2025.100129
Wookyung Kim , Keita Tanaka , Akihiro Ueda , Sushil Raut , Yangkyun Kim , Hongliang Luo
This study investigates the flame acceleration dynamics in lean hydrogen-oxygen mixtures, focusing on critical parameters such as Péclet number, Markstein number, and the acceleration exponent. Using a hemispherical soap bubble method, the research explores the onset of flame acceleration and its dependence on Darrieus–Landau and diffusive–thermal instabilities. The findings provide insights into the transition to self-similarity, fractal dimensions of the flame front, and the conditions influencing flame acceleration in hydrogen-oxygen mixtures. The results contribute to the fundamental understanding of hydrogen combustion dynamics, offering valuable data for the safe integration of hydrogen as a marine fuel. This research addresses key gaps in the literature and supports the development of safety standards for hydrogen-based energy systems in marine applications.
{"title":"Flame acceleration in unconfined lean hydrogen-oxygen mixtures using a hemispherical soap bubble method","authors":"Wookyung Kim , Keita Tanaka , Akihiro Ueda , Sushil Raut , Yangkyun Kim , Hongliang Luo","doi":"10.1016/j.gerr.2025.100129","DOIUrl":"10.1016/j.gerr.2025.100129","url":null,"abstract":"<div><div>This study investigates the flame acceleration dynamics in lean hydrogen-oxygen mixtures, focusing on critical parameters such as Péclet number, Markstein number, and the acceleration exponent. Using a hemispherical soap bubble method, the research explores the onset of flame acceleration and its dependence on Darrieus–Landau and diffusive–thermal instabilities. The findings provide insights into the transition to self-similarity, fractal dimensions of the flame front, and the conditions influencing flame acceleration in hydrogen-oxygen mixtures. The results contribute to the fundamental understanding of hydrogen combustion dynamics, offering valuable data for the safe integration of hydrogen as a marine fuel. This research addresses key gaps in the literature and supports the development of safety standards for hydrogen-based energy systems in marine applications.</div></div>","PeriodicalId":100597,"journal":{"name":"Green Energy and Resources","volume":"3 2","pages":"Article 100129"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144203361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sustainable manufacturing is pivotal to promoting societal advancements that balance the progressive growth of human needs with the gradual exhaustion of natural resources and the environmental impact of current manufacturing technologies. Gas-solid fluidization, a key process intensification technique, has advanced sustainability for over a century. The complex nature of these systems has led to numerous analysis algorithms for assessing time-series signals critical to observe the fluidization hydrodynamics. This work reviews widely used signal analysis methods for processing the commonly-measured time-series signals for fluidization, specifically focusing on pressure drop and optical signals. Despite their widespread implementation, these methods have limited potential due to the limited visibility of optical signals and the inability of pressure signals to provide localized fluidization system information. Veritably, the traditional algorithms cannot consider all influencing factors and handle flawed, large-scale signals.
Artificial intelligence (AI) has emerged as a promising solution to overcome these limitations. Nevertheless, AI-enhanced methods for fluidization signal analysis are still nascent. This work emphasizes the potential of AI to enhance understanding of complex fluidization behavior, particularly heterogeneous agglomerations, through reviewing signal analysis methods from traditional numerical methods to AI-driven approaches. Furthermore, this study highlights the future steps necessary to adequately expand upon machine learning-based analysis methodologies and extends a call to arms for future research establishment within this field. These advancements will support the development of sustainable manufacturing technologies that balance industrial progress with environmental responsibility.
