Applied Energy最新文献_第8页

Study on the redistribution mechanism and secondary purge strategy of proton exchange membrane fuel cells 质子交换膜燃料电池的再分配机制和二次净化策略研究

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2024-11-04 DOI: 10.1016/j.apenergy.2024.124755

Tiancai Ma , Chang Du , Ruitao Li , Xingwang Tang , Jianbin Su , Liqin Qian , Lei Shi

Effective control of membrane water content is essential for increasing the space for ice formation during the cold start stage and enhancing the success rate of start-up. Shutdown purge can effectively lower the membrane water content following fuel cell operation. However, during the cooling and standing process after purge, the rapid change in saturated vapor pressure can result in the redistribution of membrane dissolved water, leading to an increase in its content and a reduction in the success rate of cold start. Therefore, this study establishes a multidimensional, multiphase simulation model to comprehensively and thoroughly analyze the redistribution mechanism after purging and investigates the relationship between membrane water content and cold start. This is achieved by identifying the maximum membrane water content boundary during the cold start process and ultimately improving the success rate of cold start through a secondary purge strategy. The research results indicate that the membrane water content of the fuel cell increases from 2.31 to 8.31 after redistribution. During the cold start stage, the cold start success of the fuel cell under different environmental temperatures exhibits relatively specific boundary conditions, with the cold start process being closely related to the load current density and initial membrane water content. After implementing the secondary purging strategy, the membrane water content of the fuel cell decreases again, displaying favorable cold start characteristics in the cold start stage and successfully starting at −10 °C. This study can provide a reliable basis for the development of purging strategies during shutdown and offer a theoretical foundation for the boundary identification process of cold start.

有效控制膜水含量对于增加冷启动阶段的结冰空间和提高启动成功率至关重要。关机吹扫可有效降低燃料电池运行后的膜含水量。然而，在吹扫后的冷却和静置过程中，饱和蒸汽压的快速变化会导致膜溶解水的重新分布，从而导致其含量增加，降低冷启动的成功率。因此，本研究建立了多维、多相模拟模型，全面、深入地分析了吹扫后的再分布机理，并研究了膜水含量与冷启动之间的关系。通过确定冷启动过程中膜含水量的最大边界，最终通过二次吹扫策略提高冷启动的成功率。研究结果表明，燃料电池的膜水含量在重新分配后从 2.31 增加到 8.31。在冷启动阶段，不同环境温度下燃料电池的冷启动成功率呈现出相对特定的边界条件，冷启动过程与负载电流密度和初始膜含水量密切相关。在实施二次吹扫策略后，燃料电池的膜含水量再次降低，在冷启动阶段表现出良好的冷启动特性，并在-10 °C时成功启动。这项研究可为关机期间吹扫策略的开发提供可靠依据，并为冷启动的边界识别过程提供理论基础。

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引用次数: 0

Methodology for comparative assessment of battery technologies: Experimental design, modeling, performance indicators and validation with four technologies 电池技术比较评估方法：实验设计、建模、性能指标和四种技术的验证

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2024-11-04 DOI: 10.1016/j.apenergy.2024.124757

Elisa Irujo, Alberto Berrueta, Pablo Sanchis, Alfredo Ursúa

An increasing number of applications with diverse requirements incorporate various battery technologies. Selecting the most suitable battery technology becomes a tedious task as several aspects need to be taken into account. Two of the key aspects are the battery characteristics under temperature variations and their degradation. While numerous contributions using tailored assessment methods to evaluate both aspects for a particular application exist in the literature, a general methodology for analysis is necessary to enable a quantitative comparison between different technologies. We propose in this paper a novel methodology, based on performance indicators, to quantify the potential and limitations of a battery technology for diverse applications sharing a similar operational profile. A quantification of phenomena such as the influence of high and low temperatures on the battery, or the effect of cycling and state of charge on battery aging is obtained. In pursuit of these indicators, an experimental procedure and the fitting of aging model parameters that allow their calculation are proposed. As an additional outcome of this work, a general aging model that allows comprehensive analysis of aging behavior is developed and the trade-off between experimental time and accuracy is analyzed to find an optimal experimental time between 2 and 4 months, depending on the studied battery technology. Finally, the proposed methodology is applied to four battery technologies in order to show its potential in a real case-study.

