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SCC-UEFAS, an urban-ecological-feature based assessment system for sponge city construction

Environmental Science and Ecotechnology 2022年第4期12卷 8-17页

作者： Zi-Tong Zhao Hou-Ming Cheng Sheng Wang Hai-Yan Liu Zi-Ming Song Jun-Hui Zhou ji-wei pang Shun-Wen Bai Shan-Shan Yang jie Ding Nan-Qi Ren State Key Laboratory of Urban Water Resource and Environment Harbin Institute of TechnologyHarbin150000China Central&Southern China Municipal Engineering Design and Research Institute Co. LtdWuhan430010China China Energy Conservation and Environmental Protection Group Beijing100089China

An effective sponge city construction evaluation system plays a crucial role in evaluating sponge city construction schemes.The construction of a sponge city evaluation system still faces challenges related to incomplete index selection and unscientific weight division.Limited studies have focused on the comprehensive assessment of sponge city construction in the early stages.This study constructed a scientific assessment indicator system and a quantitative indicator weight at all levels by literature review and statistical analysis methods from an objective perspective.To demonstrate how to utilize our evaluation methods,three construction schemes randomly generated by MATLAB were evaluated under evaluation states of constant weight and variable weight,respectively.Scheme 3 had the highest score of 0.638 under the constant weight assessment,but it cannot practically be the final construction scheme due to the imbalance between indicators.Compared to the constant weight assessment,a variable weight assessment can effectively balance the states of the evaluation index with changes in the decision variable.Among the three schemes,Scheme 2 is the best choice with a value of 0.0355 under variable weight evaluation due to punishment and incentives in the variable weight method.The concept of“punishing”a disadvantageous indicator and“motivating”an advantageous indicator increases the relative advantages of the indices,ultimately affecting the assessment results of schemes and leading to a more balanced state.This study provides reasonable analysis and decision-making mechanisms to support decision-making and guide the scientific selection of a construction scheme.

关键词： Sponge city evaluation system Principal component analysis Factor analysis Variable weight theory

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Recent Advances in Electrochemical-Based Silicon Production Technologies with Reduced Carbon Emission

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Research 2024年第1期2023卷 209-236页

作者： Feng Tian Zhongya pang Shen Hu Xueqiang Zhang Fei Wang wei Nie Xuewen Xia Guangshi Li Hsien-Yi Hsu Qian Xu Xingli Zou Li ji Xionggang Lu State Key Laboratory of Advanced Special Steel&Shanghai Key Laboratory of Advanced Ferrometallurgy&School of Materials Science and Engineering Shanghai University99#Shangda RoadShanghai 200444China Center for Hydrogen Metallurgy Technology Shanghai UniversityShanghai 200444China State Key Laboratory of ASIC and System School of MicroelectronicsFudan University220#Handan RoadShanghai 200433China School of Energy and Environment Department of Materials Science and EngineeringCity University of Hong KongKowloon TongHong KongChina

Sustainable and low-carbon-emission silicon production is currently one of the main focuses for the metallurgical and materials science communities.Electrochemistry,considered a promising strategy,has been explored to produce silicon due to prominent advantages:(a)high electricity utilization efficiency;(b)low-cost silica as a raw material;and(c)tunable morphologies and structures,including films,nanowires,and nanotubes.This review begins with a summary of early research on the extraction of silicon by electrochemistry.Emphasis has been placed on the electro-deoxidation and dissolution–electrodeposition of silica in chloride molten salts since the 21st century,including the basic reaction mechanisms,the fabrication of photoactive Si films for solar cells,the design and production of nanoSi and various silicon components for energy conversion,as well as storage applications.Besides,the feasibility of silicon electrodeposition in room-temperature ionic liquids and its unique opportunities are evaluated.On this basis,the challenges and future research directions for silicon electrochemical production strategies are proposed and discussed,which are essential to achieve large-scale sustainable production of silicon by electrochemistry.

