Energy Conversion and Management最新文献

{"title":"Spatiotemporal forecasting using multi-graph neural network assisted dual domain transformer for wind power","authors":"Guolian Hou ,&nbsp;Qingwei Li ,&nbsp;Congzhi Huang","doi":"10.1016/j.enconman.2024.119393","DOIUrl":"10.1016/j.enconman.2024.119393","url":null,"abstract":"<div><div>Accurate prediction of wind power generation is crucial for operational and maintenance decision in wind farms. With the increasing scale and capacity of turbines, incorporating both temporal and spatial characteristics has become essential to improve prediction accuracy. In this paper, a novel spatiotemporal multi-step wind power forecasting method using multi-graph neural network assisted dual domain Transformer is proposed. Specifically, to adequately represent the heterogeneous dependencies among wind turbines, multi-relational graphs are constructed and integrated into a unified graph via attention mechanisms. Subsequently, the spatiotemporal fusion module (STFM) is developed using graph convolutional network and one-dimensional convolutional neural network to capture temporal and spatial features simultaneously. Moreover, the time–frequency dual domain Transformer (DDformer) is devised to fully utilize the information extracted by the STFM. Sequence learning in DDformer is performed through three perspectives, including multi-head self-attention mechanism, intrinsic mode function attention mechanism, and residual connection. Finally, the comprehensive evaluation metrics are formulated to assess the overall performance of wind power forecasting at both individual turbine and entire farm levels. Extensive simulations on a real-world dataset are conducted for multi-step forecasting, covering time horizons ranging from 10 min to 6 h ahead. In the case study, the proposed method consistently outperformed advanced benchmarks and ablation models, achieving average comprehensive normalized mean absolute error and normalized root mean square error of 5.8469% and 8.9461%, respectively, with improvements of 38.35% and 33.72%. Overall, the effectiveness of multi-step forecasting makes this study provide valuable insights into a new framework for wind power forecasting.</div></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":"325 ","pages":"Article 119393"},"PeriodicalIF":9.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143147694","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Strategic economic and energy analysis of integrated biodiesel production from waste cooking oil","authors":"Peng Yang ,&nbsp;Qiang Chen ,&nbsp;Wei Xu ,&nbsp;Yanghao Jin ,&nbsp;Yunjuan Sun ,&nbsp;Junming Xu","doi":"10.1016/j.enconman.2024.119354","DOIUrl":"10.1016/j.enconman.2024.119354","url":null,"abstract":"<div><div>The utilization of waste cooking oil to produce biodiesel is critical for advancing towards carbon neutrality. This study examines the production of first- and second-generation biodiesel from waste cooking oil, highlighting the transition from first-generation biodiesel, which achieved high purity and yield, to second-generation biodiesel through a hydrodeoxygenation-hydroisomerisation process. The first-generation process demonstrated high efficiency, with a biodiesel purity of 97.8 wt% and a yield of 99.88 wt%. However, the need for more sustainable and higher-quality fuel led to the development of a second-generation process, which, despite lower yield (69.06 wt%), produced biodiesel with 99.99 wt% purity. The energy optimization strategies employed showed a potential of 18.92% energy saving for reducing production costs and enhancing economic feasibility. This research underscores the importance of improving energy efficiency and cost-effectiveness in biodiesel production, particularly in transitioning from first- to second-generation biodiesel, which is crucial for meeting environmental and economic goals.</div></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":"325 ","pages":"Article 119354"},"PeriodicalIF":9.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143148096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Refining the black-box AI optimization with CMA-ES and ORM in the energy management for fuel cell electric vehicles","authors":"Jincheng Hu ,&nbsp;Jihao Li ,&nbsp;Ming Liu ,&nbsp;Yanjun Huang ,&nbsp;Quan Zhou ,&nbsp;Yonggang Liu ,&nbsp;Zheng Chen ,&nbsp;Jun Yang ,&nbsp;Jingjing Jiang ,&nbsp;Yuanjian Zhang","doi":"10.1016/j.enconman.2024.119399","DOIUrl":"10.1016/j.enconman.2024.119399","url":null,"abstract":"<div><div>Fuel cell electric vehicles (FCEVs) represent a significant advancement in zero-emission green mobility. By integrating deep reinforcement learning (DRL) for multi-objective energy management strategies, they unlock substantial potential for efficient and sustainable driving. However, the black-box nature of DRL and the challenges in designing multi-objective reward functions pose optimization difficulties. In this paper, we propose to an adaptive evolutionary framework to enhance DRL-based energy management strategies (EMS) by employing the covariance matrix adaptation evolutionary strategies (CMA-ES) for effective black-box optimization. By implementing an opponent reference mechanism, a self-balanced reward function for multiple optimization targets, including vehicle dynamics, powertrain economy, and more, is constructed in the proposed approach. This allows the system to automatically weigh sub-optimization targets and learn superior energy management behaviour via numerous simulation trajectories. The processor-in-the-loop (PIL) test results demonstrate that the proposed solution responds to adaptive adjustment conditions without violating any safety constraints, reduces energy consumption by at least 18.4%, and greatly improves energy utilization efficiency and safety. It exhibits promising optimality in complex energy management problems and robustness to varying velocity profiles, delivering a significant performance advantage over baseline approaches.