Applied Energy最新文献

{"title":"A systematic review of transformers and large language models in the energy sector: towards agentic digital twins","authors":"Gabriel Antonesi ,&nbsp;Tudor Cioara ,&nbsp;Ionut Anghel ,&nbsp;Vasilis Michalakopoulos ,&nbsp;Elissaios Sarmas ,&nbsp;Liana Toderean","doi":"10.1016/j.apenergy.2025.126670","DOIUrl":"10.1016/j.apenergy.2025.126670","url":null,"abstract":"<div><div>Artificial intelligence (AI) has long promised to improve energy management in smart grids by enhancing situational awareness and supporting more effective decision-making. While traditional machine learning has demonstrated notable results in forecasting and optimization, it often struggles with generalization, situational awareness, and heterogeneous data integration. Recent advances in Transformer architecture, including Large Language Models (LLMs) and Foundation Models (FMs) can significantly improve the ability to model complex temporal and contextual relationships, as well as in multi-modal data fusion which is valuable for most AI applications in the energy sector. In this review, we synthesize the rapidly expanding field of AI applications in the energy domain, with a focus on Transformer Models (TMs) and LLMs, which have shown growing relevance. We examine the architectural foundations, domain-specific adaptations and practical implementations of TMs across various forecasting and grid management tasks. We then explore the emerging role of LLMs in the field: adaptation and fine tuning for the energy sector, the type of tasks they are suited for, and the new challenges they introduce. Along the way, we highlight practical implementations, innovations, and areas where the research frontier is rapidly expanding. These recent developments reviewed underscore a broader trend: Generative AI (GenAI) is beginning to augment decision-making not only in high-level planning but also in day-to-day operations, from forecasting and grid balancing to workforce training and asset onboarding. While FMs hold promises, we found limited evidence of their concrete application in energy domain to date. Therefore we introduce the concept of the <em>Agentic Digital Twin,</em> a next-generation model that integrates FMs to bring multi-modal situational awareness, autonomy, proactivity, and social interaction into digital twin-based energy management systems. We present the transformational impact of FMs to each phase of a digital twin and identify the open challenges that need to be addressed for their efficient and effective integration.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126670"},"PeriodicalIF":11.0,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144891922","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":"Robust scenario-based stochastic expansion planning of multi-carrier microgrids considering incentive-based loans","authors":"Mahdi Azimian ,&nbsp;Xinwei Shen ,&nbsp;Gevork B. Gharehpetian","doi":"10.1016/j.apenergy.2025.126593","DOIUrl":"10.1016/j.apenergy.2025.126593","url":null,"abstract":"<div><div>This research represents a planning method for carbon-neutral multi-carrier microgrids and provides suggestions for financial policy-making. The major obstacles to the proliferation of carbon-neutral multi-carrier microgrids are significant capital cost, intermittency, and volatility of renewable resources. Thus, a robust scenario-based stochastic expansion planning approach considering long-term investment-based incentives is proposed for microgrids with renewables, energy storage systems, and demand response. The planning model aims to minimize the project's expected cost while meeting the assigned reliability and online reserve levels. We also scrutinize allocating capital investment loan subsidies within the budget for designing a fiscally viable, reliable, and carbon-neutral multi-carrier microgrid. Moreover, the goal is to optimize the distribution of loan subsidies to warrant the efficient formation of assets, regardless of the investor's budget limitations. The model not only sizes the distributed energy resources but also identifies the demand response utilization factor to offer flexibility services. Various economic indicators are considered to assess the investment's profitability and ensure the project's viability. Finally, numerical tests unveil the path for the government to adopt practical strategies to foster rapid growth of low-carbon energy systems.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126593"},"PeriodicalIF":11.0,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144889353","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":"Data-driven multi-stage distributionally robust scheduling for coupled electricity-hydrogen-refinery systems","authors":"Chao Ning ,&nbsp;Aokai Ma ,&nbsp;Zhaoyang Dong","doi":"10.1016/j.apenergy.2025.126620","DOIUrl":"10.1016/j.apenergy.2025.126620","url":null,"abstract":"<div><div>Green hydrogen production via electrolysis has fueled decarbonization for refineries, revealing a promising roadmap towards an electricity-hydrogen-refinery integration. This paper proposes a novel multi-stage distributionally robust scheduling framework for a coupled electricity-hydrogen-refinery system under renewable energy uncertainty. Power systems, hydrogen devices, and process units are seamlessly integrated to facilitate the comprehensive optimization of the refinery, achieving both economic and sustainable benefits. To effectively hedge against the high-dimensional uncertainty arising from scheduling-stage proliferation and uncertainty-type multiplicity, we develop an innovative structured moment-Wasserstein-based ambiguity set, along with theoretical set-inclusion