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Review of construction and demolition waste management tools and frameworks with the classification,causes,and impacts of the waste

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Waste Disposal and Sustainable Energy 2024年第1期6卷 95-121页

作者： Dewan Sabbir Ahammed Rayhan Iftekhar Uddin Bhuiyan institute of Appropriate Technology(IAT) Bangladesh University of Engineering and Technology(BUET)DhakaBangladesh Department of Industrial and production engineering(IPE) National Institute of Textile Engineering and Research(NITER)NayarhatSavarDhakaBangladesh

This review looks over the current construction and demolition waste management(C&DWM)situations by scrutinizing the definition,classification,components,compositions,generated sources and causes,impacts of generated construction and demolition wastes(C&DWs),waste management hierarchy(WMH),3R principles(Reduce,Reuse,and Recycle),Circular Economy(CE),frameworks,tools,and approaches of C&*** reviewing the literature this study contributes to the literature by the following means:(a)suitable working definitions of C&DW and C&DWM are provided,(b)an expanded WMH for construction and demolition operations is presented,(c)frameworks of C&DWM are identified and listed as follows:frameworks based on WMH,including 3R principles and CE concept,frameworks focusing on the quantification,estimation,and prediction of generated C&DW,frameworks focusing on effective and sustainable C&DWM,frameworks focusing economic,social,and environmental performance assessment,frameworks based on multi-criteria analysis(MCA),frameworks based on post-disaster recovery period,and other miscellaneous frameworks,and(d)four categories of tools utilized in C&DWM are identified and explained,namely,approaches employed in C&DWM,information technology(IT)tools employed in C&DWM,multi-criteria decision analysis(MCDA)tools employed in C&DWM,and C&DWM ***,this study also found that CE,and green rating system(GRS)are widely used approaches,Building Information Modeling(BIM),Radio Frequency Identification(RFID),Geographic Information System,and Big Data are the extensively used IT tools,Analytical Hierarchy Process,FUZZY,TOPSIS(Technique for Order Preference by Similarity to the Ideal Solution),Weighted Summation,Elimination and Choice Expressing the Reality II,Elimination and Choice Expressing the Reality III,Evaluation of Mixed Data,and REGIME(REG)are the widely used MCA tools in C&DWM,and Prefabricated Construction and Modular Construction are broadly used C&DWM ***

关键词： Construction and demolition waste Construction and demolition waste management Frameworks and tools 3R principles(Reduce,Reuse,&Recycle) Waste management hierarchy

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Optimized tillage methods increase mechanically transplanted rice yield and reduce the greenhouse gas emissions

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Journal of Integrative Agriculture 2024年第4期23卷 1150-1163页

作者： Shuang Cheng Zhipeng Xing Chao Tian Mengzhu Liu Yuan Feng Hongcheng Zhang Jiangsu Key Laboratory of Crop Cultivation and Physiology/Jiangsu Co-innovation Center for Modern production Technology of Grain Crops/Research institute of Rice Industrial engineering Technology Yangzhou UniversityYangzhou 225009China

Biaxia lrotary tillage in dryland(DBRT)can complete biaxial rotary tillage with straw incorporation,secondary suppression,and ditching,and it has been previously studied in direct-seeded rice and ***,the effects of DBRT on the mechanically transplanted rice yield and greenhouse gas emissions remain *** evaluate the effects of DBRT on improving the food security of mechanically transplanted rice and reducing the greenhouse gas emissions,we conducted an experiment for two years with wheat straw *** tillage methods were set up:DBRT,uniaxial rotary tillage in dryland and paddy(DPURT),and uniaxial rotary tillage in paddy(PURT).The results showed that compared with DPURT and PURT,DBRT increased the yield of machine-transplanted rice by 7.5-11.0%and 13.3-26.7%,respectively,while the seasonal cumulative CH_(4) emissions were reduced by 13.9-21.2%and 30.2-37.0%,respectively,and the seasonal cumulative N_(2)O emissions were increased by 13.5-28.6%and 50.0-73.1%,***,DBRT reduced the global warming potential by 10.7-15.5%and 23.7-28.6%,respectively,andtheyield-scaledglobalwarmingpotentialby18.2-21.8%and36.4-39.3%,respectively,compared to DPURT and *** results were mainly related to the fact that DBRT significantly reduced soil bulk density and increased soil redox potential(Eh).Therefore,implementing DBRT in machine-transplanted rice fields is feasible,which cannot only increase the rice yield,but also reduce the greenhouse gas emissions.

