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Resource utilization of typical biomass wastes as biochars in removing plasticizer diethyl phthalate from water: characterization and adsorption mechanisms

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Frontiers of Environmental science & engineering 2023年第1期17卷 49-61页

作者： Mei Shi Xiao Wang Mengying Shao Lun Lu Habib Ullah Hao Zheng Fengmin Li Institute of Coastal Environmental Pollution Control College of Environmental Science and EngineeringKey Laboratory of Marine Environment and Ecology(Ministry of Education)Ocean University of ChinaQingdao 266100China State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment South China Institute of Environmental SciencesMinistry of Ecology and EnvironmentGuangzhou 510655China Department of Environmental Science Zhejiang UniversityHangzhou 310058China Marine Ecology and Environmental Science Laboratory Pilot National Laboratory for Marine Science and TechnologyQingdao 266071China Sanya Oceanographic Institution Ocean University of ChinaSanya 572000China

Plastic pollution as a global environmental issue has become a research hotspot,among which the removal of inherent plasticizer(e.g.,phthalic acid esters,PAEs)received increasing ***,the effects of biochars derived from different feedstocks on the adsorption of PAEs are poorly ***,the characteristics of biochars derived from six largely produced biomass wastes in China at 400°C,as well as their performance in adsorption of diethyl phthalate(DEP),one of frequently detected PAEs in aqueous environment,were *** results indicated that the variation in feedstock type showed significant changes in the properties(e.g.,porosity,specific surface area,surface functional groups)of biochars,which affected DEP adsorption and *** order and Freundlich models fitted the adsorption data well,and adsorption mechanisms mainly included hydrophobic effect,followed by micropore filling,hydrogen bonding,andπ-πEDA *** thermodynamics revealed that the adsorption was a spontaneous and exothermic the adsorption capacities of DEP on these biochars slightly decreased with the increasing pH but increased with the increasing ionic *** these biochars,the giant reed biochar with relatively higher DEP adsorption and low desorption exhibited the great efficiency for DEP removal as an environment-friendly *** results highlighted the significant roles of micropore filling and hydrogen bond in determining adsorption capacity of designed biochars prepared from selecting suitable agricultural straws and wetland plant waste to typical *** findings are useful for producing designed biochars from different biomass wastes for plasticizer pollution control.

关键词： Plastic pollution Phthalic acid esters Designed biochar Wetland plant Adsorption

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Dynamic Feature Extractor for industrial key Variable Prediction Based on Enhanced Gating Mechanism

Dynamic Feature Extractor for Industrial Key Variable Predic...

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Chinese Automation Congress (CAC)

作者： Cheng Qiu Xinmin Zhang Jinchuan Qian Zheren Zhu Zhihuan Song Zhichao Chen The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou P. R. China

In real-world industrial processes, the realtime measurement of key variables is vital to the control of industrial systems. Soft sensing, as a method of predicting key variables using auxiliary variables, has been widely used in industrial environments. To construct an accurate soft-sensing model, the effective extraction of data features is crucial, especially for industrial processes with complex nonlinearity and dynamism. However, traditional dynamic feature extractors often lose information over extended time durations, disappearing with the increase in time-step length through iterations. To address this issue, this study proposes an advanced dynamic feature extractor called ATTENREG-DFE, which has a novel gated mechanism. In addition, in ATTENREG-DFE, the attention mechanism is employed for feature extraction from the input data, and it ensures the inclusion of long-duration information within the output features. To verify the effectiveness of the proposed method, it was applied to actual synthetic ammonia industrial processes, and the experimental results show that the proposed method is superior to other state-of-the-art methods.

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Prototype Adaption and Projection for Few- and Zero-shot 3D Point Cloud Semantic Segmentation

arXiv

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arXiv 2023年

作者： He, Shuting Jiang, Xudong Jiang, Wei Ding, Henghui The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China Singapore639798 Singapore