越来越多具有不同要求的应用采用了各种电池技术。选择最合适的电池技术是一项繁琐的工作，因为需要考虑多个方面。其中两个关键方面是温度变化下的电池特性及其降解。虽然文献中有许多文章采用量身定制的评估方法来评估特定应用的这两个方面，但有必要制定一种通用的分析方法，以便对不同技术进行定量比较。我们在本文中提出了一种基于性能指标的新方法，用于量化电池技术在不同应用中的潜力和局限性，这些应用具有相似的运行特征。我们对一些现象进行了量化，如高温和低温对电池的影响，或循环和充电状态对电池老化的影响。为了追求这些指标，我们提出了一种实验程序和老化模型参数拟合方法，以便对其进行计算。作为这项工作的额外成果，还开发了一个可全面分析老化行为的通用老化模型，并分析了实验时间和准确性之间的权衡，以根据所研究的电池技术，找到 2 至 4 个月之间的最佳实验时间。最后，将所提出的方法应用于四种电池技术，以展示其在实际案例研究中的潜力。

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引用次数: 0

Bi-level planning-operation model of PV considering reactive power capability 考虑无功功率能力的光伏双级规划-运行模型

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2024-11-03 DOI: 10.1016/j.apenergy.2024.124647

Ying Wang , Ying Chen , Xianyong Xiao , Yunzhu Chen , Qilin Li

The voltage violation is a significant issue in distribution networks, which impacts the operation of networks and users. The inverter-based photovoltaic (PV), which can be used for reactive power dispatch, may be a possible solution to address the issue, especially in the network with high PV penetration. However, there are three challenges for utilizing PV to improve voltage quality, including the planning, operation, and reactive power pricing of PV. Therefore, a bi-level planning-operation model of PV is proposed considering its reactive power capability. This study provides the following contributions. First, an upper planning level model is established, aiming to obtain the optimal planning scheme to effectively improve the economic benefits for PV investor and technical performances for distribution system operator. Second, a lower operation level model is developed to mitigate voltage violations and reduce network loss cost in multi-scenarios. Finally, a reactive power pricing model is designed, which is integrated into the upper planning level, to maximum the profit of all the participants. The simulation results on the modified IEEE 33-bus test system show that, the application of the proposed method guarantees the voltage for all the buses are within the qualified range, and increases the economic benefits for each participant by more than 7 % compared to conventional methods. This finding highlights the superior performances of the proposed method and its contribution to promote the sustainable development of renewable energy.

电压违规是配电网络中的一个重要问题，会影响网络和用户的运行。基于逆变器的光伏（PV）可用于无功功率调度，是解决这一问题的可行方案，尤其是在光伏渗透率较高的网络中。然而，利用光伏发电改善电压质量面临三个挑战，包括光伏发电的规划、运行和无功功率定价。因此，考虑到光伏的无功功率能力，提出了光伏的双级规划-运行模型。本研究有以下贡献。首先，建立了上层规划模型，旨在获得最优规划方案，从而有效提高光伏投资者的经济效益和配电系统运营商的技术性能。其次，建立了下层运行模型，以缓解多情景下的电压违规问题并降低网络损失成本。最后，设计了一个无功功率定价模型，并将其集成到上层规划中，以实现所有参与者的利润最大化。对修改后的 IEEE 33 总线测试系统的仿真结果表明，与传统方法相比，建议方法的应用保证了所有总线的电压都在合格范围内，并为每个参与者增加了 7% 以上的经济效益。这一结果凸显了所提方法的卓越性能及其对促进可再生能源可持续发展的贡献。

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引用次数: 0

Valorization of the by-product oxygen from green hydrogen production: A review 绿色制氢过程中副产品氧气的价值评估：综述

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2024-11-02 DOI: 10.1016/j.apenergy.2024.124817

Florentin Eckl , Ana Moita , Rui Castro , Rui Costa Neto

Green hydrogen is regarded as an important pillar in the sustainable energy system of the future. With a constantly increasing speed, new projects of every scale are producing the gas all over the world. Though, the process of electrolysis produces not only hydrogen but eight-times the amount of oxygen. Oxygen is crucial in many applications, however, the majority of the current green hydrogen projects neglect the value of the by-product. In this article, the state-of-the-art of different oxygen production technologies and the usage in today's economy are reviewed. Current studies and projects focusing on the usage of oxygen from electrolysis as well as an outlook on possible developments are presented. It demonstrates the advantages of the utilization of electrolytic oxygen and the increasing interest from governments and industry. A transition from traditional oxygen supply through cryogenic distillation and swing adsorption to electrolytic O₂ can be expected in various cases.