</div></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":"325 ","pages":"Article 119399"},"PeriodicalIF":9.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142888302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coupling system of calcium looping thermal energy storage and adsorption-enhanced hydrogen production","authors":"Haocheng Sun ,&nbsp;Zhiwei Ge ,&nbsp;Zhihan Yao ,&nbsp;Liang Wang ,&nbsp;Xipeng Lin ,&nbsp;Yakai Bai ,&nbsp;Shuang Zhang ,&nbsp;Haisheng Chen","doi":"10.1016/j.enconman.2024.119254","DOIUrl":"10.1016/j.enconman.2024.119254","url":null,"abstract":"<div><div>CaL(Calcium Looping)-based Sorption-Enhanced Steam Methane Reforming (SE-SMR) is an essential method for achieving low-carbon hydrogen production. However, existing in-situ reactors struggle to produce H₂ continuously over long periods. This study proposes an innovative quasi-in-situ SE-SMR reactor based on CaL and develops a multi-physical field model with multiple reaction couplings. The study elucidates the mechanisms of heat and mass transfer, as well as reaction enhancement, and identifies the key parameters influencing the hydrogen production process in this reactor. During the pre-breakthrough phase, stored heat drives the reforming reaction, sustaining an average H₂ purity of 95.62% and a high carbon capture rate. A hydrogen yield of 3.61 demonstrates efficient methane reforming and conversion. Under the pre-breakthrough replacement strategy, the reactor performance stabilizes after the second replacement and generally maintains the high-performance level of the pre-breakthrough phase. Additionally, the heat storage properties of CaL help to reduce the heat demand of the reactor, enhancing system stability under fluctuating heat source conditions. These findings highlight the crucial role of the heat-mass coupling relationship in CaL in enhancing the hydrogen production process, offering valuable insights for developing long-term, high-performance hydrogen production solutions in solar-powered systems.</div></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":"325 ","pages":"Article 119254"},"PeriodicalIF":9.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142888355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative visualization study of turbulent jet ignition for zero carbon ammonia-hydrogen pre-chamber engines: focus on pre-chamber parameters optimization and hydrogen blending ratio","authors":"Yuhao Liu ,&nbsp;Yu Liu ,&nbsp;Fangxi Xie ,&nbsp;Linghai Han ,&nbsp;Yanfeng Gong ,&nbsp;Dingchao Qian ,&nbsp;Jingxun Yang","doi":"10.1016/j.enconman.2024.119432","DOIUrl":"10.1016/j.enconman.2024.119432","url":null,"abstract":"<div><div>Ammonia, a carbon-free fuel, holds significant potential for clean combustion applications, but challenges like ignition difficulties and slow combustion rates limit its practical use. This study aims to improve the ignition and combustion of ammonia-hydrogen mixtures using turbulent jet ignition, with experiments conducted in an optical constant volume combustion chamber. The investigation focuses on optimizing three key parameters: the equivalence ratio of hydrogen injected into the pre-chamber (Φ_p,h), the pre-chamber nozzle diameter (D_N), and the volume ratio of hydrogen mixed in the main chamber (V_H). Results indicate that adjusting Φ_p,h and D_N can significantly increase ignition energy, leading to a stronger hot jet flame and a faster combustion process. A D_N of 3 mm achieves a balance between ignition stability and combustion duration, while a larger D_N (4 mm) reduces pressure buildup, resulting in slower flame ejection. In contrast, a smaller D_N (2 mm) extends ignition delay due to re-ignition effects. Increasing V_H to 0.1 shortens ignition delay by 7.6 % and reduces combustion duration by 10.4 %. The optimal configuration—D_N = 3 mm, Φ_p,h = 1.0, and V_H = 0.1—achieves an 80.7 % reduction in ignition delay and a 35.0 % decrease in combustion duration compared to the passive pre-chamber.</div></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":"325 ","pages":"Article 119432"},"PeriodicalIF":9.