关键词： directions electrodeposition electricity

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Soft coarse-grained particle model for particle-fluid systems

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Particuology 2024年第1期84卷 178-193页

作者： Yanhao Ye ji Xu wei Ge State Key Laboratory of Multiphase Complex Systems Institute of Process EngineeringChinese Academy of SciencesBeijing100190China School of Chemical Engineering University of Chinese Academy of SciencesBeijing100049China Innovation Academy for Green Manufacture Chinese Academy of SciencesBeijing100190China

By modeling a group of neighboring real particles as a single coarse-grained particle(CGP),discrete particle method(DPM)is now capable of simulating industrial-scale particle-fluid systems.However,a systematic approach to determine the CGP properties and develop their interaction models is still lacking,which casts uncertainty on the predictivity of the method.In this study,collisions between predefined particle groups are analyzed to construct kernel functions for modeling the CGPs and then the model parameters are determined by equating the statistical properties of the CGPs and the real particles in the physical process studied.This approach is implemented for homogeneous cooling of granular gas,then demonstrated effective in simulating experimental fluidized beds.

关键词： Coarse-grain(CG) Discrete particle method(DPM) Fluidized bed Multi-scale simulation

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3D coarse-grained DPM simulation of the MIP reaction-regeneration loop

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Particuology 2024年第6期89卷 57-66页

作者： Yanhao Ye Shuai Zhang ji Xu wei Ge State Key Laboratory of Mesoscience and Engineering(formerly State Key Laboratory of Multiphase Complex Systems) Institute of Process EngineeringChinese Academy of SciencesBeijing100190China School of Chemical Engineering University of Chinese Academy of SciencesBeijing100049China

The reactor-regenerator loop is the core facility of the maximizing iso-paraffin(MIP)process.Although the discrete particle method(DPM)simulation can provide detailed information at the particle scale,it has been unable to simulate such a complex loop system due to limitations of coarse-grained(CG)models,computing software,and hardware.In this study,a newly proposed soft-shell CG-DPM model with a CG ratio of up to 800 is used to simulate a 3.5 Mt/a industrial-scale MIP reactor-regenerator loop.The solid fraction distribution obtained is found to agree well with in-situ measurements.Hydrodynamic properties including the distribution of solid fraction,gas and solid velocity,standard derivation of solid fraction with time,temporal distribution of the flow field,and particle residence time distribution are measured in the simulation,which are meaningful to better design and operate such systems in the future.

关键词： Discrete particle method Coarse-grain Maximizing iso-paraffins Full-loop Reaction-regeneration system

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Gas–solid reactor optimization based on EMMS-DPM simulation and machine learning

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Particuology 2024年第6期89卷 131-143页

作者： Haolei Zhang Aiqi Zhu ji Xu wei Ge State Key Laboratory of Mesoscience and Engineering Institute of Process EngineeringChinese Academy of SciencesBeijing 100190ChinaFormerly State Key Laboratory of Multiphase Complex SystemsInstitute of Process EngineeringChinese Academy of SciencesBeijing 100190China School of Chemical Engineering University of Chinese Academy of SciencesBeijing 100049China Innovation Academy for Green Manufacture Chinese Academy of SciencesBeijing 100190China

Design,scaling-up,and optimization of industrial reactors mainly depend on step-by-step experiments and engineering experience,which is usually time-consuming,high cost,and high risk.Although numerical simulation can reproduce high resolution details of hydrodynamics,thermal transfer,and reaction process in reactors,it is still challenging for industrial reactors due to huge computational cost.In this study,by combining the numerical simulation and artificial intelligence(AI)technology of machine learning(ML),a method is proposed to efficiently predict and optimize the performance of industrial reactors.A gas–solid fluidization reactor for the methanol to olefins process is taken as an example.1500 cases under different conditions are simulated by the coarse-grain discrete particle method based on the Energy-Minimization Multi-Scale model,and thus,the reactor performance data set is constructed.To develop an efficient reactor performance prediction model influenced by multiple factors,the ML method is established including the ensemble learning strategy and automatic hyperparameter optimization technique,which has better performance than the methods based on the artificial neural network.Furthermore,the operating conditions for highest yield of ethylene and propylene or lowest pressure drop are searched with the particle swarm optimization algorithm due to its strength to solve non-linear optimization problems.Results show that decreasing the methanol inflow rate and increasing the catalyst inventory can maximize the yield,while decreasing methanol the inflow rate and reducing the catalyst inventory can minimize the pressure drop.The two objectives are thus conflicting,and the practical operations need to be compromised under different circumstance.