关键词： integrated tillage rice grain yield CH_(4) N_(2)O

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machine learning for carbonate formation drilling: Mud loss prediction using seismic attributes and mud loss records

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Petroleum Science 2024年第2期21卷 1241-1256页

作者： Hui-Wen Pang Han-Qing Wang Yi-Tian Xiao Yan Jin Yun-Hu Lu Yong-Dong Fan Zhen Nie National Key Laboratory of Petroleum Resources and engineering China University of Petroleum(Beijing)Beijing102249China College of Science China University of Petroleum-BeijingBeijing102249China Petroleum Exploration and production Research institute SINOPECBeijing102206China College of Petroleum engineering China University of Petroleum-BeijingBeijing102249China Research institute of Petroleum Exploration and Development CNPCBeijing100083China

Due to the complexity and variability of carbonate formation leakage zones, lost circulation prediction and control is one of the major challenges of carbonate drilling. It raises well-control risks and production expenses. This research utilizes the H oilfield as an example, employs seismic features to analyze mud loss prediction, and produces a complete set of pre-drilling mud loss prediction solutions. Firstly, 16seismic attributes are calculated based on the post-stack seismic data, and the mud loss rate per unit footage is specified. The sample set is constructed by extracting each attribute from the seismic trace surrounding 15 typical wells, with a ratio of 8:2 between the training set and the test set. With the calibration results for mud loss rate per unit footage, the nonlinear mapping relationship between seismic attributes and mud loss rate per unit size is established using the mixed density network ***, the influence of the number of sub-Gausses and the uncertainty coefficient on the model's prediction is evaluated. Finally, the model is used in conjunction with downhole drilling conditions to assess the risk of mud loss in various layers and along the wellbore trajectory. The study demonstrates that the mean relative errors of the model for training data and test data are 6.9% and 7.5%, respectively, and that R2is 90% and 88%, respectively, for training data and test data. The accuracy and efficacy of mud loss prediction may be greatly enhanced by combining 16 seismic attributes with the mud loss rate per unit footage and applying machine learning methods. The mud loss prediction model based on the MDN model can not only predict the mud loss rate but also objectively evaluate the prediction based on the quality of the data and the model.

关键词： Lost circulation Risk prediction machine learning Seismic attributes Mud loss records

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基于拍卖理论的动态多代理同类机调度算法

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engineering 2024年第4期35卷 32-45页

作者： Yaqiong Liu Shudong Sun Gaopan Shen Xi Vincent Wang Magnus Wiktorsson Lihui Wang Department of Industrial engineering Northwestern Polytechnical UniversityXi’an 710072China Key Laboratory of Industrial engineering and Intelligent Manufacturing Ministry of Industry and Information TechnologyXi’an 710072China Department of production engineering KTH Royal Institute of TechnologyStockholm 10044Sweden

This paper addresses a multi-agent scheduling problem with uniform parallel machines owned by a resource agent and competing jobs with dynamic arrival times that belong to different consumer *** agents are self-interested and rational with the aim of maximizing their own objectives,resulting in intense resource competition among consumer agents and strategic behaviors of unwillingness to disclose private *** the context,a centralized scheduling approach is unfeasible,and a decentralized approach is considered to deal with the targeted *** study aims to generate a stable and collaborative solution with high social welfare while simultaneously accommodating consumer agents’preferences under incomplete *** this purpose,a dynamic iterative auction-based approach based on a decentralized decision-making procedure is *** the proposed approach,a dynamic auction procedure is established for dynamic jobs participating in a realtime auction,and a straightforward and easy-to-implement bidding strategy without price is presented to reduce the complexity of bid *** addition,an adaptive Hungarian algorithm is applied to solve the winner determination problem efficiently.A theoretical analysis is conducted to prove that the proposed approach is individually rational and that the myopic bidding strategy is a weakly dominant strategy for consumer agents submitting *** computational experiments demonstrate that the developed approach achieves high-quality solutions and exhibits considerable stability on largescale problems with numerous consumer agents and jobs.A further multi-agent scheduling problem considering multiple resource agents will be studied in future work.