{"title":"Time-series signal analysis of sustainable process intensification: Characterization method development of gas-solid fluidized bed hydrodynamics towards AI-enhanced algorithms","authors":"Yue Yuan , Silu Chen , Meifeng Li , Jesse Zhu , Lihui Feng , Tinghui Zhang , Kaiqiao Wu , Donovan Chaffart","doi":"10.1016/j.gerr.2025.100128","DOIUrl":"10.1016/j.gerr.2025.100128","url":null,"abstract":"<div><div>Sustainable manufacturing is pivotal to promoting societal advancements that balance the progressive growth of human needs with the gradual exhaustion of natural resources and the environmental impact of current manufacturing technologies. Gas-solid fluidization, a key process intensification technique, has advanced sustainability for over a century. The complex nature of these systems has led to numerous analysis algorithms for assessing time-series signals critical to observe the fluidization hydrodynamics. This work reviews widely used signal analysis methods for processing the commonly-measured time-series signals for fluidization, specifically focusing on pressure drop and optical signals. Despite their widespread implementation, these methods have limited potential due to the limited visibility of optical signals and the inability of pressure signals to provide localized fluidization system information. Veritably, the traditional algorithms cannot consider all influencing factors and handle flawed, large-scale signals.</div><div>Artificial intelligence (AI) has emerged as a promising solution to overcome these limitations. Nevertheless, AI-enhanced methods for fluidization signal analysis are still nascent. This work emphasizes the potential of AI to enhance understanding of complex fluidization behavior, particularly heterogeneous agglomerations, through reviewing signal analysis methods from traditional numerical methods to AI-driven approaches. Furthermore, this study highlights the future steps necessary to adequately expand upon machine learning-based analysis methodologies and extends a call to arms for future research establishment within this field. These advancements will support the development of sustainable manufacturing technologies that balance industrial progress with environmental responsibility.</div></div>","PeriodicalId":100597,"journal":{"name":"Green Energy and Resources","volume":"3 2","pages":"Article 100128"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01DOI: 10.1016/j.gerr.2025.100133
Shuangshuang Yan , Dongmei Bi , Chengxizi Zhang , Zhisen He , Yu Ni , Kang Yue , Shanjian Liu
Carbon-based catalysts for low-temperature denitrification were prepared from wheat straw via ZnCl2 activation and thiourea doping. The catalysts were systematically characterized using BET surface area analysis, NH3-TPD, XPS, and transient response experiments. The ZnCl2-activated catalyst exhibited a NOx reduction efficiency of 45.1%. To further enhance the denitrification performance, the Z1.2 biochar was co-doped with sulfur and nitrogen. Experimental results demonstrated that the SN2.5Z1.2/AC biochar catalyst achieved a maximum NO conversion of 88% within the temperature range of 50–260°C and exhibited stable activity in long-term durability tests. Sulfur and nitrogen co-doping markedly increased the number of strong acid sites and surface chemisorbed oxygen (Oα), thereby facilitating the formation of N-6 functional groups. The presence of C-SO3-H species may be a critical factor contributing to the enhanced NOx conversion. The denitrification process over sulfur- and nitrogen-doped biochar follows both the Eley-Rideal (E-R) and Langmuir-Hinshelwood (L-H) mechanisms, wherein •NH2 radicals play a pivotal role in the reduction of NO to its gaseous and adsorbed forms.
