Green Energy and Resources最新文献_第3页

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 : 2025-05-10 DOI: 10.1016/j.gerr.2025.100131

Muhamad Reda Galih Pangestu , Shaikh Abdur Razzak , Shihab Uddin

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 m³/dm³, suggesting highly efficient hydrogen generation possibilities. However, most rely on indirect processes or specific light conditions, potentially hindering H₂ production. Dark fermentation (DF) demonstrates significantly higher HPRs, up to 12 m³/d/m³, 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 m³/d/m³, 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.

对清洁和可持续能源不断增长的需求将氢推向了替代燃料研究的前沿。微生物生物量转化是一种利用微生物将有机物转化为氢的生物基过程，为实现这一目标提供了一条有希望的途径。本文综述了可能的微生物生物质转化方法，包括依赖和光不依赖的方法，并比较了它们的产氢率（HPRs）。光发酵等依赖光的方法的hpr超过3 m3/dm3，表明高效制氢的可能性。然而，大多数依赖于间接过程或特定的光照条件，这可能会阻碍H2的产生。暗发酵（DF）表现出显著更高的hpr，高达12 m3/d/m3，不需要光，使其成为大规模生产的有力竞争者。微生物电解细胞（MECs）的hpr更高，可达72 m3/d/m3，在制氢可行性方面具有优势。尽管取得了有希望的进展，但在扩大这些工艺的商业可行性方面仍然存在挑战。虽然目前的研究取得了很高的hpr，但反应器体积通常低于1l。本文探讨了与扩大规模相关的机遇和挑战，特别关注DF和mec的整合。结合这些方法有望提高稳定性并实现有效的能量回收。

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引用次数: 0

Corrigendum to ‘Exploring the landscape of machine learning-aided research in biofuels and biodiesel: A bibliometric analysis’ [Green Energy Res. 2 (2024) 100089] “探索生物燃料和生物柴油中机器学习辅助研究的前景：文献计量分析”的勘误表[Green Energy Res. 2 (2024) 100089]

Green Energy and Resources

Pub Date : 2025-05-05 DOI: 10.1016/j.gerr.2025.100117

Avinash Alagumalai, Hua Song

引用次数: 0

Dynamic process of hydrogen flow and spontaneous combustion in tubes featuring different configurations after leakage from 35 and 70 MPa 35 MPa和70 MPa泄漏后不同构型管内氢气流动和自燃的动态过程

Green Energy and Resources

Pub Date : 2025-04-10 DOI: 10.1016/j.gerr.2025.100127

Qin Huang , Zuo-Yu Sun , Ya-Long Du , Jia-Ying Li

Hydrogen, as a green energy resource, presents a crucial opportunity to reduce emissions and facilitate the transition to sustainable energy, particularly in the shipping industry. The storage pressure for hydrogen gas (like 35 MPa for metal-composite Type III vessels and 70 MPa for polymer-composite Type IV vessels) is prone to leakage or even rupture, and hydrogen could be spontaneously ignited during pressurized leakage; thus, investigating the dynamics of spontaneous hydrogen combustion is essential for safely advancing hydrogen energy in marine applications. This study numerically examined the development of shockwaves and the spontaneous combustion process during pressurized leakage within tubes featuring various configurations (L-shaped and T-shaped, which are commonly found in actual pipelines) at pressures of 35 and 70 MPa. The results indicated that, upon release from the tested pressures, hydrogen would spontaneously ignite within the upstream sections of the tubes beyond the leakage port, with the flame propagating downstream along with the shockwave development. Notably, shockwave and spontaneous combustion characteristics variations differed across the two tube configurations. Velocity measurements showed that values would be lowest near the corner of the L-shaped tube, whereas they would consistently decline downstream in the T-shaped tube. This suggested that measures to mitigate shockwave effects (thus reducing the likelihood of spontaneous combustion) should be implemented in the upstream section of the tubes, regardless of the configuration. Additionally, pressure readings were highest near the corner of the L-shaped tube and showed a consistent decline downstream in the T-shaped tube. Therefore, protective measures addressing stress intensity should focus on the L-shaped tube's corner and the T-shaped tube's upstream section.