关键词： Discrete particle method Artificial intelligence Machine learning Particle swarm optimization Industrial reactor optimization

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Enhanced thermoelectric and mechanical properties of Bi0.5Sb1.5Te3 alloy with dispersed yttrium oxide ceramic nanoparticles

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Rare Metals 2024年第4期43卷 1758-1768页

作者： Cun-Cheng Li Ming-wei Zhang ji-Wu Xin Lei wei Wen-Yu Zhao School of Materials Science and Engineering Shandong University of Technology School of Electrical and Electronic Engineering Nanyang Technological University State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of Technology

Reducing thermal conductivity while avoiding adverse interfacial reactions during sintering is crucial for improving the thermoelectric performance of Bi2Te3-based composites.Inert ceramic nanoparticles are good candidates for achieving this goal.In this study,we designed and prepared a series of p-type Bi0.5Sb1.5Te3nanocomposites decorated with Y2O3ceramic nanoparticles via ball-milling dispersion and spark-plasma sintering.Owing to the chemical stability of the ceramics,no traces of atomic doping or interfacial reactions were observed.Transport measurements revealed that the Y2O3nanoparticles distributed along the grain boundaries acted as energy-dependent carrier-filtering centers to improve the scattering parameter and Seebeck coefficient,contributing to the elevated power factor even with a decreased electrical conductivity.Moreover,the incorporated Y2O3nanoparticles and various defect structures they induced effectively strengthened the phonon scattering and suppressed the lattice thermal conductivity.Consequently,a peak figure of merit（ZT） of 1.23 at 313 K was achieved for 0.4%Y2O3/Bi0.5Sb1.5Te3,which is 13% higher than that of the matrix.In addition,the Vickers hardness of the composite material was 35% higher than that of the matrix.This study demonstrates the effectiveness of ceramic nanoparticles in synergistic ally improving the thermoelectric and mechanical properties,which may be further extended to other thermoelectric systems.

关键词： Bi 0.5 Sb 1.5 Te 3 alloy Y 2 O 3 ceramic nanoparticles Microstructures Thermoelectric performance Mechanical properties

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Pyrolysis Empirical Modeling of Polyester Glass Fiber Reinforced Plastics Using Sestak-Berggren Model Method

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Journal of Thermal Science 2024年第2期33卷 725-738页

作者： NAN wei ji Wenhui YUAN Yanping YUAN Zhongyuan SUN Yong School of Mechanical Engineering Southwest Jiaotong UniversityChengdu610031China CRRC Changchun Railway Vehicles Co. LtdChangchun130062China

Polyester Glass Fiber Reinforced Plastic(Polyester GFRP),a thermosetting plastic comprised of glass fiber and polyester polymer compounds,is extensively utilized in high-speed trains.Unraveling its pyrolysis mechanism is crucial as it significantly influences the combustion characteristics and fire safety aspects.Currently,kinetic research on polyester GFRP primarily focuses on employing the Coats-Redfern method to derive a theoretical kinetic model.However,the pyrolysis process of polyester GFRP is complex and the aforementioned theoretical model fails to accurately describe the pyrolysis mechanism.Therefore,this study seeks to utilize the Sestak and Berggren(SB) model as a methodological approach to reveal the complex reaction mechanism during the pyrolysis process.Based on thermogravimetric analysis,the entire pyrolysis process of polyester GFRP is divided into two primary stages.Furthermore,model-free methods are employed to ascertain the activation energy and pre-exponential factor.The results show that the fitted empirical models of the two main pyrolysis stages are f(α)=(1-α)^(1.47)[-ln(1-α)]^(1.50) and f(α)=(1-α)^(1.77)[-ln(1-α)]^(1.72),respectively.The predicted results are in good agreement with experimental data under different heating rates,which indicates that the empirical model can sufficiently describe the pyrolysis process of polyester GFRP.

