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Deep learning-based prediction of effluent quality of a constructed wetland

Environmental science and Ecotechnology 2023年第1期13卷 64-74页

作者： Bowen Yang Zijie Xiao Qingjie Meng Yuan Yuan Wenqian Wang Haoyu Wang Yongmei Wang Xiaochi Feng State Key Laboratory of Urban Water Resource and Environment School of Civil and Environmental EngineeringHarbin Institute of Technology(Shenzhen)ShenzhenGuangdong518055PR China Shenzhen Shenshui Water Resources Consulting CO LTDShenzhenGuangdong518022PR China College of Biological Engineering Beijing PolytechnicBeijing10076PR China State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control School of Environmental Science and EngineeringSouthern University of Science and TechnologyShenzhen518055China

Data-driven approaches that make timely predictions about pollutant concentrations in the effluent of constructed wetlands are essential for improving the treatment performance of constructed ***,the effect of the meteorological condition and flow changes in a real scenario are generally neglected in water quality *** address this problem,in this study,we propose an approach based on multi-source data fusion that considers the following indicators:water quality indicators,water quantity indicators,and meteorological *** this study,we establish four representative methods to simultaneously predict the concentrations of three representative pollutants in the effluent of a practical large-scale constructed wetland:(1)multiple linear regression;(2)backpropagation neural network(BPNN);(3)genetic algorithm combined with the BPNN to solve the local minima problem;and(4)long short-term memory(LSTM)neural network to consider the influence of past results on the *** results suggest that the LSTM-predicting model performed considerably better than the other deep neural network-based model or linear method,with a satisfactory R^(2).Additionally,given the huge fluctuation of different pollutant concentrations in the effluent,we used a moving average method to smooth the original data,which successfully improved the accuracy of traditional neural networks and hybrid neural *** results of this study indicate that the hybrid modeling concept that combines intelligent and scientific data preprocessing methods with deep learning algorithms is a feasible approach for forecasting water quality in the effluent of actual engineering.

关键词： LSTM Constructed wetlands Water quality prediction Deep learning Multi-source data fusion

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Effect of fractures on mechanical behavior of sand powder 3D printing rock analogue under triaxial compression

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Journal of Central South University 2024年第8期31卷 2703-2716页

作者： LI Pi-mao JIANG Li-shuai WEN Zhi-jie WU Chao-lei YANG Yi-ming PENG Xiao-han WU Quan-sen WU Quan-lin State Key Laboratory of Mining Disaster Prevention and Control Shandong University of Science and TechnologyQingdao 266590China Mining College Guizhou UniversityGuiyang 550025China School of Engineering Jining UniversityQufu 273100China

In practical engineering applications,rock mass are often found to be subjected to a triaxial stress ***,defects like joints and fractures have a notable impact on the mechanical behavior of rock *** defects are identified as crucial contributors to the failure and instability of the surrounding rock,subsequently impacting the engineering *** study aimed to investigate the impact of fracture geometry and confining pressure on the deformation,failure characteristics,and strength of specimens using sand powder 3D printing technology and conventional triaxial compression *** results indicate that the number of fractures present considerably influences the peak strength,axial peak strain and elastic modulus of the *** pressure is an important factor affecting the failure pattern of the specimen,under which the specimen is more prone to shear failure,but the initiation,expansion and penetration processes of secondary cracks in different fracture specimens are *** study confirmed the feasibility of using sand powder 3D printing specimens as soft rock analogs for triaxial compression *** insights from this research are deemed essential for a deeper understanding of the mechanical behavior of fractured surrounding rocks when under triaxial stress state.

关键词： sand powder 3D printing triaxial compression confining pressure fracture geometry mechanical behavior

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Hunger games search optimization with deep learning model for sustainable supply chain management

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Discover Internet of Things 2023年第1期3卷 10页

作者： Xu, Zheng Jain, Deepak Kumar Neelakandan, S. Abawajy, Jemal School of Computer and Information Engineering Institute for Artificial Intelligence Shanghai Polytechnic University Shanghai201209 China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian116024 China School of Artificial Intelligence Dalian China Symbiosis Institute of Technology Symbiosis International University Pune India Department of Computer Science and Engineering R.M.K Engineering College Taluk India Deakin University Geelong Australia

