Energy Conversion and Management-X最新文献

Artificial intelligence-based forecasting models for integrated energy system management planning: An exploration of the prospects for South Africa 基于人工智能的综合能源系统管理规划预测模型：南非前景探讨

IF 7.1 Q1 ENERGY & FUELS

Energy Conversion and Management-X

Pub Date : 2024-10-01 DOI: 10.1016/j.ecmx.2024.100772

Senthil Krishnamurthy , Oludamilare Bode Adewuyi , Emmanuel Luwaca , Mukovhe Ratshitanga , Prathaban Moodley

The regional energy demand for Southern Africa has been predicted to increase by ten to fourteen times between the years 2010 and 2070. Thus, to address the proliferation of energy demand, South Africa’s integrated resource plan, which includes using renewable energy sources to increase the electricity supply and reduce the country’s carbon footprint, has been formulated. However, integrating renewable power into the power grid brings different dynamics for the system operators, as renewable power sources are variable and uncertain. Thus, accurate demand and generation forecasting become critical to the safe operation and ensuring continuity of supply, as consumers require. Due to the complexity of the earth’s atmosphere, weather forecasting uncertainty, and region-specific criteria, traditional forecasting models are limited. Thus, Machine Learning, Deep Learning, and other artificial intelligence techniques are attractive possibilities for improving classical forecasting models. This study comprehensively reviewed relevant works on AI-based models for generation potential and load demand forecasting toward intelligent energy resource management and planning. The approach involved searching research databases and other sources for studies, reports, and publications on location-specific energy resource management using criteria such as demography, policy, and sociotechnical information. Consequently, the review study has highlighted how AI predictive analytics can enhance long-term energy resource potential and load forecasting toward improving electricity sector performance and promoting integrated energy system management implementation in South Africa.

据预测，从 2010 年到 2070 年，南部非洲的区域能源需求将增加 10 到 14 倍。因此，为了解决能源需求激增的问题，南非制定了综合资源计划，其中包括利用可再生能源来增加电力供应和减少国家的碳足迹。然而，由于可再生能源的可变性和不确定性，将可再生能源纳入电网给系统运营商带来了不同的动力。因此，准确的需求和发电预测对于安全运行和确保连续供电至关重要，这也是消费者的要求。由于地球大气层的复杂性、天气预报的不确定性以及特定地区的标准，传统的预测模型存在局限性。因此，机器学习、深度学习和其他人工智能技术是改进经典预报模型的诱人可能性。本研究全面回顾了基于人工智能的发电潜力和负荷需求预测模型的相关工作，以实现能源资源的智能管理和规划。研究方法包括利用人口、政策和社会技术信息等标准，搜索研究数据库和其他来源有关特定地点能源资源管理的研究、报告和出版物。因此，审查研究强调了人工智能预测分析如何加强长期能源资源潜力和负荷预测，从而提高南非电力部门的绩效并促进综合能源系统管理的实施。

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引用次数: 0

Novel energy efficient integration of chimney ventilation, liquid desiccant dehumidification, and evaporative cooling for humid climates 烟囱通风、液体干燥剂除湿和蒸发冷却的新型高效节能集成技术，适用于潮湿气候

IF 7.1 Q1 ENERGY & FUELS

Energy Conversion and Management-X

Pub Date : 2024-10-01 DOI: 10.1016/j.ecmx.2024.100755

Omar Allahham , Kamel Ghali , Nesreen Ghaddar

The chimney effect for driving passively building ventilation is effective in dry and moderate climates to introduce cool fresh air into the space. However, its application in hot humid climates is limited due to the high energy demands associated with dehumidifying and cooling outdoor air. Thus, a novel energy-efficient assistive system is proposed integrating a chimney-driven ventilation with liquid desiccant dehumidification membrane loop and indirect evaporative cooling. This system leverages natural buoyancy to supply ventilation airflow and uses potassium formate loops through semi-permeable membranes for effective dehumidification. The objective is to dehumidify and cool the induced outdoor air to the room conditions of