In this work, we address the challenging task of few-shot and zero-shot 3D point cloud semantic segmentation. The success of few-shot semantic segmentation in 2D computer vision is mainly driven by the pre-training on large-scale datasets like imagenet. The feature extractor pre-trained on large-scale 2D datasets greatly helps the 2D few-shot learning. However, the development of 3D deep learning is hindered by the limited volume and instance modality of datasets due to the significant cost of 3D data collection and annotation. This results in less representative features and large intra-class feature variation for few-shot 3D point cloud segmentation. As a consequence, directly extending existing popular prototypical methods of 2D few-shot classification/segmentation into 3D point cloud segmentation won’t work as well as in 2D domain. To address this issue, we propose a Query-Guided Prototype Adaption (QGPA) module to adapt the prototype from support point clouds feature space to query point clouds feature space. With such prototype adaption, we greatly alleviate the issue of large feature intra-class variation in point cloud and significantly improve the performance of few-shot 3D segmentation. Besides, to enhance the representation of prototypes, we introduce a Self-Reconstruction (SR) module that enables prototype to reconstruct the support mask as well as possible. Moreover, we further consider zero-shot 3D point cloud semantic segmentation where there is no support sample. To this end, we introduce category words as semantic information and propose a semantic-visual projection model to bridge the semantic and visual spaces. Our proposed method surpasses state-of-the-art algorithms by a considerable 7.90% and 14.82% under the 2-way 1-shot setting on S3DIS and ScanNet benchmarks, respectively. Code is available at https://***/heshuting555/PAP-FZS3D. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Adaptive Decentralized Asymptotic Tracking control for Large-Scale Nonlinear Systems With Unknown Strong Interconnections

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 173-186页

作者： Ben Niu Jidong Liu Ding Wang Xudong Zhao Huanqing Wang College of Information Science and Engineering Shandong Normal UniversityJinan 250014China School of Artificial Intelligence Nankai UniversityTianjin 300071China Faculty of Information Technology Beijing University of TechnologyBeijing 100124 State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Control Science and Engineering Dalian University of TechnologyDalian 116023China Department of Mathematical Science and Automatization Institute Bohai UniversityJinzhou 121000China

An adaptive decentralized asymptotic tracking control scheme is developed in this paper for a class of large-scale nonlinear systems with unknown strong interconnections,unknown time-varying parameters,and ***,by employing the intrinsic properties of Gaussian functions for the interconnection terms for the first time,all extra signals in the framework of decentralized control are filtered out,thereby removing all additional assumptions imposed on the interconnec-tions,such as upper bounding functions and matching ***,by introducing two integral bounded functions,asymptotic tracking control is ***,the nonlinear filters with the compensation terms are introduced to circumvent the issue of“explosion of complexity”.It is shown that all the closed-loop signals are bounded and the tracking errors converge to zero *** the end,a simulation example is carried out to demonstrate the effectiveness of the proposed approach.

关键词： Adaptive decentralized control asymptotic tracking control large-scale nonlinear systems unknown strong interconnections

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Multiple Fault Detection and Appliance Classification Using Shuttle Attention-Based Model

SSRN

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SSRN 2024年

作者： Jiang, Jiahao Wang, Zhelong Qiu, Sen Zhao, Hongyu Liu, Ruichen Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education The School of Control Science and Engineering Dalian University of Technology Dalian116024 China

Electrical faults pose a significant risk of fire accidents, making their early detection and diagnosis critical for ensuring safety in buildings and electrical systems. Current fault detection methods relying on low-frequency power data inadequately capture subtle indicators of electrical faults. Moreover, these methods rarely engage in detecting multiple load types and fault categories. To this end, a new fault detection and appliance classification method was proposed. First, a new feature based on mutual information variational mode extraction (MIVME) was extracted. The feature can reflect the fault signal’s dynamics and variations on multiple scales. Further, a new model called load fault shuffle attention temporal convolutional network (LFSATCN) was designed to identify multiple load fault types. The model utilities autocorrelation channel attention and local amplification attention to capture the load fault details in the feature. The proposed model was evaluated on a self-collected high-frequency load fault dataset, as well as public datasets. The load fault dataset contains data samples of electrical faults, such as short circuits, open circuits, relay malfunctions and arc faults. The comparison result reveal that our method outperforms the state-of-art approaches. © 2024, The Authors. All rights reserved.