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142888358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a novel electro-mechanical brake motor thermal management system for nonuniform heating under extreme thermal conditions","authors":"Piljun Park ,&nbsp;Hongseok Choi ,&nbsp;Sangwook Lee ,&nbsp;Sunoh Jeong ,&nbsp;Hoseong Lee","doi":"10.1016/j.enconman.2024.119406","DOIUrl":"10.1016/j.enconman.2024.119406","url":null,"abstract":"<div><div>A challenge currently faced by automotive brake systems industry is the development of electromechanical brakes that need to overcome the impact of frictional heat on the motor performance. However, previous studies that examined motor cooling performance have been conducted in surrounding air temperatures below 80°C while considering uniform coil heat generation. These assumptions are not valid for EMB systems. This study conducted experiments that considered extreme surrounding temperature conditions and nonuniform coil heat generation. Based on the results of these experiments, a hybrid cooling system that can withstand extreme thermal conditions is proposed through simulation. The Hybrid cooling method that uses heat sinks, insulation, and phase change materials is the most effective with a reduction in the maximum coil temperature of 23 K. Moreover, Hybrid cooling attained maximum temperature of 137.1°C even in the most extreme 1-phase motor control strategy, which is 22.8 K lower than the Baseline. When tested for pad friction coefficient ranges from 0.3 to 0.5, the system operated below the target temperature reaching up to 139.9°C under the most extreme 0.31 conditions. This study shows that effective thermal management of electromechanical brake systems that ensures system durability and reliability of driver safety is achievable.</div></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":"325 ","pages":"Article 119406"},"PeriodicalIF":9.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142888359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multivariate nonlinear regression prediction model for the performance of cooling tower assisted ground source heat pump system","authors":"Ting Lan ,&nbsp;Rong Hu ,&nbsp;Qi Tang ,&nbsp;Minxia Han ,&nbsp;Shuqin Wu ,&nbsp;Gang Liu","doi":"10.1016/j.enconman.2024.119333","DOIUrl":"10.1016/j.enconman.2024.119333","url":null,"abstract":"<div><div>Cooling tower-assisted ground source heat pump (GSHP) systems have been widely used in the regions where both cooling and heating are required in recent years. However, the issue of system design and management is still under discussion. The ratio of heat removed by cooling tower to the absorbed by the ground would influence the operation performance of hybrid system. This study developed a method to predict the system comprehensive coefficient of performance (SCOP) of hybrid system to optimize system structure and operation. Taking a cooling tower-assisted GSHP in a residential district in a hot summer and cold winter region as an example, a multivariate nonlinear regression prediction model for SCOP was derived based on the data recorded from May to September 2021 by the Building Energy Management System (BEMS) and the simulation results using TRNSYS software. Outdoor dry bulb, wet bulb temperatures, soil temperature, and auxiliary cooling ratio (ACR) are involved in the model. Based on model prediction and system simulation, the ACR of cooling tower-chiller unit should take 0.7 of the accumulated cooling load, considering the SCOP in summer and sustainability for long-term. An operation strategy has been proposed, prioritizing the operation of cooling tower-chiller and controlling the temperature difference between supply and return chilled water within 6℃. The average SCOP of the existing hybrid system can reach 5.56, and the soil temperature rise is within 4℃ over 15 years. The model can predict the variation of average SCOP with ACR during the cooling season in different regions. The calculation results serve as reference for designing and operating hybrid ground source heat pump (HGSHP) systems, ensuring system sustainability while achieving optimal SCOP.</div></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":"325 ","pages":"Article 119333"},"PeriodicalIF":9.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142788806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ammonia thermal atmosphere compression ignition combustion mode to achieve efficient combustion and low greenhouse gas emissions","authors":"Rui Yang,&nbsp;Zongyu Yue,&nbsp;Shouzhen Zhang,&nbsp;Zhijie Lv,&nbsp;Mingfa Yao","doi":"10.1016/j.enconman.2024.119427","DOIUrl":"10.1016/j.enconman.2024.119427","url":null,"abstract":"<div><div>Ammonia is a carbon-free fuel with widespread attentions and broad application prospects. The International Maritime Organization considers ammonia one of the main solutions for achieving zero-carbon emissions in future shipping. Existing research indicate that ammonia premixed combustion is constrained by the low flame propagation speed, resulting in low combustion efficiency, high nitrogen oxides emissions, and high unburned ammonia emissions. In contrast, the mixing-controlled diffusion combustion mode with ammonia high-pressure direct-injection can significantly improve the combustion performance and reduce unburned ammonia emissions. Therefore, this study innovatively proposes the ammonia thermal atmosphere compression ignition combustion mode for ammonia engine, utilizing the active thermal atmosphere produced by n-heptane premixed combustion to achieve ammonia diffusion combustion. The ammonia ignition mechanism, ammonia diffusion combustion characteristics, and the influence of ammonia injection timing are investigated in-depth through engine experiment, zero-dimensional chemical kinetics analysis and three-dimensional numerical simulation methods. According to the experiment results, ammonia thermal atmosphere compression ignition combustion mode demonstrates favorable nitrogen oxides and unburned ammonia emissions. By controlling the ammonia injection timing, ultra-low nitrous oxide and unburned ammonia emissions are observed under various engine load and engine speed conditions. The assessment of greenhouse gas emissions indicates that the proposed combustion mode has great potential in greenhouse gas reduction thanks to the high ammonia substitution rate and low nitrous oxide emissions. The maximum greenhouse gas reduction under the investigated conditions exceeds 70 %, which meets the International Maritime Organization 2040 target of greenhouse gas reduction.