IF 9.4 1区工程技术 Q1 ENERGY & FUELS

Energy

Pub Date : 2025-12-30 DOI: 10.1016/j.energy.2025.139797

Bin Chen , Longyun Zhu , Lin Hu , Rui Zhang , Yue Wu , Heng Li , Xinji Wen , Yucheng Zhang , Kai Gao

Existing prediction-based energy management optimization methods struggle to capture characteristics of complex operating conditions like vehicle abrupt acceleration or traffic congestion, and cannot adjust prediction horizons to adapt to varying driving conditions. To address these issues, this paper proposes a novel energy management method for hybrid electric vehicles (HEVs) based on attention-enhanced long short-term memory (LSTM) and adaptive model predictive control (AMPC). First, an attention-enhanced LSTM is proposed to provide the load power demand prediction, where the self-attention mechanism (SAM) is integrated to improve the prediction accuracy under complex conditions by capturing key time-step features in temporal data. Then, a heuristic algorithm called the sparrow search algorithm (SSA) is introduced to dynamically adjust hyperparameters to improve the generalization capability of the attention-enhanced LSTM. Building on the load power demand prediction, a model predictive control (MPC) strategy with adaptive prediction horizons is further developed by integrating operating condition awareness through vehicle-to-everything (V2X) technology. Simulation results demonstrate that the proposed power demand prediction model reduces the root mean square error (RMSE) by 25.9% in comparison with the traditional LSTM. Compared to the energy management method using MPC with the fixed prediction horizon, the proposed power demand prediction model and energy management method with adaptive prediction horizon achieve a 1.4% reduction in operating costs per 100 km.

现有的基于预测的能量管理优化方法难以捕捉车辆突然加速或交通拥堵等复杂工况的特征，且无法调整预测范围以适应不同的驾驶条件。针对这些问题，提出了一种基于注意增强长短期记忆（LSTM）和自适应模型预测控制（AMPC）的混合动力汽车能量管理方法。首先，提出了一种基于注意力增强的LSTM方法来提供负荷需求预测，其中集成了自注意机制（SAM），通过捕获时间数据中的关键时间步长特征来提高复杂条件下的预测精度。然后，引入一种启发式算法——麻雀搜索算法（SSA）来动态调整超参数，以提高注意增强LSTM的泛化能力。在负荷电力需求预测的基础上，通过车联网（V2X）技术集成运行状态感知，进一步开发了具有自适应预测视野的模型预测控制（MPC）策略。仿真结果表明，与传统LSTM相比，所提出的电力需求预测模型的均方根误差（RMSE）降低了25.9%。与固定预测水平的MPC能源管理方法相比，提出的电力需求预测模型和自适应预测水平的能源管理方法每百公里运行成本降低1.4%。

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引用次数: 0

GeoMTNet: Multi-Task deep learning for investigating the impact of injection temperature on CO2 storage in saline aquifers GeoMTNet：用于研究注入温度对盐水含水层二氧化碳储存影响的多任务深度学习

IF 9.4 1区工程技术 Q1 ENERGY & FUELS

Energy

Pub Date : 2025-12-29 DOI: 10.1016/j.energy.2025.139840

Yongbin He , Jianming He , Guanglin Zhang , Jian Wang , Zhaobin Zhang , Shouding Li , Xiao Li