关键词： Multi-agent scheduling Decentralized scheduling Auction Dynamic jobs Private information

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Digital Twin Modeling Enabled machine Tool Intelligence:A Review

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Chinese Journal of Mechanical engineering 2024年第2期37卷 29-54页

作者： Lei Zhang Jianhua Liu Cunbo Zhuang School of Mechanical engineering Tianjin University of CommerceTianjin 300134China Laboratory of Digital Manufacturing School of Mechanical EngineeringBeijing Institute of TechnologyBeijing 10081China

machine tools,often referred to as the“mother machines”of the manufacturing industry,are crucial in developing smart manufacturing and are increasingly becoming more *** twin technology can promote machine tool intelligence and has attracted considerable research ***,there is a lack of clear and systematic analyses on how the digital twin technology enables machine tool ***,digital twin modeling was identified as an enabling technology for machine tool intelligence based on a comparative study of the characteristics of machine tool intelligence and digital *** review then delves into state-of-the-art digital twin modelingenabled machine tool intelligence,examining it from the aspects of data-based modeling and mechanism-data dual-driven ***,it highlights three bottleneck issues facing the *** these problems,the architecture of a digital twin machine tool(DTMT)is proposed,and three key technologies are expounded in detail:Data perception and fusion technology,mechanism-data-knowledge hybrid-driven digital twin modeling and virtual-real synchronization technology,and dynamic optimization and collaborative control technology for multilevel ***,future research directions for the DTMT are *** work can provide a foundation basis for the research and implementation of digital-twin modeling-enabled machine tool intelligence,making it significant for developing intelligent machine tools.

关键词： machine tool Digital twin Smart manufacturing Synchronization

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Bolstering integrity in environmental data science and machine learning requires understanding socioecological inequity

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Frontiers of Environmental Science & engineering 2024年第5期18卷 171-179页

作者： Joe F.Bozeman III School of Civil and Environmental engineering Georgia Institute of TechnologyAtlantaGA30332USA School of Public Policy Georgia Institute of TechnologyAtlantaGA30332USA

Socioecological inequity in environmental data science—such as inequities deriving from data-driven approaches and machine learning(ML)—are current issues subject to debate and *** is growing consensus around embedding equity throughout all research and design domains—from inception to administration,while also addressing procedural,distributive,and recognitional ***,practically doing so may seem onerous or daunting to *** current perspective helps to alleviate these types of concerns by providing substantiation for the connection between environmental data science and socioecological inequity,using the Systemic Equity Framework,and provides the foundation for a paradigmatic shift toward normalizing the use of equity-centered approaches in environmental data science and ML *** the integrity of environmental data science and ML is just beginning from an equity-centered tool development and rigorous application *** this end,this perspective also provides relevant future directions and challenges by overviewing some meaningful tools and strategies—such as applying the Wells-Du Bois Protocol,employing fairness metrics,and systematically addressing irreproducibility;emerging needs and proposals—such as addressing data-proxy bias and supporting convergence research;and establishes a ten-step path ***,the work that environmental scientists and engineers do ultimately affect the well-being of us all.

关键词： Equity Bias machine Learning Data Science Justice Systemic Equity

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Identification of reservoir types in deep carbonates based on mixedkernel machine learning using geophysical logging data

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Petroleum Science 2024年第3期21卷 1632-1648页

作者： Jin-Xiong Shi Xiang-Yuan Zhao Lian-Bo Zeng Yun-Zhao Zhang Zheng-Ping Zhu Shao-Qun Dong Key Laboratory of Exploration Technologies for Oil and Gas Resources of the Ministry of Education Yangtze UniversityWuhan4310HubeiChina School of Geosciences Yangtze UniversityWuhan430100HubeiChina College of Geosciences China University of Petroleum(Beijing)Beijing102249China Petroleum Exploration and production Research institute of SINOPEC Beijing100083China School of Earth Sciences and engineering Xi'an Shiyou UniversityXi'an 710065ShaanxiChina