氢作为一种绿色能源，提供了减少排放和促进向可持续能源过渡的关键机会，特别是在航运业。氢气的储存压力（如金属复合III型容器为35 MPa，聚合物复合IV型容器为70 MPa）容易泄漏甚至破裂，氢气在加压泄漏时容易自燃；因此，研究氢自燃动力学对于安全推进氢能在海洋中的应用至关重要。本研究数值研究了在35 MPa和70 MPa压力下，不同配置的管道（l型和t型管道，在实际管道中常见）中，冲击波的发展和加压泄漏过程中的自燃过程。结果表明，当测试压力释放后，氢气会在泄漏口以外的管道上游段自燃，火焰会随着冲击波的发展向下游传播。值得注意的是，冲击波和自燃特性的变化不同于两种管道配置。速度测量表明，在l型管的拐角附近，流速值最低，而在t型管的下游，流速值持续下降。这表明，无论结构如何，应在管道的上游部分实施减轻冲击波影响的措施（从而减少自燃的可能性）。此外，压力读数在l型管的角落附近最高，在t型管的下游显示出持续下降。因此，针对应力强度的防护措施应集中在l型管的转角和t型管的上游段。

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引用次数: 0

Solar dryers: A review of mechanism, methods and critical analysis of transport models applicable in solar drying of product 太阳能干燥机：综述了适用于产品太阳能干燥的运输模型的机理、方法和关键分析

Green Energy and Resources

Pub Date : 2025-03-01 DOI: 10.1016/j.gerr.2025.100118

Onyinyechi Nnamchi , Cyprian Tom , Godwin Akpan , Murphy Umunna , David Ubong , Mathew Ibeh , Adindu Linus–Chibuezeh , Leonard Akuwueke , Stephen Nnamchi , Augustine Ben , Macmanus Ndukwu

As the world transitions towards green energy sources solar drying has become a vital technology for sustainable agricultural production, offering a cleaner, more efficient alternative to traditional drying methods. Solar drying has been demonstrated to be a sustainable and eco-friendly drying process for drying and preserving agricultural products, offering advantages over traditional methods that include faster drying rates, improved product quality, and reduced energy costs. This review examines the mechanisms and methods applicable to solar drying, including indirect and direct solar drying, hybrid systems combining solar drying with other heating sources, and thermal storage materials to address challenges such as intermittent solar radiation. The designs of solar drying systems include various solar collector configurations, drying chamber geometries, and air conveyance mechanisms crucial for efficient drying. This review therefore explores different design approaches and their effects on drying performance, highlighting the importance of understanding the complex interactions between system components. Additionally, the approach for Energy and exergy analysis of solar drying systems was explored, providing insights into energy utilization and efficiency. Finally, this review elucidates the complex transport phenomena governing solar drying, including moisture diffusion, heat and mass transfer, and airflow patterns. It identifies knowledge gaps in existing models and future research directions in transport modelling phenomena to advance sustainable, efficient, and scalable solar drying techniques.

随着世界向绿色能源转型，太阳能干燥已成为可持续农业生产的重要技术，为传统干燥方法提供了一种更清洁、更有效的替代方法。太阳能干燥已被证明是一种可持续和环保的干燥过程，用于干燥和保存农产品，提供比传统方法更快的干燥速度、提高产品质量和降低能源成本的优势。本文综述了适用于太阳能干燥的机理和方法，包括间接和直接太阳能干燥，将太阳能干燥与其他热源结合的混合系统，以及用于解决间歇性太阳辐射等挑战的储热材料。太阳能干燥系统的设计包括各种太阳能集热器配置、干燥室几何形状和空气输送机制，这对有效干燥至关重要。因此，本文探讨了不同的设计方法及其对干燥性能的影响，强调了理解系统组件之间复杂相互作用的重要性。此外，还探讨了太阳能干燥系统的能源和火用分析方法，为能源利用和效率提供了见解。最后，本文综述了控制太阳干燥的复杂传输现象，包括水分扩散、传热传质和气流模式。它确定了现有模型中的知识差距和运输建模现象的未来研究方向，以推进可持续、高效和可扩展的太阳能干燥技术。

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引用次数: 0

Optimal control strategy based on artificial intelligence applied to a continuous dark fermentation reactor for energy recovery from organic wastes 基于人工智能的连续暗发酵反应器能量回收控制策略研究