Given the critical importance of geological carbon storage in mitigating climate change, this study examines the influence of injection temperature on solubility and mineral trapping in saline aquifers using thermo-hydro-chemical (THC) simulations with the TOUGH code. To address the high computational cost of traditional simulations, we developed a lightweight deep learning model, GeoMTNet. Its shared encoder and dual-branch design enables unified extraction of global and local features and allows simultaneous prediction of CO₂ storage amounts and spatial distribution fields—capabilities beyond those of conventional image-based networks. Using grid and formation properties as inputs, GeoMTNet rapidly predicts solubility and mineral trapping with high accuracy (R² > 0.99 for amount; R² > 0.97 and SSIM >0.85 for distribution) and achieves about 30,000 × speedup over traditional simulations. Both simulations and deep learning predictions show that injection temperature strongly affects a near-wellbore zone. Lower temperatures enhance early (<10 years) solubility trapping but inhibit late (>50 years) mineral trapping, whereas higher temperatures inhibit early solubility trapping but markedly promote late mineral trapping. The deep learning model effectively captures and explains the influence of injection temperature on CO₂ storage dynamics. At 100 years, both dissolution storage and mineralization storage increase with higher injection temperatures, with mineralization storage exhibiting greater sensitivity. Therefore, higher injection temperatures are recommended in field operations to enhance safe storage amount, defined as the sum of dissolved and mineralized CO₂ storage amounts.

考虑到地质碳储存对减缓气候变化的重要性，本研究利用TOUGH代码进行热水化学（THC）模拟，研究了注入温度对咸水含水层溶解度和矿物捕获的影响。为了解决传统模拟的高计算成本问题，我们开发了一个轻量级的深度学习模型，GeoMTNet。它的共享编码器和双分支设计能够统一提取全球和局部特征，并允许同时预测二氧化碳储存量和空间分布领域，这些能力超出了传统的基于图像的网络。使用网格和地层属性作为输入，GeoMTNet快速预测溶解度和矿物捕获，精度很高（数量R2 >； 0.99，分布R2 >； 0.97, SSIM >0.85），比传统模拟提高了约30,000倍的速度。模拟和深度学习预测都表明，注入温度对近井筒区域有很大影响。较低温度促进早期（10年）溶解度捕获，但抑制晚期（50年）矿物捕获，而较高温度抑制早期溶解度捕获，但显著促进晚期矿物捕获。深度学习模型有效捕获并解释了注入温度对CO2储存动态的影响。在100年时，溶蚀储存和矿化储存随注入温度的升高而增加，其中矿化储存表现出更大的敏感性。因此，在现场作业中，建议提高注入温度，以提高安全储存量，即溶解和矿化CO2储存量的总和。

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引用次数: 0

In-depth understanding synergistic characteristics of heat-fluid-mass transport in packed bed with large-size hierarchically porous heat storage module: in-situ experiments and numerical simulations 大尺寸分层多孔储热模块填料床热-液-质协同输运特性的深入研究：原位实验与数值模拟

IF 9.4 1区工程技术 Q1 ENERGY & FUELS

Energy

Pub Date : 2025-12-29 DOI: 10.1016/j.energy.2025.139781

Liang Yao , Zhihui Wang , Nan He , Qicheng Chen , Guangxu Hu , Qila Sa , Binjian Nie

Modification strategies can significantly enhance the sintering resistance of calcium (Ca)-based materials on a laboratory-scale. However, achieving high thermal storage performance of modified materials in large reactors remains a challenging task due to mismatched heat–fluid–mass transfer, which limits the application of these materials across different scales. In this study, a three-dimensional network of connected flow channels was constructed using a hierarchically porous heat storage module, which facilitated the synergistic transport of heat, fluid, and mass in a large-scale reactor. This study explored the synergistic characteristics and coupling mechanism of heat–fluid–mass transport in the reactor by combining in-situ experiments and numerical simulations. The in-situ experimental findings revealed that the 3D-connected flow channel network improved synergistic transport of heat, mass, and fluid. Compared to the conventional powder packed bed, the average heat release power of the reactor with the packed hierarchically porous heat storage module increased by 479 %. Further, a multi-physical coupling model with modified mechanism function was established based on in-situ experimental data, and the relationship among temperature, gas distribution, and reaction process was clarified. Most importantly, a three-stage synergistic mechanism was uncovered, encompassing intrinsic kinetic control of the material, subsequent device-scale heat–fluid–mass coupling control, and ultimately reverting to the material's diffusion control. In-depth understanding of the three-stage synergistic transition mechanism provides the scientific basis for scale-up thermochemical energy storage reactor design.