24^{°} C

and relative humidity between

40 %

and

60 %

at minimal energy consumption. Mathematical models were developed to simulate the heat and mass transfer processes in the air and liquid desiccant flows within the system components. The system was sized, and its operation was optimized using an advanced machine learning-genetic algorithm model for a typical office space in Beirut. During the summer, the chimney air flowrate ranged from

45 L / s

to

48 L / s

, and it was delivered at the target room conditions. The system saved around

350 k W h

of electrical energy during the summer months due to elimination of the need to treat ventilation air by room cooling system. This was equivalent to the total energy required to handle the ventilation load during the summer season and resulted in a saving of $50/month in the case study.

在干燥和温和的气候条件下，烟囱效应能有效地驱动被动式建筑通风，为空间引入凉爽的新鲜空气。然而，由于除湿和冷却室外空气所需的能耗较高，它在炎热潮湿气候中的应用受到限制。因此，我们提出了一种新型节能辅助系统，将烟囱驱动通风与液体干燥剂除湿膜循环和间接蒸发冷却整合在一起。该系统利用自然浮力提供通风气流，并通过半透膜使用甲酸钾回路进行有效除湿。其目的是以最低的能耗将室外空气除湿和冷却到 24°C 的室内环境和 40% 至 60% 的相对湿度。开发了数学模型来模拟系统组件内空气和液体干燥剂流动的传热和传质过程。利用先进的机器学习遗传算法模型，对贝鲁特典型办公空间的系统大小和运行情况进行了优化。在夏季，烟囱空气流量在 45 升/秒至 48 升/秒之间，并在目标室内条件下输送。由于无需使用室内冷却系统处理通风空气，该系统在夏季节省了约 350 千瓦时的电能。这相当于夏季处理通风负荷所需的总能量，在案例研究中每月可节省 50 美元。

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引用次数: 0

Optimizing hybrid renewable energy based automated railway level crossing in Bangladesh: Techno-economic, emission and sensitivity analysis 优化孟加拉国基于混合可再生能源的自动铁路平交道口：技术经济、排放和敏感性分析

IF 7.1 Q1 ENERGY & FUELS

Energy Conversion and Management-X

Pub Date : 2024-10-01 DOI: 10.1016/j.ecmx.2024.100744

Zia Ul Islam , M.S. Hossain Lipu , Tahia F. Karim , Abu M. Fuad , M.M. Naushad Ali , ASM Shihavuddin , Ahmed Al Mansur

The railway system in Bangladesh, particularly the level crossing system, needs significant advancements, including a shift towards using renewable energy to power these crossings. As a solution, this study proposes optimal hybrid systems powered by renewable energy on an automated railway level crossing system, which is reliable, efficient, and sustainable. The main contribution of this study is to introduce an optimal hybrid renewable energy-based automated railway level crossing system in Bangladesh, focusing on technical and economic evaluation, emissions, and sensitivity assessment using HOMER Pro. The proposed system examines the optimal outcome for a 1 kW vertical-axis wind turbine and a 0.440 kW photovoltaic system at five selected locations. The results reveal satisfactory net present cost values amounting to USD 8495, USD 8505, USD 8564, USD 8262, and USD 8357 for Narayanganj, Cox’s Bazaar, Noakhali, Dinajpur, and Rajshahi respectively. Moreover, HOMER Pro indicates that the photovoltaic-wind turbine–grid-connected model offers a lower Cost of Energy which is around 0.03 USD/kWh compared to other configurations. The Internal Rate of Return for the selected locations are 20 %, 33.7 %, 31.5 %, 5.2 %, and 4.6 % for Narayanganj, Cox’s Bazaar, Noakhali, Dinajpur, and Rajshahi, respectively. The developed structure is projected to have a payback period ranging from 4.57 to 13.29 years across the five selected locations. Furthermore, the proposed systems reduce greenhouse gas emissions by up to 44.23 % compared to a grid-only system. Additionally, sensitivity analysis is performed by varying wind speed and solar irradiation to emphasize the robustness of the proposed systems.