relationships and probabilistic guarantees. Based on this ambiguity set, the refinery scheduling problem is then formulated to achieve robust optimal dispatch strategies under a given production target. In this formulation, the variables for refinery processes are considered as here-and-now decisions due to inflexibility and production requirements, while electricity and hydrogen dispatches are treated as multi-stage recourse decisions for flexible regulation. To efficiently solve the resulting scheduling problem, we exploit a lifted decision rule, based on which an equivalent mixed-integer linear programming reformulation is derived. Case studies demonstrate the effectiveness and superiority of the proposed approach compared with state-of-the-art decision-making methods. The electricity-hydrogen-refinery coupled scheduling scheme exhibits significant advantages over the conventional scheduling scheme without distributed hydrogen generation, achieving a 3.77 % reduction in scheduling costs and a 20.59 % decrease in carbon emissions.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126620"},"PeriodicalIF":11.0,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144889311","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":"Optimizing electric bus charging station locations: An integrated land-use and transportation approach","authors":"Shaopeng Zhong ,&nbsp;Ao Liu ,&nbsp;Meihan Fan ,&nbsp;Yan Song ,&nbsp;Yu Jiang","doi":"10.1016/j.apenergy.2025.126649","DOIUrl":"10.1016/j.apenergy.2025.126649","url":null,"abstract":"<div><div>Existing research on optimizing electric bus charging station locations often assumes an exogenous demand, overlooking the feedback effects of station locations on demand. Moreover, the long-term implications of location strategies are deeply influenced by the complex interactions between land-use and transportation systems. To address these two challenges simultaneously, this study develops a bi-level programming model—a hierarchical decision-making framework involving two interconnected problems. Specifically, the upper-level problem is formulated as a mixed integer nonlinear programming model that minimizes the electric bus system's investment, operation, and passenger waiting time costs by optimizing the fleet size of electric buses, the corresponding frequency setting, and the location and capacity of charging stations. The lower-level model is an integrated land-use and transportation model that captures the long-term impacts of upper-level location decisions on transportation and land-use systems. To solve the proposed model, an iterative solution method is devised, which employs Gurobi to generate upper-level decisions via solving a linearized upper-level model and subsequently evaluates the decisions via TRNUS, which is an integrated land-use and transportation model, in the lower-level. Case studies are carried out using real data from Jiangyin City, China. The results demonstrate that the optimal design considering the interaction between land use and transportation attracts a higher number of bus users across various routes and increases the share of passenger kilometers traveled by bus from 19.9 % to 20.5 %. Meanwhile, it contributes to alleviating traffic congestion by 2.7 %, improving regional accessibility by 0.4 %, and reducing vehicle carbon emissions by 1.1 %, promoting urban sustainability.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126649"},"PeriodicalIF":11.0,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144889310","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":"An AI explained data-driven framework for electricity theft detection with optimized and active machine learning","authors":"Nadeem Javaid,&nbsp;Muhammad Hasnain,&nbsp;Muhammad Ammar","doi":"10.1016/j.apenergy.2025.126632","DOIUrl":"10.1016/j.apenergy.2025.126632","url":null,"abstract":"<div><div>Electricity theft is a major problem that causes significant financial losses and inefficient power distribution. Effective theft detection systems play a critical role in detecting fraudulent consumption patterns. However, the performance and generalization of traditional theft detection systems are hindered by issues such as class imbalance, lack of labeled data, suboptimal hyperparameter tuning, and limited model interpretability. To overcome these issues, we propose a novel framework that combines active learning and metaheuristic optimization to enhance theft detection performance. Initially, the proposed framework addresses the data imbalance in the State Grid Corporation of China dataset by employing localized randomized affine shadow sampling. Next, two models are proposed to increase classification accuracy: Active Stochastic Gradient Descent (ASGD) and Cuckoo Stochastic Gradient Descent (CSGD). The ASGD uses entropy-based active learning to prioritize informative samples, whereas CSGD incorporates cuckoo search optimization to improve parameter tuning. The proposed ASGD and CSGD models show significant improvements of 36.67 % and 35 %, respectively, over the baseline SGD in accuracy, demonstrating enhanced performance in electricity theft detection. The experimental results demonstrate that ASGD and CSGD outperform state-of-the-art models with an improvement score of 6.57 % and 7.89 % in accuracy, 7.89 % and 9.21 % in F1-score, and 7.14 % and 8.33 % in the precision-recall area under the curve. Furthermore, the results of the proposed models are validated using a 10-fold cross-validation technique to ensure their reliability. Additionally, the statistical significance of ASGD and CSGD is confirmed using a <em>t</em>-test. Finally, two explainable artificial intelligence methods: local interpretable model-agnostic explanations and Shapley additive explanations, are employed to uncover the interpretability and explainability of the proposed models’ predictions. The proposed framework is useful for detecting electricity consumption anomalies as it enhances both classification performance and model interpretability, ensuring more reliable predictions.