在实验规模上，改性策略可以显著提高钙基材料的抗烧结性能。然而，由于热-流-质传递不匹配，改性材料在大型反应器中实现高储热性能仍然是一项具有挑战性的任务，这限制了这些材料在不同尺度上的应用。在这项研究中，使用分层多孔储热模块构建了一个三维连接的流动通道网络，促进了大型反应器中热量、流体和质量的协同传输。本研究通过现场实验和数值模拟相结合的方法，探讨了反应器内热-液-质输运的协同特性和耦合机理。现场实验结果表明，3d连接的流道网络改善了热量、质量和流体的协同传递。与传统的粉末填料床相比，采用分层多孔储热模块的反应器的平均放热功率提高了479%。基于现场实验数据，建立了修正机理函数的多物理耦合模型，明确了温度、气体分布与反应过程之间的关系。最重要的是，揭示了一个三阶段的协同机制，包括材料的内在动力学控制，随后的设备规模的热-流-质耦合控制，并最终恢复到材料的扩散控制。深入了解三段式协同过渡机理，为规模化热化学储能堆设计提供了科学依据。

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引用次数: 0

Improved soft actor-critic based health energy management strategy design for a dual-motor battery electric vehicle 基于软行为评价的改进双电机电动汽车健康能量管理策略设计

IF 9.4 1区工程技术 Q1 ENERGY & FUELS

Energy

Pub Date : 2025-12-29 DOI: 10.1016/j.energy.2025.139838

Wanli Yang , Xin Guo , Wei Dai

Battery electric vehicles (BEVs) require enhanced energy efficiency and safety, challenges addressable through advanced energy management strategy (EMS). This study presents an enhanced soft actor-critic (SAC) based EMS incorporating health-aware mechanisms to minimize vehicle power consumption while maximizing battery and motor longevity. To overcome temporal dependency limitations inherent in conventional SAC implementations, we propose a hybrid architecture integrating gated recurrent units (GRU) with mixed attention (MA) mechanisms. The GRU-MA synergy delivers three key advancements: (1) significantly accelerated convergence (57.8 % faster, 87 training episodes vs. baseline SAC's 206 episodes), attributed to superior computational efficiency from GRU's simplified gate structures compared to long short-term memory (LSTM)-based counterparts (SAC-LSTM: 143 episodes; SAC-GRU: 109 episodes); (2) optimal energy utilization achieving 7.88 kWh consumption, representing 2.3 % and 3.2 % improvements over SAC-LSTM-MA and twin delayed deep deterministic policy gradient (TD3)-GRU-SA strategies, respectively; (3) robust cross-domain adaptability, maintaining 97.93 % of the energy efficiency benchmark set by dynamic programming (DP) methods while preserving battery state-of-health (SOH) at 97.19 % and motor SOH at 97.83 % of DP benchmarks, surpassing deep deterministic policy gradient (DDPG)-based approaches by 3.6 %–4.8 % in stochastic driving cycles.

纯电动汽车（bev）需要提高能源效率和安全性，这些挑战可以通过先进的能源管理战略（EMS）来解决。本研究提出了一种增强的基于软行为者评价（SAC）的EMS，该EMS结合了健康意识机制，以最大限度地减少车辆功耗，同时最大限度地提高电池和电机的寿命。为了克服传统SAC实现中固有的时间依赖性限制，我们提出了一种将门控循环单元（GRU）与混合注意（MA）机制集成在一起的混合架构。GRU- ma协同带来了三个关键的进步：(1)显著加快了收敛速度（比基线SAC的206集快57.8%，87集训练集），这归功于GRU简化的门结构比基于长短期记忆（LSTM）的同类（SAC-LSTM: 143集，SAC-GRU: 109集）的计算效率更高；(2)最优能源利用达到7.88 kWh，分别比SAC-LSTM-MA和双延迟深度确定性策略梯度(TD3)-GRU-SA策略提高2.3%和3.2%；(3)鲁棒的跨域适应性，在随机驾驶循环中，保持动态规划（DP）方法设定的97.93%的能效基准，同时保持电池健康状态（SOH） 97.19%和电机SOH 97.83%的DP基准，比基于深度确定性策略梯度（DDPG）的方法高出3.6% - 4.8%。