关键词： Feature extraction

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AeroGTO: An Efficient Graph-Transformer Operator for Learning Large-Scale Aerodynamics of 3D Vehicle Geometries 39

AeroGTO: An Efficient Graph-Transformer Operator for Learnin...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liu, Pengwei Wang, Pengkai Ren, Xingyu Yuan, Hangjie Hao, Zhongkai Xu, Chao Cai, Shengze Ni, Dong College of Control Science and Engineering Zhejiang University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Department of Computer Science and Technology Tsinghua University Beijing China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

ISBN: (纸本)157735897X

Obtaining high-precision aerodynamics in the automotive industry relies on large-scale simulations with computational fluid dynamics, which are generally time-consuming and computationally expensive. Recent advances in operator learning for partial differential equations offer promising improvements in terms of efficiency. However, capturing intricate physical correlations from extensive and varying geometries while balancing large-scale discretization and computational costs remains a significant challenge. To address these issues, we propose AeroGTO, an efficient graph-transformer operator designed specifically for learning large-scale aerodynamics in engineering applications. AeroGTO combines local feature extraction through message passing and global correlation capturing via projection-inspired attention, employing a frequency-enhanced graph neural network augmented with k-nearest neighbors to handle three-dimensional (3D) irregular geometries. Moreover, the transformer architecture adeptly manages multi-level dependencies with only linear complexity concerning the number of mesh points, enabling fast inference of the model. Given a car's 3D mesh, AeroGTO accurately predicts surface pressure and estimates drag. In comparisons with five advanced models, AeroGTO is extensively tested on two industry-standard benchmarks, Ahmed-Body and DrivAerNet, achieving a 7.36% improvement in surface pressure prediction and a 10.71% boost in drag coefficient estimation, with fewer FLOPs and only 1% of the parameters used by the prior leading method. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Graph neural networks

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Measurement of atmospheric nanoparticles:Bridging the gap between gas-phase molecules and larger particles

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Journal of Environmental sciences 2023年第1期123卷 183-202页

作者： Chao Peng Chenjuan Deng Ting Lei Jun Zheng Jun Zhao Dongbin Wang Zhijun Wu Lin Wang Yan Chen Mingyuan Liu Jingkun Jiang Anpei Ye Maofa Ge Weigang Wang State Key Laboratory for Structural Chemistry of Unstable and Stable Species Beijing National Laboratory for Molecular Sciences(BNLMS)CAS Research/Education Center for Excellence in Molecular SciencesInstitute of ChemistryChinese Academy of SciencesBeijing 100190China State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing 100084China School of Environment Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China School of Atmospheric Sciences Sun Yat-sen UniversityZhuhaiGuangdong 519082China State Key Joint Laboratory of Environmental Simulation and Pollution Control College of Environmental Sciences and EngineeringPeking UniversityBeijing 100871China Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention(LAP3) Department of Environmental Science&EngineeringFudan UniversityShanghai 200433China Key Laboratory for the Physics and Chemistry of Nanodevices Department of ElectronicsSchool of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China University of Chinese Academy of Sciences Beijing 100049China

Atmospheric nanoparticles are crucial components contributing to fine particulate matter(PM_(2.5)),and therefore have significant effects on visibility,climate,and human *** to the unique role of atmospheric nanoparticles during the evolution process from gas-phase molecules to larger particles,a number of sophisticated experimental techniques have been developed and employed for online monitoring and characterization of the physical and chemical properties of atmospheric nanoparticles,helping us to better understand the formation and growth of new *** this paper,we firstly review these state-of-the-art techniques for investigating the formation and growth of atmospheric nanoparticles(e.g.,the gas-phase precursor species,molecular clusters,physicochemical properties,and chemical composition).Secondly,we present findings from recent field studies on the formation and growth of atmospheric nanoparticles,utilizing several advanced ***-more,perspectives are proposed for technique development and improvements in measuring atmospheric nanoparticles.

关键词： Nanoparticle Measurement techniques Size distributions Physical properties Chemical composition

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A Photoplethysmography Signal Compression Method of Extreme Learning Machine 42

A Photoplethysmography Signal Compression Method of Extreme ...

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42nd Chinese control Conference, CCC 2023

作者： Zhou, Yan An, Aimin Liu, Jicheng Chou, Yongxin College of Electrical and Information Engineering Lanzhou University of Technology Lanzhou730000 China School of Electrical and Automatic Engineering Changshu Institute of Technology Suzhou215000 China Key Laboratory of Gansu Advanced Control for Industrial Processes Lanzhou730050 China Lanzhou University of Technology National Demonstration Center for Experimental Electrical and Control Engineering Education Lanzhou730050 China