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126632"},"PeriodicalIF":11.0,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144889313","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":"Thermodynamic analysis and performance enhancement of an integrated solar–geothermal polygeneration system using grey wolf optimization and LSTM-based forecasting with Monte Carlo uncertainty analysis: A case study on Tenerife Island","authors":"Ali Shokri Kalan ,&nbsp;Mohammadreza Babaei Khuyinrud ,&nbsp;Farshad Jahangiri ,&nbsp;Ramin Ahmadi ,&nbsp;Amir Mahboubi ,&nbsp;Xiaoshu Lü ,&nbsp;Marc A. Rosen","doi":"10.1016/j.apenergy.2025.126640","DOIUrl":"10.1016/j.apenergy.2025.126640","url":null,"abstract":"<div><div>Global warming and fossil fuel supply limitations highlight the need for sustainable energy options. Renewable-based systems provide a path to carbon neutrality but face reliability challenges due to intermittency. This study investigates Tenerife Island's potential for integrating solar and geothermal energy. A novel hybrid system is proposed, combining concentrated solar power, geothermal energy resources, with a system comprised of the following components: a supercritical CO₂ cycle, a lithium bromide-water absorption cooling system, a multi-effect desalination unit, a three-stage organic Rankine cycle and a proton exchange membrane electrolyzer. This system produces electricity, heating, cooling, freshwater, and hydrogen, achieving baseline energy and exergy efficiencies of 62 % and 17, respectively. The system's production rates are 7844 kW power, 4416 kW cooling, 6848 kW heating, 22.6 kg/h hydrogen, and 20.7 m³/h freshwater. Optimization using the grey wolf algorithm enhances the energy efficiency by 21 %, the exergy efficiency by 38 %, and the hydrogen production rate by 18 %. Solar energy forecasting employs direct normal irradiance data (2005–2024) with seq2seq long short-term memory predictions up to 2030. A forward uncertainty analysis using Monte Carlo simulations reveals that cooling capacity, exergy destruction rate, and net power production are most sensitive to fluctuations in direct normal irradiance, with coefficients of variation (CV) ranging from 4.4 % to 4.5 %, while energy and exergy efficiencies exhibit minimal coefficient of variation (CV &lt; 0.1 %).</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126640"},"PeriodicalIF":11.0,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144886465","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":"Robust capacity estimation with uncertainty quantification for li-ion batteries under temporal data masking challenges: A progressive learning approach","authors":"Tengwei Pang ,&nbsp;Guodong Fan ,&nbsp;Boru Zhou ,&nbsp;Yansong Wang ,&nbsp;Yujie Wang ,&nbsp;Xi Zhang","doi":"10.1016/j.apenergy.2025.126648","DOIUrl":"10.1016/j.apenergy.2025.126648","url":null,"abstract":"<div><div>Accurate estimation of lithium-ion battery capacity is critical for reliability management but it faces challenges due to temporal data masking, a prevalent issue in real-world cloud applications causing time-series masking and data degradation. To address this, we propose a progressive learning framework that constructs a data-quality-aware learning pathway, enabling robust training solely on high-quality laboratory data by progressively generating and incorporating artificially masked low-quality samples. The framework integrates dynamic sampling and adaptive resampling strategies to enhance model robustness against data skewness. Additionally, uncertainty quantification with strong physical interpretability is efficiently achieved through implicit ensemble learning on homologous charging segments, avoiding the computational bottlenecks of Bayesian or ensemble-based methods. Validated on the LFP, NCA, and NCM datasets, our method achieves RMSEs of 0.2170 %, 0.1924 %, and 0.1326 % on clean data, respectively. When 50 % of the data is masked, the RMSEs increase only slightly, with the maximum absolute increase being just 0.0303 %, and the model maintains high accuracy even with masking ratios as high as 70 %. The framework also generalizes well across different deep learning architectures. This work bridges the gap between laboratory models and real-world deployment for battery management systems.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126648"},"PeriodicalIF":11.0,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144889312","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":"Thermal management of solid oxide electrolysis cell systems: Integration principles, coupling with external heat sources and integration of heat storage technologies","authors":"Titouan Fabiani ,&nbsp;Nolwenn Le Pierrès ,&nbsp;Patrice Tochon ,&nbsp;Pierre Dumoulin","doi":"10.1016/j.apenergy.2025.126539","DOIUrl":"10.1016/j.apenergy.2025.126539","url":null,"abstract":"<div><div>For efficient and low-cost operation, Solid Oxide Electrolysis Cell (SOEC) systems require a well-designed heat integration architecture. In this work, SOEC thermal integration and thermal coupling with external heat source are discussed. SOEC systems strongly benefit from such a thermal coupling which can reduce the total electricity consumption by 17 %. A literature review of systems proposing such a thermal coupling is presented. Emphasis is placed on the nature