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引用次数: 0

Optimizing design of heat exchangers based on targeted control of local thermal resistance ratio 基于局部热阻比目标控制的换热器优化设计

IF 9.4 1区工程技术 Q1 ENERGY & FUELS

Energy

Pub Date : 2025-12-29 DOI: 10.1016/j.energy.2025.139837

Dongyang Yan , Hongliang Chang , Guangyu Ma , Ting Ma , Weidong Li , Qiuwang Wang

Heat exchanger design is primarily based on lumped parameters method, employing an average thermal resistance approach. However, this conventional method often yields inaccurate results, thus necessitating excessive design margins. To overcome these limitations, a novel design methodology focusing on targeted control of the local thermal resistance ratio was proposed herein. Through systematic adjustment of the local thermal resistance ratio, this approach breaks away from the traditional uniform structure design, leading to the development of non-uniform heat exchangers. These non-uniform structures can achieve a significant improvement in the overall performance of heat exchangers.

The proposed methodology utilized a sectional calculation approach to modify the physical structure of each heat exchange unit, thereby enabling the attainment of desired local thermal resistance ratios. Furthermore, a comprehensive evaluation criterion was established to select the optimal non-uniform structural configuration. This design was applied to a Z-channel heat exchanger, utilizing supercritical helium as the working fluid. The results demonstrate that, under identical heat exchange conditions, the pressure drop could be reduced by 8% compared with conventional uniform structures. The non-uniform configuration exhibits greater uniformity in thermal resistance, which can either reduce heat exchanger resistance or enhance heat transfer performance.

换热器的设计主要基于集总参数法，采用平均热阻法。然而，这种传统的方法往往产生不准确的结果，因此需要过多的设计余量。为了克服这些限制，本文提出了一种新的设计方法，重点是局部热阻比的目标控制。这种方法通过系统地调整局部热阻比，打破了传统的均匀结构设计，导致了非均匀换热器的发展。这些非均匀结构可以显著改善换热器的整体性能。所提出的方法利用截面计算方法来修改每个热交换单元的物理结构，从而实现所需的局部热阻比。在此基础上，建立了非均匀结构构型的综合评价准则。该设计应用于z通道换热器，利用超临界氦作为工作流体。结果表明，在相同换热条件下，与传统均匀结构相比，压降可降低8%。非均匀结构表现出更大的热阻均匀性，既可以降低换热器阻力，也可以提高传热性能。

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引用次数: 0

Thermo-economic performance and operational strategies analysis of AA-CAES system integrated with low melting point molten salt TES 低熔点熔盐TES集成AA-CAES系统热经济性能及运行策略分析

IF 9.4 1区工程技术 Q1 ENERGY & FUELS

Energy

Pub Date : 2025-12-29 DOI: 10.1016/j.energy.2025.139789

Qianting Wang , Cancan Zhang , Yuting Wu

The high penetration of renewable energy in power generation necessitates the deployment of energy storage systems to ensure a reliable and uninterrupted power supply. Compressed air energy storage (CAES) presents an economically viable energy storage solution, while advanced adiabatic compressed air energy storage (AA-CAES) demonstrates wide promise due to fossil fuel independence. To address the low system efficiency caused by limited thermal energy storage (TES) temperature, this study proposes four distinct AA-CAES system configurations integrated with low melting point molten salt TES. A comprehensive thermo-economic evaluation is conducted to identify the optimal system configuration. Additionally, the dynamic behavior of key operating parameters throughout one complete operational cycle is analyzed. The results demonstrate that the system presents superior performance when the thermal energy storage and release system is arranged in an array configuration. The system achieves 18.67 %, 60.26 %, and 40.98 % improvements in round-trip efficiency (RTE), energy storage density (ESD), and heat utilization rate (η_h) compared to the conventional water-based TES system, respectively. Since high-temperature TES results in a relatively high exhaust temperature from the final-stage expander, the waste heat can be recovered by cogeneration. The performance of the molten-salt-based TES system under partial load and under different operation strategies during system maintenance is further discussed. This study provides theoretical guidance for the development of large-scale high-temperature thermal energy storage CAES systems and their engineering applications.