孟加拉国的铁路系统，尤其是平交道口系统，需要进行重大改进，包括转向使用可再生能源为这些道口供电。作为一种解决方案，本研究提出了在自动铁路平交道口系统上使用可再生能源供电的最佳混合系统，该系统可靠、高效且可持续。本研究的主要贡献是介绍孟加拉国基于可再生能源的最优混合自动铁路平交道口系统，重点关注技术和经济评估、排放以及使用 HOMER Pro 进行的敏感性评估。拟议系统在五个选定地点对 1 千瓦垂直轴风力涡轮机和 0.440 千瓦光伏系统的最佳结果进行了研究。结果显示，Narayanganj、Cox's Bazaar、Noakhali、Dinajpur 和 Rajshahi 的净现值分别为 8495 美元、8505 美元、8564 美元、8262 美元和 8357 美元，令人满意。此外，HOMER Pro 显示，与其他配置相比，光伏-风力涡轮机并网模式的能源成本较低，约为 0.03 美元/千瓦时。所选地点的内部收益率分别为 20%、33.7%、31.5%、5.2% 和 4.6%，分别位于 Narayanganj、Cox's Bazaar、Noakhali、Dinajpur 和 Rajshahi。据预测，在五个选定地点，所开发结构的投资回收期从 4.57 年到 13.29 年不等。此外，与纯电网系统相比，建议的系统最多可减少 44.23% 的温室气体排放。此外，还通过改变风速和太阳辐照度进行了敏感性分析，以强调拟议系统的稳健性。

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引用次数: 0

Electrical Performance and Degradation Analysis of Field-Aged PV Modules in Tropical Climates: A Comparative Experimental Study 热带气候下野外老化光伏组件的电气性能和退化分析：对比实验研究

IF 7.1 Q1 ENERGY & FUELS

Energy Conversion and Management-X

Pub Date : 2024-10-01 DOI: 10.1016/j.ecmx.2024.100719

Md. Imamul Islam , Mohd Shawal Bin Jadin , Ahmed Al Mansur , Talal Alharbi

Performance degradation is a prevalent phenomenon in solar photovoltaic systems globally, with varying aging profiles and effects depending on environmental and climatic conditions. This paper presents an extensive investigation of the electrical performance, aging mechanisms, and degradation analysis of field-aged PV modules under the tropical climate of Malaysia. Utilizing a combination of visual inspection, I-V curve measurement, and thermal imaging techniques, this research provides a comprehensive assessment of the electrical and thermal properties of field-aged PV modules from two locations in Malaysia over extended periods (8 years at UMPSA and 10 years at Pasir Mas). The multi-faceted approach of the study allows for a deeper understanding of the degradation process, offering insights into the causes and effects of higher current and lower voltage in aged modules. The study found the average annual degradation rates at UMPSA were 0.3% for open circuit voltage (

V_{oc}

), 0.23% for short circuit current (

I_{sc}

), 0.81% for maximum power (

P_{\max}

), and 0.35% for fill factor (FF) and at Pasir Mas, the average annual degradation rates were 1.124% for Voc, −0.166% for Isc, 1.276% for Pmax, and 0.43% for FF. The study also found that monocrystalline silicon (m-Si) panels experienced an average power degradation of 6.48%, while polycrystalline silicon (p-Si) panels showed a higher degradation of 12.76%. However, Thermal imaging revealed significant temperature variations across the modules, with hotspots reaching up to 11.2 °C above cooler areas in UMPSA panels and an even more pronounced 26.1 °C difference in Pasir Mas modules. These temperature disparities highlight the uneven heat distribution and potential performance issues in the panels. This research contributes to the understanding of PV module degradation in tropical climates and aligns with sustainable development, climate change mitigation efforts, and SDG 7 by promoting sustainable energy solutions.