Identification of reservoir types in deep carbonates has always been a great challenge due to complex logging responses caused by the heterogeneous scale and distribution of storage *** cross-plot analysis and empirical formula methods for identifying reservoir types using geophysical logging data have high uncertainty and low efficiency,which cannot accurately reflect the nonlinear relationship between reservoir types and logging ***,the kernel Fisher discriminant analysis(KFD),a kernel-based machine learning technique,attracts attention in many fields because of its strong nonlinear processing ***,the overall performance of KFD model may be limited as a single kernel function cannot simultaneously extrapolate and interpolate well,especially for highly complex data *** address this issue,in this study,a mixed kernel Fisher discriminant analysis(MKFD)model was established and applied to identify reservoir types of the deep Sinian carbonates in central Sichuan Basin,*** MKFD model was trained and tested with 453 datasets from 7 coring wells,utilizing GR,CAL,DEN,AC,CNL and RT logs as input *** particle swarm optimization(PSO)was adopted for hyper-parameter optimization of MKFD *** evaluate the model performance,prediction results of MKFD were compared with those of basic-kernel based KFD,RF and SVM ***,the built MKFD model was applied in a blind well test,and a variable importance analysis was *** comparison and blind test results demonstrated that MKFD outperformed traditional KFD,RF and SVM in the identification of reservoir types,which provided higher accuracy and stronger *** MKFD can therefore be a reliable method for identifying reservoir types of deep carbonates.

关键词： Reservoir type identification Geophysical logging data Kernel Fisher discriminantanalysis Mixedkernel function Deep carbonates

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Recent innovations in laser additive manufacturing of titanium alloys

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International Journal of Extreme Manufacturing 2024年第3期6卷 2-37页

作者： Jinlong Su Fulin Jiang Jie Teng Lequn Chen Ming Yan Guillermo Requena Lai-Chang Zhang Y Morris Wang Ilya V Okulov Hongmei Zhu Chaolin Tan College of Materials Science and engineering Hunan UniversityChangsha 410082People’s Republic of China Singapore institute of Manufacturing Technology(SIM Tech) Agency for ScienceTechnology and Research(A∗STAR)5 Cleantech LoopSingapore 636732Singapore Department of Materials Science and engineering Southern University of Science and TechnologyShenzhen 518055People’s Republic of China institute of Materials Research German Aerospace Center(DLR)Linder Höhe51147 CologneGermany Centre for Advanced Materials and Manufacturing Edith Cowan University270 Joondalup DriveJoondalupPerthWestern Australia 6027Australia Department of Materials Science and engineering University of CaliforniaLos AngelesCA 900095United States of America Leibniz institute for Materials engineering-IWT Badgasteiner Str.328359 BremenGermany Faculty of production engineering University of BremenBadgasteiner Str.128359 BremenGermany Key Laboratory of Hunan Province of Equipment Safety Service Technology under Extreme Environment University of South ChinaHengyangPeople’s Republic of China RWTH Aachen University Metallic Structures and Materials Systems for Aerospace EngineeringD-52062 AachenGermany

Titanium(Ti)alloys are widely used in high-tech fields like aerospace and biomedical *** additive manufacturing(LAM),as an innovative technology,is the key driver for the development of Ti *** the significant advancements in LAM of Ti alloys,there remain challenges that need further research and development *** recap the potential of LAM high-performance Ti alloy,this article systematically reviews LAM Ti alloys with up-to-date information on process,materials,and *** feasible solutions to advance LAM Ti alloys are reviewed,including intelligent process parameters optimization,LAM process innovation with auxiliary fields and novel Ti alloys customization for *** auxiliary energy fields(***,acoustic,mechanical deformation and magnetic fields)can affect the melt pool dynamics and solidification behaviour during LAM of Ti alloys,altering microstructures and mechanical *** kinds of novel Ti alloys customized for LAM,like peritecticα-Ti,eutectoid(α+β)-Ti,hybrid(α+β)-Ti,isomorphousβ-Ti and eutecticβ-Ti alloys are reviewed in ***,machine learning in accelerating the LAM process optimization and new materials development is also *** review summarizes the material properties and performance envelops and benchmarks the research achievements in LAM of Ti *** addition,the perspectives and further trends in LAM of Ti alloys are also highlighted.