ISBN: (纸本)9789887581543

As one of the important physiological signals for human health monitoring, there are massive photoplethysmography (PPG) signals and excessive demands on storage and network communication resources for long time monitoring. Therefore, it is desirable to compress the PPG signal where redundancy exists without losing important information. In this paper, a PPG signal compression method with extreme learning machine (ELM) is proposed. ELM does not require iterative adjustment of the hidden layer parameters and has the advantages of high generalization ability and fast learning speed. First, the PPG signal is segmented and processed as input and output data. Second, the parameters of the hidden layer based on the set training set is calculated. Then, the obtained parameters are used to compress and reconstruct the PPG signal, one PPG wave band at a time, until the end of the training. Comparing with the error Back Propagation Training (BP) neural network, for the 4 min sampled data, the average time to compress and reconstruct the PPG signal is 0.026±0.007, the MSE is 0.004±0.004 and the MAD is 0.031±0.026. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Neural networks

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Causality-driven Sequence Segmentation for Enhancing Multiphase industrial Process Data Analysis and Soft Sensing

arXiv

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arXiv 2024年

作者： He, Yimeng Yao, Le Zhang, Xinmin Kong, Xiangyin Song, Zhihuan The State Key Laboratory of Industrial Control Technology The College of Control Science and Engineering Zhejiang University Hangzhou310027 China The School of Mathematics Hangzhou Normal University Hangzhou311121 China

The dynamic characteristics of multiphase industrial processes present significant challenges in the field of industrial big data modeling. Traditional soft sensing models frequently neglect the process dynamics and have difficulty in capturing transient phenomena like phase transitions. To address this issue, this article introduces a causality-driven sequence segmentation (CDSS) model. This model first identifies the local dynamic properties of the causal relationships between variables, which are also referred to as causal mechanisms. It then segments the sequence into different phases based on the sudden shifts in causal mechanisms that occur during phase transitions. Additionally, a novel metric, similarity distance, is designed to evaluate the temporal consistency of causal mechanisms, which includes both causal similarity distance and stable similarity distance. The discovered causal relationships in each phase are represented as a temporal causal graph (TCG). Furthermore, a soft sensing model called temporal-causal graph convolutional network (TC-GCN) is trained for each phase, by using the time-extended data and the adjacency matrix of TCG. The numerical examples are utilized to validate the proposed CDSS model, and the segmentation results demonstrate that CDSS has excellent performance on segmenting both stable and unstable multiphase series. Especially, it has higher accuracy in separating non-stationary time series compared to other methods. The effectiveness of the proposed CDSS model and the TC-GCN model is also verified through a penicillin fermentation process. Experimental results indicate that the breakpoints discovered by CDSS align well with the reaction mechanisms and TC-GCN significantly has excellent predictive accuracy. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolution

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Effects of external dynamic disturbances and structural plane on rock fracturing around deep underground cavern

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International Journal of Coal science & technology 2022年第1期9卷 99-119页

作者： Fan Feng Shaojie Chen Xingdong Zhao Diyuan Li Xianlai Wang Jiqiang Cui College of Energy and Mining Engineering Shandong University of Science and TechnologyQingdaoShandongChina State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines Anhui University of Science and TechnologyHuainanAnhuiChina School of Resources and Civil Engineering Northeastern UniversityShenyangLiaoningChina School of Resources and Safety Engineering Central South UniversityChangshaHunanChina State Key Laboratory of Nickel and Cobalt Resources

The occurrence of disasters in deep mining engineering has been confirmed to be closely related to the external dynamic disturbances and geological ***,a combined finite-element method was employed to simulate the failure process of an underground cavern,which provided insights into the failure mechanism of deep hard rock affected by factors such as the dynamic stress-wave amplitudes,disturbance direction,and dip angles of the structural *** crack-propagation process,stress-field distribution,displacement,velocity of failed rock,and failure zone around the circular cavern were analyzed to identify the dynamic response and failure properties of the underground *** simulation results indicate that the dynamic disturbance direction had less influence on the dynamic response for the constant in situ stress state,while the failure intensity and damage range around the cavern always exhibited a monotonically increasing trend with an increase in the dynamic *** crack distribution around the circular cavern exhibited an asymmetric pattern,possibly owing to the stress-wave reflection behavior and attenuation effect along the propagation *** discontinuities significantly affected the stability of nearby caverns subjected to dynamic disturbances,during which the failure intensity exhibited the pattern of an initial increase followed by a decrease with an increase in the dip angle of the structural ***,the dynamic disturbance direction led to variations in the crack distribution for specific structural planes and stress *** results indicate that the failure behavior should be the integrated response of the excavation unloading effect,geological conditions,and external dynamic disturbances.

关键词： Underground cavern Dynamic disturbances Structural plane Crack propagation Failure intensity Excavation unloading

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