性能退化是全球太阳能光伏系统的普遍现象，其老化情况和影响因环境和气候条件而异。本文对马来西亚热带气候下实地老化光伏组件的电气性能、老化机制和退化分析进行了广泛调查。这项研究结合使用了目视检查、I-V 曲线测量和热成像技术，对马来西亚两个地点的实地老化光伏组件的电气和热性能进行了全面评估，评估时间跨度较长（UMPSA 为 8 年，Pasir Mas 为 10 年）。这项研究采用了多方面的方法，有助于深入了解退化过程，深入了解老化组件电流增大和电压降低的原因和影响。研究发现，在 UMPSA，开路电压（Voc）的年平均衰减率为 0.3%，短路电流（Isc）为 0.23%，最大功率（Pmax）为 0.81%，填充因子（FF）为 0.35%；在 Pasir Mas，开路电压（Voc）的年平均衰减率为 1.124%，短路电流（Isc）为 -0.166%，最大功率（Pmax）为 1.276%，填充因子（FF）为 0.43%。研究还发现，单晶硅（m-Si）电池板的平均功率衰减率为 6.48%，而多晶硅（p-Si）电池板的衰减率更高，为 12.76%。然而，热成像技术显示组件的温度差异很大，在 UMPSA 面板中，热点温度比冷点温度高出 11.2 °C，而在 Pasir Mas 组件中，差异甚至达到 26.1 °C。这些温度差异凸显了电池板热量分布不均和潜在的性能问题。这项研究有助于了解热带气候下的光伏组件退化情况，并通过推广可持续能源解决方案，与可持续发展、减缓气候变化的努力以及可持续发展目标 7 保持一致。

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引用次数: 0

An Ecoflex-encapsulated interlayer-structured triboelectric nanogenerator for Sports activity monitoring 用于体育活动监测的 Ecoflex 封装层间结构三电纳米发电机

IF 7.1 Q1 ENERGY & FUELS

Energy Conversion and Management-X

Pub Date : 2024-10-01 DOI: 10.1016/j.ecmx.2024.100759

Haojie Yang, Zhongyang Xu, Zixuan Liu, Yifei Lu, Yonggeng Wei, Yong Shi

Incorporating self-powered devices with flexible sensors not only tackles the power obstacles linked to wearable electronics but also greatly broadens their capability and application fields. Triboelectric nanogenerator (TENG) have garnered significant interest in the wearable electronics sector because of their distinct ability to harness ambient energy. Several techniques have been suggested to improve the efficiency of TENG, but these strategies often result in higher intricacy and manufacturing expenses. This study presents an interlayer structure that efficiently increases the surface contact area of TENG, consequently improving their output performance. Using this structure, a cost-effective, highly sensitive, and easily manufacturable sensor called the Ecoflex-encapsulated interlayered triboelectric nanogenerator (EI-TENG), has been developed. The EI-TENG demonstrates superior performance compared to a monolayer TENG (M-TENG) of identical dimensions, exhibiting a 1.6-fold increase in voltage output, a fivefold enhancement in minimum measurement precision, and a 2.29-fold increase in sensitivity. In addition, the EI-TENG exhibits exceptional endurance, as it maintains a consistent output even after undergoing 10,000 cycles. Furthermore, it reliably functions under different temperature and humidity situations. The energy produced by the EI-TENG is adequate to power 45 LED lamps directly. The EI-TENG, when affixed to an athlete’s hand, is capable of sensing the distribution of pressure while dribbling and shooting a ball, which assists athletes in honing their methods and making necessary adjustments to their hand strength. This study not only demonstrates the creation of interlayer-structured TENG, which produces a high amount of electrical energy and is durable and stable in many environments, but also provides valuable information for the future advancement of affordable, self-sustaining electronic devices.