可再生能源在发电中的高渗透率要求部署储能系统以确保可靠和不间断的电力供应。压缩空气储能（CAES）是一种经济可行的储能解决方案，而先进的绝热压缩空气储能（AA-CAES）由于不依赖化石燃料而显示出广阔的前景。为了解决热能储存（TES）温度有限导致的系统效率低的问题，本研究提出了四种不同的AA-CAES系统配置，并与低熔点熔盐TES相结合。进行了全面的热经济评价，以确定最佳的系统配置。此外，还分析了关键运行参数在一个完整运行周期内的动态特性。结果表明，将储放热系统布置成阵列结构时，储放热系统具有较好的性能。与常规水基TES系统相比，该系统在往返效率（RTE）、储能密度（ESD）和热利用率（ηh）方面分别提高了18.67%、60.26%和40.98%。由于高温TES导致末级膨胀机的排气温度相对较高，废热可以通过热电联产回收。进一步讨论了熔融盐基TES系统在部分负荷和不同运行策略下的性能。该研究为大规模高温储热CAES系统的开发及其工程应用提供了理论指导。

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引用次数: 0

Navigating the future of energy storage: A data mining and raw material cost analysis of lithium-ion and emerging batteries 导航能源存储的未来：锂离子电池和新兴电池的数据挖掘和原材料成本分析

IF 9.4 1区工程技术 Q1 ENERGY & FUELS

Energy

Pub Date : 2025-12-29 DOI: 10.1016/j.energy.2025.139835

Scott Bayus, Donald McCleeary , Nancy Diaz-Elsayed

The market for lithium-ion batteries was 40.5 billion USD in 2020 and is expected to exceed 90 billion USD by 2030. There is a growing concern that as the applications for batteries increase, it will increase the demand for lithium carbonate leading to a surge in global production, thereby making the cost of lithium-ion battery chemistries prohibitive. Industry, governments, and researchers alike are placing great emphasis on researching alternatives to lithium-ion technologies, including solid-state, sodium-ion, zinc-air, and aluminum-ion batteries. This research conducts a cost analysis and examines key performance indicators through a text mining approach to compare alternative battery technologies to lithium-ion. Data mined for zinc-air battery technology reveals a promising mean gravimetric energy density of 666 Wh per kg, which surpasses that commonly reported for lithium-ion batteries. The raw material cost analysis shows that the range of raw material pricing for emerging batteries narrows as mass production is approached. It also shows that key raw materials (lithium and carbon) will continue to be employed in emerging battery technologies but in slightly different chemical forms. The advantages and disadvantages of the batteries are also discussed.

2020年锂离子电池市场规模为405亿美元，预计到2030年将超过900亿美元。人们越来越担心，随着电池应用的增加，对碳酸锂的需求将增加，导致全球产量激增，从而使锂离子电池化学物质的成本令人望而却步。工业界、政府和研究人员都非常重视研究锂离子技术的替代品，包括固态电池、钠离子电池、锌空气电池和铝离子电池。本研究进行了成本分析，并通过文本挖掘方法检验了关键性能指标，以比较替代电池技术与锂离子电池。对锌空气电池技术的数据挖掘显示，锌空气电池的平均重力能量密度有望达到每公斤666 Wh，超过了锂离子电池的通常报道。原材料成本分析显示，随着大规模生产的临近，新兴电池的原材料定价范围会缩小。它还表明，关键原材料（锂和碳）将继续用于新兴电池技术，但化学形式略有不同。本文还讨论了这些电池的优缺点。