绿色氢气被视为未来可持续能源系统的重要支柱。随着速度的不断加快，各种规模的新项目正在世界各地生产这种气体。尽管在电解过程中产生的不仅是氢气，还有八倍于氢气的氧气。氧气在许多应用中都至关重要，但目前大多数绿色制氢项目都忽视了副产品的价值。本文回顾了不同制氧技术的最新发展以及在当今经济中的应用。文章介绍了当前以电解制氧为重点的研究和项目，并对可能的发展进行了展望。报告展示了利用电解氧气的优势，以及政府和行业日益增长的兴趣。在各种情况下，传统的低温蒸馏和摇摆吸附供氧方式有望向电解氧气过渡。

引用次数: 0

Collaborative optimization method for solving the diffusion and allocation issues in complex variable flow rate HVAC systems 解决复杂变流量暖通空调系统中扩散和分配问题的协同优化方法

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2024-11-02 DOI: 10.1016/j.apenergy.2024.124788

Jiaming Wang , Yacine Rezgui , Tianyi Zhao

The complexity of current optimization methods for HVAC systems is increasing, resulting in relatively lower computational efficiency, particularly in more complex systems. This difficulty makes real-time optimization and control challenging in practice. Therefore, there is an urgent need to simultaneously improve both system energy efficiency and computational efficiency to enhance system robustness. Present optimization methods predominantly emphasize enhancing system energy efficiency, often overlooking computational efficiency. Consequently, these methods become infeasible or unstable when implemented in practical systems. In our research, a multi-agent-based collaborative optimization method is proposed to solve the global optimization problem of complex HVAC systems. Under the multi-agent framework, the global optimization problem is decomposed into multiple sub-optimization problems considering the interaction characteristics among components, thus reducing the complexity of the global optimization problem in HVAC systems. The proposed AH-AFSA algorithm supports the solution of optimization problems containing hybrid decision variables (continuous and discrete variables) and can directly search for optimal discrete variables in the binary space. This feature is suitable for searching the optimal ON/OFF sequence and setpoints simultaneously during the global optimization process. The results demonstrate that the proposed method can save 18.9 % of electricity consumption with an average computing time of 12.2 s for each operating condition, saving about 54 % of the time cost compared to centralized methods. The methodology used in our research holds significant theoretical and practical value for enhancing the computational efficiency and productivity of optimization methods in complex HVAC systems.

当前暖通空调系统优化方法的复杂性不断增加，导致计算效率相对较低，尤其是在较为复杂的系统中。这种困难使得实时优化和控制在实践中面临挑战。因此，迫切需要同时提高系统能效和计算效率，以增强系统的鲁棒性。目前的优化方法主要强调提高系统能效，往往忽略了计算效率。因此，这些方法在实际系统中实施时会变得不可行或不稳定。在我们的研究中，提出了一种基于多代理的协同优化方法来解决复杂暖通空调系统的全局优化问题。在多代理框架下，全局优化问题被分解为多个子优化问题，并考虑了各组件之间的交互特性，从而降低了暖通空调系统全局优化问题的复杂性。所提出的 AH-AFSA 算法支持解决包含混合决策变量（连续和离散变量）的优化问题，并能直接在二进制空间中搜索最佳离散变量。这一特点适用于在全局优化过程中同时搜索最佳开/关顺序和设定点。结果表明，与集中式方法相比，所提出的方法在每个运行条件下的平均计算时间为 12.2 秒，可节省 18.9% 的电力消耗，节约了约 54% 的时间成本。我们研究中使用的方法对于提高复杂暖通空调系统中优化方法的计算效率和生产率具有重要的理论和实践价值。

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引用次数: 0

Cooperative game robust optimization control for wind-solar-shared energy storage integrated system based on dual-settlement mode and multiple uncertainties 基于双结算模式和多不确定性的风光互补储能集成系统的协同博弈鲁棒优化控制

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2024-11-02 DOI: 10.1016/j.apenergy.2024.124799

Xiaojuan Han , Zuran Wang , Haoyu Li , Muran Liu

Aiming at the problems of renewable energy output uncertainties and single scenario operation mode of energy storage systems, a cooperative game robust optimization control method for wind-solar-shared energy storage system based on dual-settlement mode of power market is proposed in this paper. A cooperative game-based energy management framework under dual settlement mode of electricity market is constructed, the profit relationship between shared energy storage under multiple application scenarios and renewable energy are extracted and the corresponding profit models are established. Considering the multiple uncertainties of renewable energy and electricity prices, combined with robust optimization theory, a multi-level two-stage robust optimization model is established to make optimal electricity trading decisions for renewable energy and shared energy storage. Additionally, the cooperative game robust optimization model is solved by i-C&CG algorithm. The effectiveness of proposed control method is verified through actual operating data of a certain power grid in China. The simulation results show that the cooperative game robust optimization model achieves the optimal operation of the wind-solar-shared energy storage system considering multiple uncertainties, which can improve the ability of the system to cope with the uncertainty risk and the reliability of the system.