将自供电设备与灵活的传感器结合在一起，不仅能解决与可穿戴电子设备相关的电力障碍，还能大大拓宽其能力和应用领域。三电纳米发电机（TENG）因其利用环境能源的独特能力而在可穿戴电子设备领域备受关注。人们已经提出了几种提高 TENG 效率的技术，但这些策略往往会导致更高的复杂性和制造成本。本研究提出了一种夹层结构，可有效增加 TENG 的表面接触面积，从而提高其输出性能。利用这种结构，我们开发出了一种高性价比、高灵敏度且易于制造的传感器，即 Ecoflex 封装夹层三电纳米发生器（EI-TENG）。与相同尺寸的单层三电纳米发生器（M-TENG）相比，EI-TENG 表现出更优越的性能，电压输出提高了 1.6 倍，最低测量精度提高了 5 倍，灵敏度提高了 2.29 倍。此外，EI-TENG 还具有超强的耐用性，即使在经历 10,000 次循环后仍能保持稳定的输出。此外，它还能在不同的温度和湿度条件下可靠地工作。EI-TENG 产生的能量足以直接为 45 盏 LED 灯供电。EI-TENG 贴在运动员手上后，能够感知运球和投篮时的压力分布，从而帮助运动员磨练方法并对手部力量进行必要的调整。这项研究不仅展示了层间结构 TENG 的制作过程，这种 TENG 能够产生大量电能，并在多种环境中持久稳定地工作，而且还为未来开发经济实惠、可自我维持的电子设备提供了宝贵的信息。

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引用次数: 0

Enhancing thermal transport in chemically reacting nanoparticles using the energy source and Cattaneo-Christov heat flux model

IF 7.1 Q1 ENERGY & FUELS

Energy Conversion and Management-X

Pub Date : 2024-10-01 DOI: 10.1016/j.ecmx.2024.100807

Shazia Habib , Saleem Nasir , Zeeshan Khan , Abdallah Berrouk , Saeed Islam , Asim Aamir

Developing an effective heat exchange solvent remains one of the biggest hurdles facing industries today, as conventional fluids are unsatisfactory for effective heating and cooling. The purpose of this work is to analyze the heat transmission and flow behaviors of hybrid nanofluids based on Single-Walled Carbon Nanotubes and Multi-Walled Carbon Nanotubes in the context of thermal radiation on a porous surface and the Cattaneo–Christov heat flux model with Thomson and Troian boundary conditions. This investigation utilizes a unique computational framework that combines Morlet Wavelet Neural Networks with Hybrid Cuckoo Search Algorithm. This advanced stochastic computational framework can effectively handle various nonlinear models and produce accurate results. This scheme’s accurate and consistent convergence is established by analyzing its findings with a numerical approach, and statistical metrics for performance are utilized to validate it further. The recommended method exhibits exceptional accuracy and precision, showcasing the hybrid nanofluid’s remarkable heat transmission attributes and thermal conductivity. The Mean squared error values range from 10⁻⁰¹ to 10⁻⁰⁵. The Fitness values fall within the interval 10⁰-10⁻⁰⁶, whereas the range of Error in Nash Sutcliffe efficiency lies between 10⁻⁰² and 10⁻⁻⁰⁸. The important and intriguing feature of this remarkable work is that, for all parameters examined, the heat transfer rate rises with minimal measurement of errors, consistent with the core objective of applying nanofluids to nanotechnology for their prospective implications.

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引用次数: 0

Predictive control technique for solar photovoltaic power forecasting 太阳能光伏发电功率预测的预测控制技术

IF 7.1 Q1 ENERGY & FUELS

Energy Conversion and Management-X

Pub Date : 2024-10-01 DOI: 10.1016/j.ecmx.2024.100768

Nsilulu T. Mbungu , Safia Babikir Bashir , Neethu Elizabeth Michael , Mena Maurice Farag , Abdul-Kadir Hamid , Ali A. Adam Ismail , Ramesh C. Bansal , Ahmed G. Abo-Khalil , A. Elnady , Mousa Hussein

