Green Energy and Resources最新文献_第3页

A review on comprehensive strategies for decarbonizing bioethanol production process 生物乙醇生产过程脱碳综合策略综述

Green Energy and Resources Pub Date : 2025-12-01 Epub Date: 2025-10-06 DOI: 10.1016/j.gerr.2025.100153

Treerat Vacharanukrauh , Apinan Soottitantawat , Nuttha Thongchul , Worapon Kiatkittipong , Nopphon Weeranoppanant , Suttichai Assabumrungrat

{"title":"A review on comprehensive strategies for decarbonizing bioethanol production process","authors":"Treerat Vacharanukrauh , Apinan Soottitantawat , Nuttha Thongchul , Worapon Kiatkittipong , Nopphon Weeranoppanant , Suttichai Assabumrungrat","doi":"10.1016/j.gerr.2025.100153","DOIUrl":"10.1016/j.gerr.2025.100153","url":null,"abstract":"<div><div>Bioethanol plays a crucial role in the global transition to sustainability, serving as a renewable fuel especially in the transportation sector, and a versatile renewable chemical precursor in industries, mitigating greenhouse gas (GHG) emissions. Although bioethanol is renewable, its production is still carbon-intensive, with most emissions arising from fermentation and cogeneration. Despite significant advancements, existing works on bioethanol have largely focused on individual decarbonization elements (e.g., CCU, CCS in bioenergy, and process intensification in ethanol production). Few studies link these strategies together to show how they could collectively move bioethanol toward carbon-negative production. This review aims to fill that gap by systematically analyzing the evolution of bioethanol production processes, identifying key sources of CO<sub>2</sub> emissions, and critically evaluating state-of-the-art strategies—including process optimization, CCU, and CCS—within a unified framework. Overall, this review underscores that integrating process optimization, CCU, and CCS can transform bioethanol production from a low-carbon fuel into a negative-emission technology, reinforcing its pivotal role in global decarbonization efforts.</div></div>","PeriodicalId":100597,"journal":{"name":"Green Energy and Resources","volume":"3 4","pages":"Article 100153"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145467489","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}

引用次数: 0

Insight into the occurrence relationships between Pb, As, Zn and Cr with minerals phases in the coal gangue: A novel quantitative dissociation method 煤矸石中Pb、As、Zn、Cr与矿物相赋存关系的研究：一种新的定量解离方法

Green Energy and Resources Pub Date : 2025-09-01 Epub Date: 2025-09-11 DOI: 10.1016/j.gerr.2025.100143

Yichao Chen , Wanyao Li , Haofei Li , Yuhong Qin , Shugang Guo , Bin Fang , Yujia Du , Jin Yuan , Leteng Lin

{"title":"Insight into the occurrence relationships between Pb, As, Zn and Cr with minerals phases in the coal gangue: A novel quantitative dissociation method","authors":"Yichao Chen , Wanyao Li , Haofei Li , Yuhong Qin , Shugang Guo , Bin Fang , Yujia Du , Jin Yuan , Leteng Lin","doi":"10.1016/j.gerr.2025.100143","DOIUrl":"10.1016/j.gerr.2025.100143","url":null,"abstract":"<div><div>Establishing the quantitative relationships between heavy metals and mineral phases in coal gangue is essential for its comprehensive landfill and refined utilization. In this study, the Guandi coal gangue was subjected to a stepwise dissociation method using seven concentration gradients (0.1, 1.0, 4.0, 6.0, 8.0, 10.0, 12.0 mol/L) of aqua regia and hydrofluoric acid. Combined with the Rietveld refinement method, inverse matrix calculations of residual fractions of mineral phases and dissociation degrees of heavy metals after dissociation, the quantitative relationships between Pb, As, Zn, Cr and the mineral phases were determined. The results show that kaolinite, quartz, pyrite, and the amorphous phase are the primary host phases for Pb, As, Zn, and Cr, with their contents in crystalline phases ranging from 71.36% to 87.68%. Validation via the standard addition method demonstrates that the relative standard deviation of the stepwise dissociation for Pb, As, Zn, and Cr is ≤7.23%, with spike recovery rates ranging from 85.43% to 112.85%, indicating favorable test results. Sequential chemical leaching demonstrates that heavy metals are mainly distributed in stable aluminosilicate-bound state and carbonate or oxide-bound state. The toxicity characteristic leaching procedure test indicated that Cr exhibited high toxicity and thus required long-term monitoring. The results of this study provide important theoretical guidance for the comprehensive landfilling and resource utilization of Guandi coal gangue, and the established analytical method can be extended to studies on quantitative relationships between heavy metals and mineral phases in other tailings.</div></div>","PeriodicalId":100597,"journal":{"name":"Green Energy and Resources","volume":"3 3","pages":"Article 100143"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145120980","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}

引用次数: 0

Microbial biomass conversion for hydrogen production: A review 微生物生物质转化制氢研究进展

Green Energy and Resources Pub Date : 2025-09-01 Epub Date: 2025-05-10 DOI: 10.1016/j.gerr.2025.100131