</div></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":"325 ","pages":"Article 119427"},"PeriodicalIF":9.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142888352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel correlation feature self-assigned Kolmogorov-Arnold Networks for multi-energy load forecasting in integrated energy systems","authors":"Xiangfei Liu ,&nbsp;Zhile Yang ,&nbsp;Yuanjun Guo ,&nbsp;Zheng Li ,&nbsp;Xiandong Xu","doi":"10.1016/j.enconman.2024.119388","DOIUrl":"10.1016/j.enconman.2024.119388","url":null,"abstract":"<div><div>The prediction of multi-energy load in an integrated energy system (IES) is crucial for facilitating the integration of renewable energy and energy scheduling. However, the multi-energy load and its related variables exhibit strong coupling, correlation quality, and uncertainty. More specifically, the short-term correlation degree and stability of the load variables are inconsistent, significantly impacting the accuracy of the final prediction model. Therefore, this paper proposes a novel correlation features self-assigned Kolmogorov-Arnold Network (KAN) for multi-energy load prediction. Initially, a multi-decoder Informer model is utilized to encode the multi-energy load variables. The encoded features are fused using random sample self-combination and a correlation feature self-assignment module. Subsequently, the decoder is employed for energy co-decoding. The final decoded features are employed to construct a predictive model using interpretable KAN. The proposed algorithm is validated on an open-source dataset. Simulation results demonstrate that compared with Transformer and Informer algorithms, the average RMSE of multi-energy load prediction achieved by our proposed algorithm is reduced by 27.880% and 40.176%, respectively; Additionally, the robustness of the proposed model has been confirmed, and the relative error of prediction for multi-energy load data with and without noise is strictly limited to the range [−0.02, 0.02].</div></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":"325 ","pages":"Article 119388"},"PeriodicalIF":9.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143148291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-driven optimization for sustainable CO2-to-methanol conversion through catalytic hydrogenation","authors":"Seyyed Alireza Ghafarian Nia ,&nbsp;Hossein Shahbeik ,&nbsp;Alireza Shafizadeh ,&nbsp;Shahin Rafiee ,&nbsp;Homa Hosseinzadeh-Bandbafha ,&nbsp;Mohammadali Kiehbadroudinezhad ,&nbsp;Sheikh Ahmad Faiz Sheikh Ahmad Tajuddin ,&nbsp;Meisam Tabatabaei ,&nbsp;Mortaza Aghbashlo","doi":"10.1016/j.enconman.2024.119373","DOIUrl":"10.1016/j.enconman.2024.119373","url":null,"abstract":"<div><div>Growing concerns about greenhouse gas emissions have accelerated research into converting CO₂ into valuable products like methanol. Catalytic hydrogenation, utilizing a catalyst in a thermochemical process, offers a promising solution for reducing atmospheric CO₂ and combating climate change. However, optimizing operating conditions and selecting suitable catalysts for CO₂ to methanol conversion remains challenging due to the complex interplay between catalyst properties and reaction performance. This research leveraged machine learning (ML) to model CO₂ to methanol conversion using a comprehensive experimental database. ML models were developed to predict CO₂ conversion efficiency, methanol selectivity, and CO selectivity, facilitating process optimization, techno-economic analysis, and life cycle assessment (LCA). The gradient boosting regression model emerged as the most accurate, with coefficients of determination (R² &gt; 0.86) and low error metrics (RMSE &lt; 9.99, MAE &lt; 5.99). <em>De novo</em> predictions demonstrated an acceptable linear relationship with the completely unseen dataset. Feature importance analysis identified temperature and gas hourly space velocity (GHSV) as the most significant descriptors. The optimal conditions for maximum CO₂ conversion efficiency and methanol selectivity were identified as temperatures between 330 and 370 °C, a pressure of 50 bar, and a GHSV of 6,500–14,000 mL/g.h. The techno-economic analysis highlighted H₂ purchase price, methanol selling price, and CO₂ feedstock costs as critical economic factors, with a payback period of 4.6 years. The LCA demonstrated a 270 % reduction in carbon emissions through catalytic hydrogenation of CO₂ to methanol. This study underscored the importance of using sustainable H₂ and electricity sources to enhance the economic and environmental benefits of the process.</div></div>","PeriodicalId":11664,"journal":{"name":"Energy Conversion and Management","volume":"325 ","pages":"Article 119373"},"PeriodicalIF":9.9,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143148286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}