An accurate estimation of photovoltaic (PV) power production is crucial for organizing and regulating solar PV power plants. The suitable prediction is often affected by the variable nature of solar resources, system location and some internal/external disturbances, such as system effectiveness, climatic factors, etc. This paper develops a novel strategy for applying a predictive control technique to PV power forecasting applications in a smart grid environment. The strategy develops the model predictive control (MPC) under demand response (DR) and some data-driven methods. It has been found that it is challenging to model an MPC for solar power forecasting regardless of its robustness and ability to handle constraints and disturbance. Thus, an optimal quadratic performance index-based MPC scheme is formulated to model a forecasting method for a PV power prediction. This strategy is then compared with some machine learning approaches. The developed strategies solve the problem of accurately estimating the direct current (DC) power yielded from the PV plant in a real-world implementation. The study also considers external disturbances to evaluate the significance of the developed methods for a suitable forecast. Therefore, this study optimally demonstrates that an accurate solar PV DC power prediction can relatively be estimated with an appropriate strategy, such as MPC and MLs, considering the system disturbances. This study also offers promising results for intelligent and real-time energy resource estimation that assist in developing the solar power sector.

准确估算光伏发电量对于组织和调节太阳能光伏发电站至关重要。合适的预测往往受到太阳能资源的可变性、系统位置和一些内部/外部干扰的影响，如系统效率、气候因素等。本文针对智能电网环境下的光伏功率预测应用，开发了一种应用预测控制技术的新策略。该策略开发了需求响应（DR）下的模型预测控制（MPC）和一些数据驱动方法。研究发现，无论 MPC 的鲁棒性以及处理约束和干扰的能力如何，为太阳能功率预测建模都具有挑战性。因此，本文提出了一种基于二次性能指标的最优 MPC 方案，以模拟光伏发电功率预测方法。然后将该策略与一些机器学习方法进行了比较。所开发的策略解决了在实际应用中准确估算光伏电站直流（DC）发电量的问题。本研究还考虑了外部干扰，以评估所开发方法对适当预测的意义。因此，本研究以最佳方式证明，在考虑系统干扰的情况下，采用适当的策略（如 MPC 和 ML）可以相对准确地预测太阳能光伏直流电功率。本研究还为智能和实时能源资源估算提供了有前景的结果，有助于太阳能发电行业的发展。

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引用次数: 0

Optimization of biodiesel production from Croton Macrostachyus seed oil with calcium oxide (CaO) catalyst and Characterization: Potential assessment of seed and kernel 用氧化钙（CaO）催化剂从巴豆籽油中生产生物柴油的优化和表征：种子和果仁的潜力评估

IF 7.1 Q1 ENERGY & FUELS

Energy Conversion and Management-X

Pub Date : 2024-10-01 DOI: 10.1016/j.ecmx.2024.100791

Adem Siraj Mohammed , Venkata Ramaya Ancha , Samson Mekbib Atnaw

This study investigates the use of Croton macrostachyus (CMS), a widely available, fast-growing, non-edible plant common in Ethiopia’s secondary forests and less productive lands, as a biodiesel feedstock. The research compared oil yields from the seed and kernel, assessed the oxidation stability of the resulting biodiesel, and conducted transesterification using a novel combination of a CaO catalyst with methanol, which had not been explored in previous studies. Oil yield was obtained 40.8 % from the seed and 50.3 % from the kernel. Calcium oxide (CaO) was derived from eggshells through a calcination process and characterized using BET, SEM-EDX, and FTIR techniques to assess its suitability as a heterogeneous catalyst for biodiesel production. The Box-Behnken design (BBD) based on the response surface methodology (RSM) was used to optimize the reaction parameters, including reaction time (60–120 min), methanol to oil molar ratio (4:1 – 12:1), and catalyst concentration (1 % – 5 %), while the reaction temperature (60 °C) and stirring speed (450 rpm) were kept constant. Analysis of variance (ANOVA) showed that the coefficients of determination (R² and R² adj) were 99.42 % and 98.38 %, respectively, indicating a strong correlation between experimental and predicted values. The optimum conditions identified for maximum yield of CMS biodiesel (98.314 %) were a methanol to oil molar ratio of 8.44:1, a catalyst concentration of 2.78 %, and a reaction time of 108 min. The oxidation stability of the CMS biodiesel was obtained an induction time of 2.3 h but was enhanced to 16.17 h due to the addition of TBHQ antioxidant. All physiochemical properties were compared with ASTM D6751 and EN 14214 biodiesel standards, demonstrating that the biodiesel produced is suitable for internal combustion engine applications.