针对可再生能源输出不确定性和储能系统单一场景运行模式等问题，本文提出了一种基于电力市场双结算模式的风光互补共享储能系统合作博弈鲁棒优化控制方法。构建了电力市场双结算模式下基于合作博弈的能源管理框架，提取了多应用场景下共享储能与可再生能源之间的收益关系，并建立了相应的收益模型。考虑到可再生能源和电价的多重不确定性，结合鲁棒优化理论，建立了多层次两阶段鲁棒优化模型，为可再生能源和共享储能做出最优电力交易决策。此外，还利用 i-C&CG 算法求解了合作博弈鲁棒优化模型。通过中国某电网的实际运行数据验证了所提控制方法的有效性。仿真结果表明，合作博弈鲁棒优化模型实现了风光互补共享储能系统在考虑多种不确定性因素下的最优运行，提高了系统应对不确定性风险的能力和系统的可靠性。

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引用次数: 0

Levenberg-Marquardt algorithm-based solar PV energy integrated internet of home energy management system 基于 Levenberg-Marquardt 算法的太阳能光伏发电集成家庭互联网能源管理系统

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2024-11-02 DOI: 10.1016/j.apenergy.2024.124407

Md. Rokonuzzaman , Saifur Rahman , M.A. Hannan , Mahmuda Khatun Mishu , Wen-Shan Tan , Kazi Sajedur Rahman , Jagadeesh Pasupuleti , Nowshad Amin

With the emergence of smart grids, the home energy management system (HEMS) has immense prospective to optimize energy usage and reduce costs in the residential sector. However, the challenges persist in effectively controlling power consumption, reducing energy expenses, enhancing resident comfort, and optimizing the coordination of renewable energy sources (RESs). In this study, a Levenberg-Marquardt (LM) algorithm-based solar PV integrated internet of home energy management system (IoHEMS) is developed. The LM algorithm has been chosen as it outperforms the other two artificial intelligence (AI) algorithms: Bayesian regularization (BR) and scaled conjugate gradient (SCG). With the setup of using 70 % of data for training, 15 % for validation, and 15 % for testing, the LM algorithm shows the regression of 0.999999, gradient of 7.8e⁻⁵, performance of 2.7133e⁻⁹, and the momentum parameter of 1e⁻⁷. When the trained data set converges to the optimal training results, the best validation performance is achieved after 1000 epochs with approximately zero mean squared error (MSE). The proposed system transforms a conventional home into a smart home by effectively managing four household appliances: Air conditioner (AC), water heater (WH), washing machine (WM), and refrigerator (ref.). The proposed model enables accurate switching functions of appliances and efficient grid-to-battery utilization, resulting in reduced peak-hour electricity tariffs. The proposed system incorporates internet of things (IoT) functionality with the HEMS, utilizing smart plug socket (SPS) and wireless sensor network (WSN) nodes. The proposed model also supports Bluetooth low energy (BLE) connectivity for offline operation. A customized android application, ‘MQTT dashboard’, allows consumers to monitor power usage, room temperature, humidity, moisture and home appliance status every 60 s intervals.