关键词： additive manufacturing titanium alloys auxiliary field machine learning aerospace materials lightweight materials novel alloys

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Identification of carbonate sedimentary facies from well logs with machine learning Author links open overlay panel

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Petroleum Research 2024年第2期9卷 165-175页

作者： Xianmu Hou Peiqing Lian Jiuyu Zhao Yun Zai Weiyao Zhu Fuyong Wang School of Civil and Resources engineering University of Science and Technology BeijingBeijing100083China Sinopec Chongqing Fuling Shale Gas Exploration and Development Co. Ltd.Chongqing408000China Sinopec Petroleum Exploration&production Research institute SINOPECBeijing100083China National Key Laboratory of Petroleum Resources and engineering China University of Petroleum(Beijing)Beijing102249China ChinKunlun Digital Technology Co.Ltd. Beijing102206China

Sedimentary facies identification is critical for carbonate oil and gas reservoir *** traditional method of sedimentary facies identification not only be affected by the engineer's experience but also takes a long *** carbonate sedimentary facies based on machine learning is the trend of future development and has the advantages of short time consuming and reliable results without engineers'subjective *** many references reported the application of machine learning to identify lithofacies,but identifying sedimentary facies of carbonate reservoirs is much more challenging due to the complex sedimentary environment and tectonic *** paper compares the performance of the carbonate sedimentary facies identification using four different machine learning models,and the optimal machine learning with the highest prediction accuracy is ***,the carbonate sedimentary facies are classified into the lagoon,shallow sea,shoal,fore-shoal,and inter-shoal five tags based on the well ***,five well log curves including spectral gamma ray(SGR),uranium-free gamma ray(CGR),photoelectric absorption cross-section index(PE),true formation resistivity(RT),shallow lateral resistivity(RS)are used as the input,and the manual identified carbonate sedimentary facies are used as the output of the machine learning *** performance of four different machine learning algorithms,including support vector machine(SVM),deep neural network(DNN),long short-term memory(LSTM)network,and random forest(RF)are *** other two wells are used for model *** research results show that the RF method has the highest accuracy of sedimentary facies prediction,and the average prediction accuracy is 78.81%;the average accuracy of sedimentary facies prediction using SVM is 77.93%.The sedimentary facies predictions using DNN and LSTM are less satisfying compared with RF and SVM,and the average accuracy is 69.94%and 73.05%,respect

关键词： Carbonate Sedimentary facies Well logs machine learning Deep learning

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Recent progress of in-situ characterization of LiNi_(1−x−y)Co_(x)Mn_(y)O_(2) cathodes for lithium metal batteries: A mini review

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Nano Research 2024年第3期17卷 1384-1401页

作者： Huanzhu Lv Xiaoqi Zhu Jun Mei Yuanhua Xia Bin Wang institute of Fundamental and Frontier Science University of Electronic Science and Technology of ChinaChengdu 611731China Chengdu Development Center of Science and Technology China Academy of Engineering PhysicsChengdu 610200China Key Laboratory of Neutron Physics and institute of Nuclear Physics and Chemistry China Academy of Engineering Physics(CAEP)Mianyang 621999China

In the context of the gradual popularity of electric vehicles(EVs),the development of lithium battery systems with high energy density and power density is regarded as the foremost way to improve the range of ***_(1-x-y)Co_(x)Mn_(y)O_(2)(NCM)cathodes have been the focus of researchers due to their high energy density,excellent power performance,and low-temperature ***,the elaboration of the decay mechanism of NCM cathode based on lithium metal batteries(LMBs)is still being restricted to the primary *** the past decades,the development and application of advanced in-situ characterization tools have facilitated researchers'understanding of the internal operation mechanism of batteries during charging and *** this minireview,the latest progress of in-situ observation of the NCM cathode by X-ray diffraction(XRD),fourier transform infrared(FT-IR)spectroscopy,Raman spectroscopy,atomic force microscopy(AFM),transmission electron microscope(TEM),optical microscope,and other characterization tools is *** mechanisms of structural degradation,cathode-electrolyte interfaces(CEIs)composition,and dynamic changes of NCM,electrolyte breakdown,and gas production are ***,based on the existing research progress,the opportunities and challenges for future in-situ characterization technology in the study of the mechanism of LMBs are discussed in ***,the purpose of this minireview is to summarize recent work that focuses on the outstanding application of in-situ characterization techniques in the mechanistic study of LMBs,and pointing the way to the future development of high energy density and power density LMBs systems.

关键词： lithium metal batteries(LMBs) In-situ characterization LiNi_(1-x-y)Co_(x)Mn_(y)O_(2)(NCM)

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