Croton macrostachyus（CMS）是一种广泛存在于埃塞俄比亚次生林和生产力较低土地上的快速生长的非食用植物，本研究调查了这种植物作为生物柴油原料的用途。该研究比较了种子和果核的产油量，评估了所得生物柴油的氧化稳定性，并使用 CaO 催化剂与甲醇的新型组合进行了酯交换反应，这是以前的研究从未探讨过的。种子和果核的产油率分别为 40.8% 和 50.3%。通过煅烧工艺从蛋壳中提取了氧化钙（CaO），并使用 BET、SEM-EDX 和傅立叶变换红外技术对其进行了表征，以评估其作为生物柴油生产的异质催化剂的适用性。在反应温度（60 °C）和搅拌速度（450 rpm）保持不变的情况下，采用基于响应面方法（RSM）的盒-贝肯设计（BBD）优化反应参数，包括反应时间（60-120 分钟）、甲醇-油摩尔比（4:1-12:1）和催化剂浓度（1 %-5 %）。方差分析（ANOVA）显示，测定系数（R2 和 R2 adj）分别为 99.42 % 和 98.38 %，表明实验值和预测值之间具有很强的相关性。甲醇与油的摩尔比为 8.44:1，催化剂浓度为 2.78%，反应时间为 108 分钟，这些条件确定了 CMS 生物柴油最高产量（98.314%）的最佳条件。CMS 生物柴油的氧化稳定性诱导时间为 2.3 小时，但由于添加了 TBHQ 抗氧化剂，氧化稳定性提高到了 16.17 小时。所有理化性质均与 ASTM D6751 和 EN 14214 生物柴油标准进行了比较，表明所生产的生物柴油适用于内燃机应用。

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引用次数: 0

Green tribology assessment: A Comprehensive review of bio-lubricants and nano enhancers 绿色摩擦学评估：生物润滑剂和纳米增强剂综述

IF 7.1 Q1 ENERGY & FUELS

Energy Conversion and Management-X

Pub Date : 2024-10-01 DOI: 10.1016/j.ecmx.2024.100794

Amirmahdi Rahmani, Hesam Khadem Razavi, Masoud Dehghani-Soufi

The pursuit of sustainable lubrication solutions has led to significant research efforts in the development of bio-lubricants, aligned with green tribology principles, to mitigate environmental impact and enhance industrial efficiency. Nonetheless, the major setbacks such as food competing for the same vegetable oils resources, high costs of sourcing the raw materials, and difficulties in the application of nano additives have limited the extent of bio-lubricants usage. In this review, recent advancements in bio-lubricant production, focusing on chemical modifications to improve the thermal stability, oxidation resistance, and performance characteristics of vegetable oils are explored. Additionally, using nanoparticles as nano enhancers in bio-lubricants is examined for their potential to reduce friction and wear. The tribological performance of nano enhanced bio-lubricants is evaluated, highlighting their effectiveness in enhancing viscosity, reducing friction coefficients, and improving wear resistance. This paper also argues that it is necessary to improve the purity and dispersion of bio-lubricants to make them more economically viable for the performance. Finally, the incorporation of bio-lubricants with petroleum-based alternatives and implementation of eco-friendly nanomaterials is suggested to foster sustainable development in lubricant manufacturing without compromising economic aspects.