随着智能电网的出现，家庭能源管理系统（HEMS）在住宅领域优化能源使用和降低成本方面前景广阔。然而，在有效控制电力消耗、减少能源支出、提高居民舒适度以及优化可再生能源（RES）协调方面，挑战依然存在。本研究开发了基于莱文伯格-马夸特（LM）算法的太阳能光伏集成家庭互联网能源管理系统（IoHEMS）。之所以选择 LM 算法，是因为它优于其他两种人工智能（AI）算法：贝叶斯正则化（BR）和缩放共轭梯度（SCG）。在使用 70% 的数据进行训练、15% 的数据进行验证、15% 的数据进行测试的设置下，LM 算法的回归系数为 0.999999，梯度为 7.8e-5，性能为 2.7133e-9，动量参数为 1e-7。当训练数据集收敛到最佳训练结果时，1000 个历时后就能达到最佳验证性能，平均平方误差（MSE）约为零。该系统通过有效管理四种家用电器，将传统家庭转变为智能家居：空调 (AC)、热水器 (WH)、洗衣机 (WM) 和冰箱 (ref.)。所提出的模型可实现精确的电器开关功能和高效的电网-电池利用率，从而降低高峰时段的电费。拟议系统利用智能插头插座（SPS）和无线传感器网络（WSN）节点，将物联网（IoT）功能与 HEMS 结合在一起。建议的模型还支持蓝牙低能耗（BLE）连接，以实现离线操作。通过定制的安卓应用程序 "MQTT 仪表板"，消费者可以每隔 60 秒监测一次用电量、室内温度、湿度、水分和家用电器状态。

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引用次数: 0

A deep reinforcement learning-based charging scheduling approach with augmented Lagrangian for electric vehicles 基于增强拉格朗日的深度强化学习电动汽车充电调度方法

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2024-11-02 DOI: 10.1016/j.apenergy.2024.124706

Lun Yang , Guibin Chen , Xiaoyu Cao

The adoption of electric vehicles (EVs) is increasingly recognized as a promising solution to decarbonization, thereby large scales of EVs are integrated into transportation and power systems in recent years. The transportation and power systems' operation states largely influence EVs' patterns, introducing uncertainties into EVs' driving patterns and energy demand. Such uncertainties make it a challenge to optimize the operations of charging stations, which provide both charging and electric grid services such as demand responses. To handle this dilemma, this paper models the chargers' operation decisions as a constrained Markov decision process (CMDP). By synergistically combining the augmented Lagrangian method and soft actor-critic algorithm, a novel safe off-policy reinforcement learning (RL) approach is proposed in this paper to solve the CMDP. The actor-network is updated in a policy gradient manner with the Lagrangian value function. A double-critics network is adopted to estimate the action-value function to avoid overestimation bias synchronously. The proposed algorithm does not require a strong convexity guarantee of examined problems and is sample efficient. Comprehensive numerical experiments with real-world electricity prices demonstrate that our proposed algorithm can achieve high solution optimality and constraint compliance.

电动汽车（EVs）的采用被越来越多的人认为是一种有望实现脱碳的解决方案，因此近年来电动汽车被大规模地集成到交通和电力系统中。交通和电力系统的运行状态在很大程度上影响着电动汽车的模式，给电动汽车的驾驶模式和能源需求带来了不确定性。这种不确定性给充电站的运营优化带来了挑战，因为充电站既要提供充电服务，又要提供电网服务（如需求响应）。为解决这一难题，本文将充电站的运营决策建模为受约束马尔可夫决策过程（CMDP）。通过协同结合增强拉格朗日法和软演员批评算法，本文提出了一种新颖的安全非政策强化学习（RL）方法来解决 CMDP。通过拉格朗日值函数，以策略梯度方式更新演员网络。采用双批判网络来估算行动价值函数，以避免同步高估偏差。所提出的算法不要求所研究问题具有很强的凸性保证，并且具有很高的采样效率。用真实世界的电价进行的综合数值实验证明，我们提出的算法可以获得较高的最优解，并符合约束条件。

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引用次数: 0

Dynamic analysis to reduce the cost for fixed offshore wind energy turbines 通过动态分析降低固定式海上风能涡轮机的成本

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2024-11-02 DOI: 10.1016/j.apenergy.2024.124804

Yuxiang Ma , Rubo Zhao , Wenhua Zhao , Bing Tai , Guohai Dong

Offshore wind energy is the most promising marine renewable energy. To harness this type of energy, offshore wind farms are required. The main challenge in developing offshore wind energy is its high cost, necessitating studies to significantly reduce the cost. This study focuses on the optimization of their foundations, which account for over one third of the total cost. Current engineering practices rely on static analysis to calculate the responses of offshore wind turbines under extreme wave excitations, covering inherent uncertainty with a safety factor, often leading to excessively conservative designs. The physical processes associated with offshore wind turbine dynamics under extreme conditions - particularly in breaking waves - remain unclear, leading to overly conservative designs. To better understand the complex physical processes and explore the potential to reduce cost, a series of dynamic analyses is conducted here. The required monopile diameter based on dynamic analysis is found to be only three quarters of that from static analysis, potentially reducing steel consumption by 50 % and significantly lowering costs.