Green Energy and Resources

Pub Date : 2025-02-01 DOI: 10.1016/j.gerr.2024.100112

Kelly Joel Gurubel Tun , Elizabeth León-Becerril , Octavio García-Depraect

Dark fermentation process from low-cost renewable substrates for simultaneous wastewater treatment and hydrogen production (H₂) is suitable due to economic viability and environmental sustainability. This work explores the application of an innovative control strategy in a scale fermentation bioreactor designed for energy recovery from organic wastes. This approach not only promotes low carbon emissions but also offers significant potential for industrial application. Machine learning (ML) and optimization methods are used to model the nonlinear process and then, a neural predictive control (NPC) strategy to drive the system to its optimal operating order under varying influent conditions is developed. Predictive control uses the Newton-Raphson as the optimization algorithm and a multi-layer feedforward neural network for the state prediction. This strategy has demonstrated to be a viable algorithm for real-time control applications. First, experimental data from continuous dark fermentation are modeled using support vector machine (SVM) algorithm for response prediction and then, optimization algorithms are employed to identify the key parameters that enhance H₂ production. These optimal operating parameters are then used to create reference trajectory signals within a NPC scheme to achieve the optimal hydrogen production rate. The control strategy led to an HPR mean of 12.35 ± 1.2 NL H₂/L-d under pseudo-steady state with hydrogen content in the gaseous phase of 63 % v/v, and a maximum COD recovery of 90 ± 2.8 %. The results demonstrate that this innovative control method can significantly improve the performance and efficiency of biogas plants, showing viability for large-scale industrial implementation.

由于经济可行性和环境可持续性，利用低成本可再生基质进行暗发酵同时处理废水和制氢（H2）是合适的。这项工作探讨了一种创新的控制策略在规模发酵生物反应器中的应用，该反应器设计用于从有机废物中回收能量。这种方法不仅促进了低碳排放，而且具有巨大的工业应用潜力。利用机器学习（ML）和优化方法对非线性过程进行建模，然后开发了一种神经预测控制（NPC）策略，使系统在不同进水条件下达到最佳运行顺序。预测控制采用Newton-Raphson算法作为优化算法，采用多层前馈神经网络进行状态预测。该策略已被证明是实时控制应用的可行算法。首先，利用支持向量机（SVM）算法对连续暗发酵实验数据进行建模，进行响应预测，然后利用优化算法识别提高H2产率的关键参数。这些最佳操作参数随后用于在NPC方案中创建参考轨迹信号，以实现最佳产氢率。该控制策略在准稳态下的平均HPR为12.35±1.2 NL H2/L-d，气相氢含量为63% v/v，最大COD回收率为90±2.8%。结果表明，这种创新的控制方法可以显著提高沼气厂的性能和效率，具有大规模工业实施的可行性。

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引用次数: 0

Enhanced prediction of heating value of municipal solid waste using hybrid neuro-fuzzy model and decision tree-based feature importance assessment 利用混合神经模糊模型和基于决策树的特征重要性评价增强城市生活垃圾热值预测

Green Energy and Resources

Pub Date : 2025-02-01 DOI: 10.1016/j.gerr.2025.100119

Oluwatobi Adeleke , Obafemi O. Olatunji , Tien-Chien Jen , Iretioluwa Olawuyi

This study proposes a hybrid network of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA) to predict the higher heating value (HHV) of municipal solid waste (MSW). To enhance the robustness and accuracy of the model and optimize its ability to capture the complex non-linear relationship in the MSW dataset, eight membership functions (MF)-type of the grid partitioning (GP) clustering approach were tested. Moreover, understanding the relative importance and contribution of different waste properties to HHV prediction is critical for improving the model's predictive capability and optimizing the waste-to-energy (WTE) process. To this end, the feature importance analysis of MSW input variables was carried out using the decision tree regressor with the Gini importance (GI) metrics to identify the most influential variable. Key waste properties, including ultimate analysis data, ash and moisture content were used as input variables for the model. The result shows that the GP-clustered GA-ANFIS model based on triangular-shaped MF-type (tri-MF) has the most accurate HHV predictions with Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and Mean Absolute Deviation (MAD) values of 0.7642, 13.677, 1.5913 and 0.9821 at the training and 0.6364, 16.216, 1.2437 and 0.7821 at the testing stage. Feature importance assessment revealed ash content as the most important predictor of HHV based on GI-value of 0.519668 (about 50% cumulative importance). Additionally, sulphur and nitrogen, along with ash content, dominated the HHV prediction and exhibited the highest predictive power on HHV with about 80% cumulative importance. The robust integrated approach of hybrid neuro-fuzzy model, with decision tree-based feature importance assessment, offers an effective approach for enhancing the prediction of HHV of MSW. The outcome of the study enhances WTE systems, facilitating more efficient and sustainable energy recovery from MSW.