Muhamad Reda Galih Pangestu , Shaikh Abdur Razzak , Shihab Uddin

{"title":"Microbial biomass conversion for hydrogen production: A review","authors":"Muhamad Reda Galih Pangestu , Shaikh Abdur Razzak , Shihab Uddin","doi":"10.1016/j.gerr.2025.100131","DOIUrl":"10.1016/j.gerr.2025.100131","url":null,"abstract":"<div><div>The escalating demand for clean and sustainable energy sources has propelled hydrogen to the forefront of alternative fuel research. Microbial biomass conversion, a bio-based process utilizing microorganisms to convert organic matter into hydrogen, presents a promising avenue for achieving this goal. This review provides a comprehensive overview of possible microbial biomass conversion methods, including both light-dependent and light-independent methods, and compares their hydrogen production rates (HPRs). Light-dependent methods such as photo-fermentation offer HPRs exceeding 3 m<sup>3</sup>/dm<sup>3</sup>, suggesting highly efficient hydrogen generation possibilities. However, most rely on indirect processes or specific light conditions, potentially hindering H<sub>2</sub> production. Dark fermentation (DF) demonstrates significantly higher HPRs, up to 12 m<sup>3</sup>/d/m<sup>3</sup>, with no light requirements, making it a strong contender for large-scale production. Microbial electrolysis cells (MECs) show even greater HPRs of up to 72 m<sup>3</sup>/d/m<sup>3</sup>, competing favorably in hydrogen generation feasibility. Despite promising advancements, challenges remain in scaling up these processes for commercial viability. While current research achieves high HPRs, reactor volumes are typically below 1 L. This review explores opportunities and challenges associated with scaling up, particularly focusing on integrating DF and MECs. Combining these methods holds promise for enhancing stability and achieving efficient energy recovery.</div></div>","PeriodicalId":100597,"journal":{"name":"Green Energy and Resources","volume":"3 3","pages":"Article 100131"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144580901","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}

引用次数: 0

Study on preparation technology and properties of calcium based CO2 absorbent from acid leaching steel slag 酸浸钢渣制备钙基CO2吸附剂的工艺及性能研究

Green Energy and Resources Pub Date : 2025-09-01 Epub Date: 2025-08-08 DOI: 10.1016/j.gerr.2025.100140

Ruiying Wang , Tao Qi , Hongfeng Ji , Gang Du , Canhua Li , Shujing Zhu , Jiamao Li , Chen Zhao

{"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}

引用次数: 0

Study on the influence of rice straw ash cement mortar finish on the temperature pattern of sunning water pool in cold regions 稻草灰水泥砂浆饰面对寒区日光水池温度格局的影响研究

Green Energy and Resources Pub Date : 2025-09-01 Epub Date: 2025-09-03 DOI: 10.1016/j.gerr.2025.100142

Fu-cheng Wang , Wei Wang , Jia-mei Wen , Jia-bing Tian , Jin-qi Zhao , Yaqoob Majeed

{"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}

引用次数: 0

Exploring the application of artificial intelligence for bioelectrochemical systems: A review of recent research 探索人工智能在生物电化学系统中的应用：近期研究综述

Green Energy and Resources Pub Date : 2025-09-01 Epub Date: 2025-09-08 DOI: 10.1016/j.gerr.2025.100141

Miguel Esteban Pardo Gómez, Evan Park, Ying Zheng , Amarjeet Bassi, Tianlong Liu

{"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}

引用次数: 0

Editorial: AI-driven green revolution 社论：人工智能驱动的绿色革命

Green Energy and Resources Pub Date : 2025-09-01 Epub Date: 2025-09-09 DOI: 10.1016/j.gerr.2025.100144

Tianlong Liu, Ying Zheng

引用次数: 0

Review of machine learning applications for predicting the quality of biomass briquettes for sustainable and low-carbon energy solutions 回顾机器学习在预测可持续和低碳能源解决方案生物质压块质量方面的应用

Green Energy and Resources Pub Date : 2025-09-01 Epub Date: 2025-05-13 DOI: 10.1016/j.gerr.2025.100130

Constance Nakato Nakimuli , Fred Kaggwa , Johan De Greef , David Kilama Okot , Julien Blondeau , Simon Kawuma

{"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":"2025-09-01","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}

引用次数: 0

A mini review on AI-driven thermal treatment of solid Waste: Emission control and process optimization 人工智能驱动固体废物热处理技术综述：排放控制与工艺优化

Green Energy and Resources Pub Date : 2025-06-01 Epub Date: 2025-05-28 DOI: 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

{"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}

引用次数: 0

Flame acceleration in unconfined lean hydrogen-oxygen mixtures using a hemispherical soap bubble method 用半球形肥皂泡法在无约束贫氢-氧混合物中加速火焰

Green Energy and Resources Pub Date : 2025-06-01 Epub Date: 2025-05-11 DOI: 10.1016/j.gerr.2025.100129

Wookyung Kim , Keita Tanaka , Akihiro Ueda , Sushil Raut , Yangkyun Kim , Hongliang Luo

引用次数: 0