关键词： polyester GFRP model-fitting methods empirical models pyrolysis

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Endogenous Trypsin Triggers Free Fluoride Release from Antarctic Krill(Euphausia superba)Cuticle

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Journal of Ocean University of China 2024年第3期23卷 766-774页

作者： PENG Yuanhuai ji wei ji Hongwu HAN Zhiping ZHANG Shiqi WEN Yaping College of Food Science and Engineering Lingnan Normal UniversityZhanjiang 524048China College of Biological and Food Engineering Guangdong University of EducationGuangzhou 510303China Guangdong Provincial Key Laboratory of Aquatic Products Processing and Safety Guangdong Ocean UniversityZhanjiang 524088China College of Food Science and Technology Guangdong Ocean UniversityZhanjiang 524088China College of Continuing Education Lingnan Normal UniversityZhanjiang 524048China

During postmortem storage,fluoride in Antarctic krill can be enriched in the muscle.Trypsin,as the most important digestive enzyme in Antarctic krill with a high activity in low temperature,plays a potential role in this process.In this study,endogenous trypsin was purified and its properties were investigated.The involvement of trypsin in the generation of free fluoride from Antarctic krill cuticle was explored.Cuticle microstructure before and after hydrolysis was compared with scanning electron microscopy,and the ash samples of the hydrolyzed Antarctic krill cuticle were analyzed with X-ray diffraction,Fourier transform infrared spectroscopy,and electron dispersive spectroscopy,respectively.Mass spectrometry analysis and inhibition tests confirmed that the purified enzyme was endogenous trypsin.Results of the present study indicated that trypsin digestion caused the increases of the concentrations of both fluoride ions and free amino N simultaneously,while the protein coated on the cuticle surface was dissolved too.However,no compositional change was detected in the cuticle inorganic salts.These findings suggest that trypsin triggered free fluoride release from Antarctic krill cuticle.In addition,the kinetics of free fluoride release could be described by the equation C_(W)=(1-0.97^(-0.006t)-0.03e^(0.0558t))x337.53+10.50.The present study improved the understanding of the role of trypsin in free fluoride release from Antarctic krill cuticle,facilitating future studies aimed at reducing the fluoride content in krill protein during Antarctic krill processing.

关键词： Antarctic krill cuticle fluoride trypsin

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A novel seal-flow multi-vortex friction stir lap welding of metal to polymer matrix composites

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Chinese Journal of Aeronautics 2024年第1期37卷 451-462页

作者： Shuaiqiang NIAN Mingshen LI Shude ji wei HU Zhiqing ZHANG Zelin SUN College of Aerospace Engineering Shenyang Aerospace UniversityShenyang 110136China State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyHarbin 150001China

The friction stir lap welding(FSLW)of metal to polymer is a challenging work due to the unavoidable polymer overflowing.Facing this problem,a novel seal-flow multi-vortex friction stir lap welding(SM-FSLW)technology based on the subversively-designed multi-step pin was put forward.Choosing 7075 aluminum alloy and short glass fiber-reinforced polyether ether ketone(PEEK)as research subjects,the welding temperature,material flow,formation and tensile shear strength of dissimilar materials lap joint under the SM-FSLW were studied and compared with those under traditional FSLW based on the conical pin.The multi-step pin rather than the conical pin effectively hindered the polymer overflowing due to the formation of vortexes by the step,thereby attaining a joint with a smooth surface.Compared with traditional FSLW,the SMFSLW obtained the higher welding temperature,the more violent material flow and the larger area with high flow velocity,thereby producing the macro-mechanical and micro-mechanical interlockings and then heightening the joint loading capacity.The tensile shear strength of lap joint under SM-FSLW was 27.8% higher than that under traditional FSLW.The SM-FSLW technology using the multi-step pin provides an effective way on obtaining a heterogeneous lap joint of metal to polymer with the excellent formation and high strength.

关键词： Friction stir lap welding Metal to polymer Multi-step pin Material flow Tensile shear strength

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Drug-Target Interactions Prediction Based on Signed Heterogeneous Graph Neural Networks

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Chinese Journal of Electronics 2024年第1期33卷 231-244页

作者： Ming CHEN Yajian jiANG Xiujuan LEI Yi PAN Chunyan ji wei jiANG College of Information Science and Engineering Hunan Normal University School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Computer Science Department BNU-HKBU United International College

Drug-target interactions(DTIs) prediction plays an important role in the process of drug discovery.Most computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target, in this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks(SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI,which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions(DDIs) and protein-protein interactions(PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from Drug Bank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.

关键词： Drug-target interactions Signed heterogeneous network Link sign prediction Graph neural networks

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