and temperature of the heat sources and on the SOEC operation mode. The choice of an operation mode is influenced by considerations on the available heat input and on the system efficiency, production rate and durability. A focus is placed on the integration of heat storage systems in a SOEC architecture. The operation of SOEC systems must be continuous and stable to avoid temperature gradients and pressure differences. When coupling with a fluctuating heat source, heat storage offers a very interesting solution to match heat production and demand. When integrating with heat storage, SOEC architectures can be divided into three configurations. The first one includes a fluid loop, to which sensible storage systems are well adapted. In the second one, steam is directly generated from the heat input and steam storage systems such as latent storage or accumulators are preferred. Eventually, the third configuration corresponds to the storage of the heat produced in SOFC mode and its use in SOEC mode. Different heat storage systems are proposed in this case and lead to an improvement of 5 to 13 % in the system round-trip efficiency.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126539"},"PeriodicalIF":11.0,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144886466","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":"Coordinated operation of multi-energy microgrids considering green hydrogen and congestion management via a safe policy learning approach","authors":"Xueyong Jia ,&nbsp;Yang Xia ,&nbsp;Ziming Yan ,&nbsp;Hongjun Gao ,&nbsp;Dawei Qiu ,&nbsp;Josep M. Guerrero ,&nbsp;Zhengmao Li","doi":"10.1016/j.apenergy.2025.126611","DOIUrl":"10.1016/j.apenergy.2025.126611","url":null,"abstract":"<div><div>Multi-energy microgrids (MEMGs) with green hydrogen have attracted significant research attention for their benefits, such as energy efficiency improvement, carbon emission reduction, as well as line congestion alleviation. However, the complexities of multi-energy networks coupled with diverse uncertainties may threaten MEMG's operation. In this paper, a data-driven methodology is proposed to achieve effective MEMG operation, considering the green hydrogen technique and congestion management. First, a detailed MEMG modelling approach is developed, coupling with electricity, green hydrogen, natural gas, and thermal flows. Different from conventional MEMG models, hydrogen-enriched compressed natural gas (HCNG) models, and weather-dependent power flow are thoroughly considered in the modelling. Meanwhile, the power flow congestion problem is also formulated in the MEMG operation, which could be mitigated through HCNG integration. Based on the proposed MEMG model, a reinforcement learning-based method is designed to obtain the optimal solution of MEMG operation. To ensure the solution's safety, a soft actor-critic (SAC) algorithm is applied and modified by leveraging the Lagrangian relaxation and safety layer scheme. In the end, case studies are conducted and presented to validate the effectiveness of the proposed method.</div><div>© 2017 The Authors. Published by Elsevier Ltd.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126611"},"PeriodicalIF":11.0,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144886383","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":"Early cross-sector decarbonisation for Europe’s hard-to-abate sectors: Insights from Denmark’s 2030 target","authors":"Frederik Fristed ,&nbsp;Simon Tønning ,&nbsp;Zhiyuan Xie ,&nbsp;Lissy Langer ,&nbsp;Gorm Bruun Andresen","doi":"10.1016/j.apenergy.2025.126568","DOIUrl":"10.1016/j.apenergy.2025.126568","url":null,"abstract":"<div><div>With a 70 % reduction target by 2030, Denmark is among the first countries to require deep decarbonisation in hard-to-abate sectors, such as transport and agriculture, after more accessible options are exhausted. Collectively with other ambitious countries, this might set a precedent for Europe. Within a full European energy system, this study explores early decarbonisation pathways in the hard-to-abate sectors, evaluating outcomes for energy equity, supply security, and sustainability in Denmark under enacted policies. We model 33 European countries using PyPSA-Eur and impose cross-sector carbon budgets from national 2030 commitments to optimise capacity expansion and dispatch of electricity, heating, transport, hydrogen and biomass, including CCS. For Denmark, we apply 9-node spatial resolution and extend the carbon budget setting to include sectoral decarbonisation trajectories, including agriculture. Results show that carbon abatement costs can double if agriculture fails, heating electrification delays, or biomass is diverted from dual use for CCS. System reliability during critical periods relies on thermal storage, strategic biomass use, and flexible electrolysis. Furthermore, early renewable hydrogen adoption may yield export advantages to less decarbonised grids. We recommend: (1) building infrastructure that utilises sector coupling (electrified district heating, CCS, flexible electrolysis), (2) coordinating heat pumps and central heating with CCS to use biomass strategically, and (3) applying comparable carbon pricing across agriculture, heating, and industry, with follow-up actions if voluntary measures fail. While the share of hard-to-abate sectors varies by country, the framework is applicable to other European states with ambitious near-term targets, and the cross-sector dynamics are relevant across national contexts.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126568"},"PeriodicalIF":11.0,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144878634","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}