提出了一种基于自适应神经模糊推理系统（ANFIS）和遗传算法（GA）的混合网络预测城市生活垃圾的高热值（HHV）。为了提高模型的鲁棒性和准确性，并优化其捕获MSW数据集中复杂非线性关系的能力，对8种隶属函数（MF）类型的网格划分（GP）聚类方法进行了测试。此外，了解不同废物性质对HHV预测的相对重要性和贡献对于提高模型的预测能力和优化废物转化为能源（WTE）过程至关重要。为此，使用决策树回归器和基尼重要性（GI）指标对城市生活垃圾输入变量进行特征重要性分析，以确定最具影响力的变量。关键的废物性质，包括最终分析数据，灰分和水分含量被用作模型的输入变量。结果表明，基于三角形状mf型（tri-MF）的gp -聚类GA-ANFIS模型预测HHV最准确，训练阶段的平均绝对误差（MAE）、平均绝对百分比误差（MAPE）、均方根误差（RMSE）和平均绝对偏差（MAD）分别为0.7642、13.677、1.5913和0.9821，测试阶段的平均绝对偏差（MAD）分别为0.6364、16.216、1.2437和0.7821。特征重要性评价显示灰分含量是HHV最重要的预测因子，gi值为0.519668（累积重要性约为50%）。此外，硫、氮和灰分在HHV预测中占主导地位，对HHV的预测能力最高，累积重要性约为80%。基于决策树特征重要性评价的混合神经模糊模型鲁棒集成方法，为加强城市生活垃圾HHV预测提供了有效途径。研究结果有助改善废物处理系统，使都市固体废物的能源回收更有效率及可持续。

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引用次数: 0

Comparative analysis and normalization of single-hole vs. multi-hole spray characteristics: 1st report on spray characteristic comparison 单孔与多孔喷雾特性的比较分析与归一化：喷雾特性比较第一篇报告

Green Energy and Resources

Pub Date : 2025-02-01 DOI: 10.1016/j.gerr.2025.100120

Chang Zhai , Junyu Zhang , Kuichun Li , Pengbo Dong , Yu Jin , Feixiang Chang , Hongliang Luo

The single hole injector, known for its simple design and ease of measurement, is widely utilized in optical spray experiments; however, multi-hole injectors are commonly applied in real engine applications. The structural differences between the two leads to variations in spray characteristics. This series of studies, based on the principles of similarity and normalization, proposes a theory for the transformation of spray characteristics between different hole numbers injectors. The 1st report investigates the spray characteristics of different hole numbers injectors under super high injection pressure conditions. Using the Diffuser Background Imaging (DBI) method, the experimental pressure range covers 100∼300 MPa. The research indicate that the single-hole injector exhibits a shorter initial injection delay, while the multi-hole injector demonstrates a more stable injection flow rate and greater penetration. At higher pressures, the velocity increase, especially at 300 MPa. Higher ambient density has a suppressive effect on spray tip velocity and alters spray morphology. Moreover, it was observed that while the initial spray velocity of the single-hole injector is relatively higher, the penetration of the multi-hole injector significantly exceeds that of the single-hole injector in the later stages. For multi-hole injectors, interactions between adjacent sprays lead to a relatively narrower spray angle. The ratio of spray angle to cone angle for both injectors remain nearly unaffected by changes in density and injection pressure. In general, the Naber and Siebers model is better suited for predicting penetration in single-hole injectors under conditions of high density and ultra-high injection pressure (200∼300MPa). This study not only highlights the distinctive spray characteristics under super high pressure conditions but also offers valuable theoretical foundations and experimental insights for optimizing diesel engine design.

单孔喷射器以其设计简单、测量方便等优点在光学喷雾实验中得到广泛应用；然而，多孔喷油器通常应用于实际发动机应用中。两者之间的结构差异导致了喷雾特性的变化。这一系列研究基于相似性和归一化原理，提出了不同孔数喷射器之间喷雾特性转换的理论。第一篇研究了超高注入压力条件下不同孔数喷射器的喷射特性。采用扩散背景成像（DBI）方法，实验压力范围为100 ~ 300 MPa。研究表明，单孔喷油器的初始注入延迟较短，而多孔喷油器的注入流量更稳定，穿透能力更强。在更高的压力下，速度增加，特别是在300mpa时。较高的环境密度对喷雾速度有抑制作用，并改变了喷雾形态。此外，虽然单孔喷射器的初始喷射速度相对较高，但在后期，多孔喷射器的穿透力明显超过单孔喷射器。对于多孔喷射器，相邻喷雾之间的相互作用导致喷雾角相对较窄。两个喷射器的喷射角与锥角之比几乎不受密度和喷射压力变化的影响。一般来说，Naber和Siebers模型更适合在高密度和超高注入压力（200 ~ 300MPa）条件下预测单孔喷射器的侵透。该研究不仅突出了超高压工况下独特的喷淋特性，而且为柴油机优化设计提供了有价值的理论基础和实验见解。