{"title":"ZnCl2-activated S/N-doped biochar for low-temperature NH3-SCR of NOx: Performance and pathway analysis","authors":"Shuangshuang Yan , Dongmei Bi , Chengxizi Zhang , Zhisen He , Yu Ni , Kang Yue , Shanjian Liu","doi":"10.1016/j.gerr.2025.100133","DOIUrl":"10.1016/j.gerr.2025.100133","url":null,"abstract":"<div><div>Carbon-based catalysts for low-temperature denitrification were prepared from wheat straw via ZnCl<sub>2</sub> activation and thiourea doping. The catalysts were systematically characterized using BET surface area analysis, NH<sub>3</sub>-TPD, XPS, and transient response experiments. The ZnCl<sub>2</sub>-activated catalyst exhibited a NO<sub><em>x</em></sub> reduction efficiency of 45.1%. To further enhance the denitrification performance, the Z<sub>1.2</sub> biochar was co-doped with sulfur and nitrogen. Experimental results demonstrated that the SN<sub>2.5</sub>Z<sub>1.2</sub>/AC biochar catalyst achieved a maximum NO conversion of 88% within the temperature range of 50–260°C and exhibited stable activity in long-term durability tests. Sulfur and nitrogen co-doping markedly increased the number of strong acid sites and surface chemisorbed oxygen (O<sub>α</sub>), thereby facilitating the formation of N-6 functional groups. The presence of C-SO<sub>3</sub>-H species may be a critical factor contributing to the enhanced NO<sub>x</sub> conversion. The denitrification process over sulfur- and nitrogen-doped biochar follows both the Eley-Rideal (E-R) and Langmuir-Hinshelwood (L-H) mechanisms, wherein •NH<sub>2</sub> radicals play a pivotal role in the reduction of NO to its gaseous and adsorbed forms.</div></div>","PeriodicalId":100597,"journal":{"name":"Green Energy and Resources","volume":"3 2","pages":"Article 100133"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144306436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-06-01DOI: 10.1016/j.gerr.2024.100074
Gibson Owhoro Ofremu , Babatunde Yusuf Raimi , Samuel Omokhafe Yusuf , Beatrice Akorfa Dziwornu , Somtochukwu Godfrey Nnabuife , Adaeze Mary Eze , Chisom Assumpta Nnajiofor
The innumerable impact of climate change is a global menace to human health. This paper conveys a comprehensive review of scientific literature to explore the relationship between climate change, air pollutants, and human health. The integral relationship between climate change and health is complex and has a significant impact on every facet of human life. The impact can either be direct (e.g., exposures due to extreme heat, storms, flooding, and air pollution) or indirect (e.g., displacement, food security, and variation in water). The rising temperature of the planet could lead to increasingly severe health impacts from climate change in the future. It is important to take stringent climate actions to mitigate the climate change risk and adapt to the impacts that are already happening. To lessen the speed and severity of climate change, mitigation focuses on cutting greenhouse gas emissions. Options for adaptation include things like advancing to higher ground to stop sea levels from increasing, growing new crops that can grow in a new environment, or using novel construction methods. Investing in novel or enhanced technology, infrastructure, and research is frequently required for adaptation. The review emphasized the importance of considering both short-term and long-term adaptation strategies as well as mitigation efforts, which call for steps to address the root cause by halting or reducing the growth in fossil fuel emissions that might severely and completely increase the earth's scorching temperatures. The results of this study provide insightful viewpoints on adaptation measures, and mitigation strategies for decision-makers, experts in public health, and researchers working in the field of climate change and its effects on human health.
{"title":"Exploring the relationship between climate change, air pollutants and human health: Impacts, adaptation, and mitigation strategies","authors":"Gibson Owhoro Ofremu , Babatunde Yusuf Raimi , Samuel Omokhafe Yusuf , Beatrice Akorfa Dziwornu , Somtochukwu Godfrey Nnabuife , Adaeze Mary Eze , Chisom Assumpta Nnajiofor","doi":"10.1016/j.gerr.2024.100074","DOIUrl":"10.1016/j.gerr.2024.100074","url":null,"abstract":"<div><div>The innumerable impact of climate change is a global menace to human health. This paper conveys a comprehensive review of scientific literature to explore the relationship between climate change, air pollutants, and human health. The integral relationship between climate change and health is complex and has a significant impact on every facet of human life. The impact can either be direct (e.g., exposures due to extreme heat, storms, flooding, and air pollution) or indirect (e.g., displacement, food security, and variation in water). The rising temperature of the planet could lead to increasingly severe health impacts from climate change in the future. It is important to take stringent climate actions to mitigate the climate change risk and adapt to the impacts that are already happening. To lessen the speed and severity of climate change, mitigation focuses on cutting greenhouse