海上风能是最有前途的海洋可再生能源。为了利用这种能源，需要建立海上风电场。开发海上风能的主要挑战是成本高昂，因此有必要研究如何大幅降低成本。本研究的重点是优化占总成本三分之一以上的地基。目前的工程实践依靠静态分析来计算海上风力涡轮机在极端波浪激励下的响应，用安全系数来覆盖固有的不确定性，这往往导致设计过于保守。与极端条件下海上风力涡轮机动力学相关的物理过程--尤其是破浪--仍不清楚，导致设计过于保守。为了更好地理解复杂的物理过程并探索降低成本的潜力，本文进行了一系列动态分析。根据动态分析得出的所需单桩直径仅为静态分析得出的直径的四分之三，从而有可能将钢材消耗量减少 50%，并显著降低成本。

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引用次数: 0

Insights into the effect of various supports on hydrothermal liquefaction of food waste over iron-oxide nano-catalysts 各种支持物对氧化铁纳米催化剂上食物垃圾水热液化的影响的启示

IF 10.1 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2024-11-02 DOI: 10.1016/j.apenergy.2024.124808

Sayed Ahmed Ebrahim , Xin Jiang , Oltion Kodra , Martin Couillard , Elena A. Baranova , Devinder Singh

This work investigates the effect of supported iron-oxide nano-catalysts for hydrothermal conversion of food waste. The studied supports were Vulcan carbon (VC), CeO₂, ZSM-5 and amorphous SiO₂-Al₂O₃. Catalytic hydrothermal liquefaction experiments were carried out in a batch reactor at 16 MPa and 300 °C maintained for 1 h. Different fractions of Fe(0), Fe²⁺ and Fe³⁺ alter its tendency toward deoxygenation, hydrogenations and condensation reactions, which influence the bio-crude yield, elemental compositions, and energy recoveries. The fresh and spent catalysts were characterized using X-ray photoelectron spectroscopy, physisorption analysis, thermogravimetric analysis, transmission and scanning electron microscopy. It was found that the change in catalyst support influences HTL pathways and product compositions. The results reveal that the inclusion of FeO_x catalyst on Vulcan carbon, SiO₂-Al₂O₃ and ZSM-5 supports can increase the bio-crude yield by ~7–9 wt% compared to their FeO_x-free yields. The increase in bio-crude yield was associated with the decrease in the surface ratios of Fe³⁺/Fe²⁺ at the range of 0.8–1.6. In overall, catalysts that had higher tendencies in converting amines into oil-soluble compounds increased the bio-crude yield, while catalysts that promoted dehydration and decarboxylation route decreased the bio-crude yield. The maximum energy recovery in bio-crude was obtained using FeO_x/SiO₂-Al₂O₃ catalyst with values ~95 %. The deactivation of catalysts was associated with the increase in Ca and P poisonous elements on catalytic sites, which decreased the energy recovery of recycled FeO_x/SiO₂-Al₂O₃ to ~85 % after three cycles.

本研究探讨了支持性氧化铁纳米催化剂对食物垃圾水热转化的影响。所研究的支撑物有火神碳（VC）、CeO2、ZSM-5 和无定形 SiO2-Al2O3。不同的 Fe(0)、Fe2+ 和 Fe3+ 分量会改变其脱氧、加氢和缩合反应的倾向，从而影响生物原油的产量、元素组成和能量回收率。使用 X 射线光电子能谱、物理吸附分析、热重分析、透射和扫描电子显微镜对新鲜催化剂和废催化剂进行了表征。研究发现，催化剂载体的变化会影响 HTL 的路径和产物成分。研究结果表明，在 Vulcan 炭、SiO2-Al2O3 和 ZSM-5 载体上加入 FeOx 催化剂可使生物原油产量比不含 FeOx 的产量提高约 7-9 wt%。在 0.8-1.6 的范围内，生物原油产量的增加与 Fe3+/Fe2+ 表面比的降低有关。总的来说，更倾向于将胺转化为油溶性化合物的催化剂提高了生物原油产量，而促进脱水和脱羧路线的催化剂则降低了生物原油产量。使用 FeOx/SiO2-Al2O3 催化剂获得的生物原油能量回收率最高，达到约 95%。催化剂失活与催化位点上 Ca 和 P 有毒元素的增加有关，这使得再生 FeOx/SiO2-Al2O3 催化剂的能量回收率在三个循环后降至约 85%。

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引用次数: 0