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引用次数: 0

Parametric study of the decomposition of methane for COx-free H2 and high valued carbon using Ni-based catalyst via machine-learning simulation 基于机器学习模拟的ni基催化剂甲烷分解为无cox H2和高价碳的参数化研究

Green Energy and Resources

Pub Date : 2025-02-01 DOI: 10.1016/j.gerr.2025.100114

Dinghao Xue , Pingyang Zhang , Yuanyuan Lin , Wenshuo Wang , Jiachang Shi , Qiang Hu , Gartzen Lopez , Cristina Moliner , Jin Sun , Tao Wang , Xinyan Zhang , Yingping Pang , Xiqiang Zhao , Yanpeng Mao , Zhanlong Song , Ziliang Wang , Wenlong Wang

With industrial informatization, abundant data provides solutions for the digital design of methane-based hydrogen production. Catalytic methane decomposition (CMD) is a promising strategy for COx-free hydrogen production, with high-value carbon products generated. However, affected by various factors, the proper process parameters are challenge to be ascertained by the time-consuming experimental method. In this study, five machine learning methods were utilized for the precise prediction of methane conversion using Ni-based catalysts. Combined with SHAP method and univariate analysis method, XGBoost model with the best accuracy (with R² = 0.894, RSME = 7.724) was selected for the exploration of the reaction impact of active phase loading, support loading, and reaction conditions in methane convention, hydrogen production, carbon yield, and carbon quality. The result shows that methane conversion rate is mainly influenced by space velocity, reaction temperature, nickel loading, and methane percentage. Copper doping significantly affects carbon yield and its quality, and there is a strong bond between Ni and Al₂O₃, contributing the most to the reaction. This work would provide a guidance for the efficient catalyst design and effective hydrogen production.

随着工业信息化的发展，丰富的数据为甲烷制氢的数字化设计提供了解决方案。催化甲烷分解（CMD）是一种很有前途的无cox制氢策略，可以产生高价值的碳产品。然而，受各种因素的影响，通过耗时的实验方法难以确定合适的工艺参数。在本研究中，利用五种机器学习方法对镍基催化剂的甲烷转化进行了精确预测。结合SHAP方法和单变量分析方法，选择精度最佳的XGBoost模型（R2 = 0.894, RSME = 7.724），探讨活性相载荷、载体载荷和反应条件对甲烷含量、产氢量、产碳量和碳质量的影响。结果表明，甲烷转化率主要受空速、反应温度、含镍量和甲烷含量的影响。铜的掺杂对碳收率和碳质量有显著影响，而Ni和Al2O3之间存在很强的结合，对反应的贡献最大。本研究对高效催化剂的设计和高效制氢具有指导意义。

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引用次数: 0

Using machine learning methods for long-term technical and economic evaluation of wind power plants 利用机器学习方法对风力发电厂进行长期技术经济评估

Green Energy and Resources

Pub Date : 2025-02-01 DOI: 10.1016/j.gerr.2025.100115

Ali Omidkar, Razieh Es'haghian, Hua Song

The depletion of hydrocarbon reserves and the impact of global warming have posed significant challenges to the continued use of fossil fuels. Consequently, renewable energy sources have garnered substantial attention, with some countries now deriving a significant portion of their total energy needs from these alternatives. Among renewable sources, wind energy has been recognized as one of the most accessible and clean. However, it is imperative to evaluate wind power plants both technically and economically. This involves calculating the levelized cost of energy in comparison to fossil-based energy sources and predicting the minimum and maximum energy output over the long term. Achieving this requires long-term forecasts of wind speeds at specific locations, which involve complex mathematical modeling and computations typically performed by supercomputers. In this study, a data-driven machine learning model has been employed to predict wind speeds in Calgary over a 25-year period with minimal CPU time. Throughout the power plant's operational life, the optimal model was also used to calculate the annual energy production. The hybrid CNN-LSTM model demonstrated superior accuracy based on model accuracy metrics. Consequently, the levelized cost of energy produced by the plant was calculated at $0.09 per kWh, which is competitive within the Canadian electricity market. The investment reached a breakeven point in approximately six years, which is deemed acceptable.