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引用次数: 0

An improved thermal control strategy for raising energy efficiency and battery life in electric vehicles 一种改进的热控制策略，以提高电动汽车的能源效率和电池寿命

IF 9.4 1区工程技术 Q1 ENERGY & FUELS

Energy

Pub Date : 2025-12-29 DOI: 10.1016/j.energy.2025.139806

Yi Xie , Jiantao Ding , Wei Li , Bo Liu , Rui Yang , Kuining Li , Yangjun Zhang

This paper establishes a cooperative model of energy flow and heat flow for the integrated thermal management system (TMS) of electric vehicles (EVs). The energy flow model calculates the dynamic electrical characteristics of the battery based on the practical driving cycle of the vehicle, and sends to the heat flow model for the performance of the TMS and temperatures of battery and cabin. Then the cooperative model is validated against the experimental data to prove its high accuracy. The maximum calculation errors of the battery voltage, temperature and lifespan are 3.22 %, 2.56 % and 1.1 %, respectively, and maximum prediction error of the cabin temperature is 3.69 %. Based on the model, an intelligent control strategy is established for the integrated TMS. The algorithm is based on the MPC and integrates the neural network for vehicle speed prediction. The former can realize the quick cooling for battery and cabin and avoid great fluctuation in the temperatures of cabin and battery. Compared to the rule-based controller (prioritized controller), the MPC-based strategy can not only shorten the cooling time of the cabin and battery by about 2706 s and 2405 s, but also reduce the fluctuation in the cabin temperature and battery temperature shoot by 1.69 °C and 77 %. The latter can help the MPC reduce the fluctuation in the operation parameters of TMS by informing the car speed change to the MPC, save the energy and extend the battery lifespan. The maximum fluctuation in the cabin temperature achieved by the proposed VMPC algorithm 0.27 °C lower than that of the MPC. Moreover, the VMPC can reduce the energy consumption by 50.3 % and 16 % and raises the battery SOH by 0.06 % and 0.01 %, compared to the prioritized controller and traditional MPC.

建立了电动汽车综合热管理系统的能量流和热流协同模型。能量流模型根据车辆的实际行驶循环计算电池的动态电特性，并将TMS性能、电池和轿厢温度等信息发送给热流模型。然后通过实验数据验证了该合作模型的准确性。电池电压、温度和寿命的最大计算误差分别为3.22%、2.56%和1.1%，座舱温度的最大预测误差为3.69%。在此基础上，建立了集成TMS的智能控制策略。该算法以MPC为基础，结合神经网络进行车速预测。前者可以实现电池和舱段的快速冷却，避免舱段和电池温度的大波动。与基于规则的控制器（优先控制器）相比，基于mpc的策略不仅使客舱和电池的冷却时间分别缩短了2706 s和2405 s左右，而且使客舱温度和电池温度的波动分别减少了1.69℃和77%。后者通过将车速变化通知MPC，帮助MPC减小TMS运行参数的波动，节约能源，延长电池寿命。提出的VMPC算法实现的座舱温度最大波动比MPC算法小0.27℃。此外，与优先级控制器和传统MPC相比，VMPC可以降低50.3%和16%的能耗，提高电池SOH 0.06%和0.01%。

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引用次数: 0

Risk as opportunity: The incentive effect of green finance policy on risk-taking of energy firms 风险即机遇：绿色金融政策对能源企业风险承担的激励效应