对可持续润滑解决方案的追求促使人们大力研发符合绿色摩擦学原理的生物润滑剂，以减轻对环境的影响并提高工业效率。尽管如此，生物润滑剂在使用过程中也遇到了一些主要问题，如食品对相同植物油资源的竞争、原材料采购成本高昂以及纳米添加剂的应用困难等，这些都限制了生物润滑剂的使用范围。本综述探讨了生物润滑油生产的最新进展，重点是通过化学改性来提高植物油的热稳定性、抗氧化性和性能特征。此外，还研究了在生物润滑油中使用纳米颗粒作为纳米增强剂以减少摩擦和磨损的潜力。本文对纳米增强型生物润滑剂的摩擦学性能进行了评估，强调了它们在提高粘度、降低摩擦系数和改善耐磨性方面的功效。本文还认为，有必要提高生物润滑剂的纯度和分散性，使其性能更具经济可行性。最后，本文建议将生物润滑剂与石油基替代品相结合，并采用生态友好型纳米材料，以促进润滑剂制造的可持续发展，同时又不影响经济效益。

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引用次数: 0

Impacts of digitalization on smart grids, renewable energy, and demand response: An updated review of current applications 数字化对智能电网、可再生能源和需求响应的影响：当前应用的最新回顾

IF 7.1 Q1 ENERGY & FUELS

Energy Conversion and Management-X

Pub Date : 2024-10-01 DOI: 10.1016/j.ecmx.2024.100790

Mou Mahmood , Prangon Chowdhury , Rahbaar Yeassin , Mahmudul Hasan , Tanvir Ahmad , Nahid-Ur-Rahman Chowdhury

Decarbonization, decentralization, and digitalization are essential for advanced energy systems (AES), which encompass smart grids, renewable energy integration, and demand response initiatives. Digitalization is a significant trend that transforms societal, economic, and environmental processes globally. This shift moves us from traditional power grids to decentralized, intelligent networks that enhance efficiency, reliability, and sustainability. By integrating data and connectivity, these technologies optimize energy production, distribution, and consumption. This article presents a comprehensive literature review of four closely related emerging technologies: Artificial Intelligence (AI), Internet of Things (IoT), Blockchain, and Digital Twin (DT) in AES. Our findings from the previous works indicate that AI significantly improves Demand Response strategies by enhancing the prediction, optimization, and management of energy consumption. Techniques like linear regression effectively predict power demand and aggregated loads, while more complex methods such as Support Vector Regression (SVR) and reinforcement learning (RL) optimize appliance scheduling and load forecasting. The integration of IoT technologies into Energy Management Systems (EMS) further enhances efficiency and sustainability through real-time monitoring and automated control. Additionally, DT technology aids in simulating energy scenarios and optimizing consumption in both residential and commercial smart grids. Our findings also emphasize blockchain’s role in creating decentralized energy trading platforms, facilitating peer-to-peer transactions, and enhancing trust through smart contracts. The insights gained from this review highlight the essential role of these emerging technologies in supporting decentralized, intelligent energy networks, offering valuable strategies for stakeholders to navigate the complexities of the evolving digital energy landscape.

去碳化、分散化和数字化对先进能源系统（AES）至关重要，其中包括智能电网、可再生能源集成和需求响应措施。数字化是改变全球社会、经济和环境进程的重要趋势。这一转变使我们从传统的电网转向分散的智能网络，从而提高了效率、可靠性和可持续性。通过整合数据和连接性，这些技术优化了能源生产、分配和消费。本文对四种密切相关的新兴技术进行了全面的文献综述：人工智能（AI）、物联网（IoT）、区块链和 AES 中的数字孪生（DT）。前人的研究结果表明，人工智能通过加强能源消耗的预测、优化和管理，极大地改进了需求响应策略。线性回归等技术能有效预测电力需求和总负荷，而支持向量回归（SVR）和强化学习（RL）等更复杂的方法则能优化设备调度和负荷预测。将物联网技术集成到能源管理系统（EMS）中，可通过实时监控和自动控制进一步提高效率和可持续性。此外，DT 技术还有助于模拟能源情景，优化住宅和商业智能电网中的能源消耗。我们的研究结果还强调了区块链在创建去中心化能源交易平台、促进点对点交易以及通过智能合约增强信任方面的作用。从本综述中获得的见解强调了这些新兴技术在支持去中心化、智能化能源网络中的重要作用，为利益相关者提供了有价值的战略，以应对不断发展的数字能源领域的复杂性。

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