gas emissions. Options for adaptation include things like advancing to higher ground to stop sea levels from increasing, growing new crops that can grow in a new environment, or using novel construction methods. Investing in novel or enhanced technology, infrastructure, and research is frequently required for adaptation. The review emphasized the importance of considering both short-term and long-term adaptation strategies as well as mitigation efforts, which call for steps to address the root cause by halting or reducing the growth in fossil fuel emissions that might severely and completely increase the earth's scorching temperatures. The results of this study provide insightful viewpoints on adaptation measures, and mitigation strategies for decision-makers, experts in public health, and researchers working in the field of climate change and its effects on human health.</div></div>","PeriodicalId":100597,"journal":{"name":"Green Energy and Resources","volume":"3 2","pages":"Article 100074"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141052124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-05-13DOI: 10.1016/j.gerr.2025.100130
Constance Nakato Nakimuli , Fred Kaggwa , Johan De Greef , David Kilama Okot , Julien Blondeau , Simon Kawuma
This review discusses how Machine Learning has been applied to predict the quality of biomass briquettes produced from agricultural and municipal solid organic waste, which are crucial for advancing green and low-carbon energy solutions. Traditional methods of assessment of briquette quality involve destructive laboratory experiments, do not favor sample reuse, are time-consuming, and labor-intensive, posing barriers to efficient production. This paper reviews literature on various Machine Learning models applied for predicting and optimizing briquette quality parameters, including combustion, physical, and emission properties. Several Machine Learning models have shown promising results in predicting and optimizing these key parameters for example, a Random Forest model with R2 of 0.9936 in deformation energy prediction and Artificial Neural Networks with R2 of 0.8936 in the prediction of impact resistance. By enhancing the accuracy and efficiency of briquette quality predictions, Machine Learning algorithms contribute to the development of high-quality biomass briquettes, thereby creating sustainable and low-carbon energy systems. This review points to critical literature gaps regarding model generalizability across diverse biomass feedstocks and integration of broader quality parameters. Addressing these gaps will advance AI-based solutions, promote greener energy practices, and support sustainable development. The findings are intended to aid researchers, industry professionals, and policymakers in advancing the production of high-quality biomass briquettes for cleaner energy and sustainable development.
{"title":"Review of machine learning applications for predicting the quality of biomass briquettes for sustainable and low-carbon energy solutions","authors":"Constance Nakato Nakimuli , Fred Kaggwa , Johan De Greef , David Kilama Okot , Julien Blondeau , Simon Kawuma","doi":"10.1016/j.gerr.2025.100130","DOIUrl":"10.1016/j.gerr.2025.100130","url":null,"abstract":"<div><div>This review discusses how Machine Learning has been applied to predict the quality of biomass briquettes produced from agricultural and municipal solid organic waste, which are crucial for advancing green and low-carbon energy solutions. Traditional methods of assessment of briquette quality involve destructive laboratory experiments, do not favor sample reuse, are time-consuming, and labor-intensive, posing barriers to efficient production. This paper reviews literature on various Machine Learning models applied for predicting and optimizing briquette quality parameters, including combustion, physical, and emission properties. Several Machine Learning models have shown promising results in predicting and optimizing these key parameters for example, a Random Forest model with R<sup>2</sup> of 0.9936 in deformation energy prediction and Artificial Neural Networks with R<sup>2</sup> of 0.8936 in the prediction of impact resistance. By enhancing the accuracy and efficiency of briquette quality predictions, Machine Learning algorithms contribute to the development of high-quality biomass briquettes, thereby creating sustainable and low-carbon energy systems. This review points to critical literature gaps regarding model generalizability across diverse biomass feedstocks and integration of broader quality parameters. Addressing these gaps will advance AI-based solutions, promote greener energy practices, and support sustainable development. The findings are intended to aid researchers, industry professionals, and policymakers in advancing the production of high-quality biomass briquettes for cleaner energy and sustainable development.</div></div>","PeriodicalId":100597,"journal":{"name":"Green Energy and Resources","volume":"3 3","pages":"Article 100130"},"PeriodicalIF":0.0,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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