碳氢化合物储量的枯竭和全球变暖的影响对化石燃料的持续使用构成了重大挑战。因此，可再生能源已引起相当大的注意，有些国家目前从这些替代能源中获得其总能源需求的很大一部分。在可再生能源中，风能被认为是最容易获得和最清洁的能源之一。然而，对风力发电厂进行技术和经济评估势在必行。这包括计算与化石能源相比的能源平化成本，并预测长期的最小和最大能源输出。实现这一目标需要对特定地点的风速进行长期预测，这涉及复杂的数学建模和计算，通常由超级计算机执行。在这项研究中，一个数据驱动的机器学习模型被用来预测卡尔加里25年期间的风速，而CPU时间最短。在电厂的整个运行周期内，利用最优模型计算年发电量。基于模型精度指标，CNN-LSTM混合模型显示出更高的精度。因此，该厂生产能源的平均成本计算为每千瓦时0.09美元，这在加拿大电力市场上具有竞争力。投资在大约六年的时间里达到了盈亏平衡点，这是可以接受的。

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引用次数: 0

Potentials and effects of electricity cogeneration via ORC integration in small-scale biomass district heating system 小型生物质区域供热系统中ORC集成热电联产的潜力和效果

Green Energy and Resources

Pub Date : 2025-02-01 DOI: 10.1016/j.gerr.2024.100113

Truong Nguyen , Leteng Lin

This study explores the potential and impact of electricity cogeneration using Organic Rankine Cycle (ORC) integrated with small-scale biomass boilers within district heating systems. An analysis is conducted on a 3 MW_th biomass-fired district heating plant in southern Sweden. Process monitoring data, collected over a one-year period from the plant, serves as the basis for simulation and analysis. The study examines operational changes and fuel usage at a local level, together with an extension to a regional scale considering both short-term and long-term energy system implications. The results show that integrating a 200 kW_e ORC unit with the existing boiler having a flue gas condenser is cost-optimal and could cogenerate approximately 1.1 GWh electricity annually, with a levelized electricity cost of €64.4 per MWh. This is equivalent to a system power-to-heat ratio of 7.5%. From a broader energy system perspective, this efficient integration could potentially reduce CO₂ emissions by 234∼454 tons per year when the saved energy locally is used to replace fossil fuels in the energy system, depending on how biomass is utilized and what type of fossil fuels are replaced. Increasing installed capacity of ORC unit to maximize electricity co-generation could result in a carbon abatement cost ranging from €204 to €79 per ton CO₂. This cost fluctuates depending on the installed capacity, operation of the ORC units, and prevailing electricity prices. The study highlights the trade-off between financial gains and CO₂ emission reductions, underscoring the complex decision-making involved in energy system optimization.

本研究探讨了在区域供热系统中使用有机朗肯循环（ORC）与小型生物质锅炉相结合的热电联产的潜力和影响。对瑞典南部一个3兆瓦的生物质燃烧区域供热厂进行了分析。从工厂收集了一年的过程监控数据，作为模拟和分析的基础。该研究审查了地方一级的业务变化和燃料使用情况，并考虑到短期和长期能源系统的影响，将其扩展到区域范围。结果表明，将200千瓦时的ORC机组与现有的带有烟气冷凝器的锅炉集成是成本最优的，每年可产生约1.1吉瓦时的电力，平均每兆瓦时的电力成本为64.4欧元。这相当于系统的功率热比为7.5%。从更广泛的能源系统的角度来看，如果将当地节省的能源用于替代能源系统中的化石燃料，这种有效的整合可能每年减少234 ~ 454吨二氧化碳排放，具体取决于如何利用生物质和替代哪种类型的化石燃料。增加ORC装置的装机容量以最大限度地实现热电联产，可能导致每吨二氧化碳的碳减排成本从204欧元到79欧元不等。这一成本根据装机容量、ORC机组的运行情况和现行电价而波动。该研究强调了经济收益与二氧化碳减排之间的权衡，强调了能源系统优化所涉及的复杂决策。

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