IF 9.4 1区工程技术 Q1 ENERGY & FUELS

Energy

Pub Date : 2025-12-29 DOI: 10.1016/j.energy.2025.139811

Rouchen Li, Shuyang Wen, Shuyu Xue

This study explores the incentive effect of green finance policy on green transition of energy firms in China by focusing on the Green Finance Pilot Zones Policy (GFP policy). We developed a staggered difference-in-differences (DID) model and found that implementation of the GFP policy significantly increased energy firms’ risk-taking, which accelerated their green transition. Energy firms in green finance pilot zones exhibit improved financing capacity and increased investment level, eventually taking more risks and facilitating their green transition. We further found that energy firms facing higher financing constraints, non-state-owned energy firms, those with lower regulatory oversight, and located in less developed cities experience a more significant increase in risk-taking. Moreover, we found a significant improvement in green innovation and environmental performance of treated firms. This evidence highlights the necessity for financial backing and investment incentives through green finance policies to advance the green transition in the traditional energy sectors.

本文以绿色金融试验区政策为研究对象，探讨了绿色金融政策对中国能源企业绿色转型的激励效应。我们开发了一个交错差分差分（DID）模型，发现GFP政策的实施显著增加了能源企业的风险承担，从而加速了它们的绿色转型。绿色金融试验区的能源企业融资能力增强，投资水平提高，最终承担的风险更大，有利于绿色转型。我们进一步发现，面临较高融资约束的能源企业、非国有能源企业、监管程度较低的能源企业以及位于欠发达城市的能源企业，其风险承担能力的增加更为显著。此外，我们发现处理后的企业在绿色创新和环境绩效方面有显著改善。这一证据强调了通过绿色金融政策提供金融支持和投资激励的必要性，以推进传统能源部门的绿色转型。

引用次数: 0

Modelica-based dynamic modeling and exergy analysis of a heat pump drying system incorporating exhaust waste-heat recovery 基于modelica的废气余热回收热泵干燥系统动态建模与火用分析

IF 9.4 1区工程技术 Q1 ENERGY & FUELS

Energy

Pub Date : 2025-12-29 DOI: 10.1016/j.energy.2025.139834

Hailun Fu , Juan Shi , Sandro Nižetić , Li Sun

Heat pump drying (HPD) is a promising technology for reducing agricultural carbon emissions by efficiently replacing conventional fossil fuel combustion with electrification. Waste heat recovery (WHR) from the drying chamber exhaust can further enhance drying efficiency, although it introduces more complex dynamic coupling. In this study, dynamic models of key components were developed, and the effects of WHR on the dynamic characteristics of HPD systems were analyzed. The inclusion of WHR was found to induce an open-loop unstable state, necessitating control to maintain the rated drying temperature and superheat. Subsequently, the impacts of different control schemes on dynamic performance and exergy losses were investigated. The results indicate that introducing WHR reduces compressor power consumption and leads to an improvement in the average specific moisture extraction rate (SMER) of 0.36 kg/kWh. Using compressor speed to control drying temperature and expansion valve opening to control superheat yielded the highest SMER, the lowest total exergy loss, and optimal control of drying temperature, but the poorest superheat control. In contrast, using the expansion valve opening to control drying temperature and the evaporator fresh air flow to control superheat achieved optimal superheat regulation.

热泵干燥（HPD）是一种很有前途的技术，通过有效地取代传统的化石燃料燃烧与电气化来减少农业碳排放。干燥室废气余热回收（WHR）可以进一步提高干燥效率，但它引入了更复杂的动态耦合。本研究建立了关键部件的动态模型，分析了WHR对HPD系统动态特性的影响。发现WHR的加入会导致开环不稳定状态，需要控制以保持额定干燥温度和过热度。随后，研究了不同控制方案对动态性能和火用损失的影响。结果表明，引入WHR后，压缩机功耗降低，平均比吸湿率（SMER）提高0.36 kg/kWh。用压缩机转速控制干燥温度，用膨胀阀开度控制过热度，SMER最高，总火用损失最小，干燥温度控制最优，但过热度控制最差。采用膨胀阀开度控制干燥温度，蒸发器新风流量控制过热度，实现了最优过热度调节。

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引用次数: 0

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