The supply chain network is one of the most important areas of focus in the majority of business circumstances. Blockchain technology is a feasible choice for secure information sharing in a supply chain network. Despite the fact that maintaining security at all levels of the blockchain is difficult, cryptographic methods are commonly used in existing works. Effective supply chain management (SCM) offers various benefits to organizations, such as enhanced customer satisfaction, increased operational efficiency, competitive advantage, and cost reduction. Potential SCM for agricultural and food supply chains needs distributors, coordination and collaboration among farmers, retailers, and stakeholders. The use of technology like Block Chain (BC), sensors, and data analytics, can boost traceability and visibility, decrease waste, and ensure safety and quality throughout the supply chain. Therefore, this study develops a Hunger Games Search Optimization with Deep Learning Model for Sustainable agricultural and food Supply Chain Management (HGSODL-ASCM) technique. The fundamental goal of the HGSODL-ASCM technique is to improve decision-making processes for agricultural and food commodity production and storage in order to optimise revenue. In the provided HGSODL-ASCM technique, a bidirectional long short-term memory (Bi-LSTM) model is built to determine the amount of productivity and storage required to maximise profit. In order to boost the performance of the Bi-LSTM classification process, the HGSO algorithm has been utilized in this work. The presented HGSODL-ASCM technique can independently achieve the SCM policies via interaction with complicated and adaptive environments. A brief set of simulations were performed to ensure the improved performance of the HGSODL-ASCM technique. The simulation results demonstrated how superior the HGSODL-ASCM method is to other methods already in use. © The Author(s) 2023.

关键词： Food supply

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Nitrogen-doped carbon@TiO_(2) double-shelled hollow spheres as an electrochemical sensor for simultaneous determination of dopamine and paracetamol in human serum and saliva

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Journal of Pharmaceutical Analysis 2022年第3期12卷 436-445页

作者： Hui Yang Gongxun Cao Yongjun Huang Ye Lin Fengying Zheng Luxiu Lin Fengjiao Liu Shunxing Li College of Chemistry Chemical Engineering and EnvironmentMinnan Normal UniversityZhangzhouFujian363000China Fujian Provincial Key Laboratory of Pollution Monitoring and Control Minnan Normal UniversityZhangzhouFujian363000China Fujian Key Laboratory of Separation and Analysis Science and Technology ZhangzhouFujian363000China

As the most commonly used antipyretic and analgesic drug,paracetamol(PA)coexists with neurotransmitter dopamine(DA)in real biological *** simultaneous determination is extremely important for human health,but they also interfere with each *** order to improve the conductivity,adsorption affinity,sensitivity,and selectivity of TiO_(2)-based electrochemical sensor,N-doped carbon@-TiO_(2) double-shelled hollow sphere(HeC/N@TiO_(2))is designed and synthesized by simple alcoholic and hydrothermal method,using polystyrene sphere(PS)as a ***,TiO_(2) hollow spheres(H eTiO_(2))or N-doped carbon hollow spheres(HeC/N)are also prepared by the same ***/N@TiO_(2) has good conductivity,charge separation,and the highly enhanced and stable current responses for the detection of PA and *** detection limit and linear range are 50.0 nmol/L and 0.3-50 mmol/L for PA,40.0 nmol/L and 0.3e50 mmol/L for DA,respectively,which are better than those of carbon-based ***,this electrochemical sensor,with high selectivity,strong anti-interference,high reliability,and long time durability,can be used for the simultaneous detection of PA and DA in human blood serum and *** high electrochemical performance of HeC/N@TiO_(2) is attributed to the multifunctional combination of different layers,because of good conductivity,absorption and electrons transfer ability from in-situ N-doped carbon and electrocatalytic activity from TiO_(2).

关键词： Electrochemical sensor Simultaneous determination Paracetamol Dopamine

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Recent advances in biomolecule-based films and coatings for active and smart food packaging applications

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Food Bioscience 2023年第2期52卷 1-12页

作者： Wenqi Yin Chao Qiu Hangyan Ji Xiaojing Li Shangyuan Sang David Julian McClements Aiquan Jiao Jinpeng Wang Zhengyu Jin State Key Laboratory of Food Science and Technology School of Food Science and TechnologyCollaborative Innovation Center of Food Safety and Quality Control in Jiangsu ProvinceJiangnan UniversityWuxiJiangsu214122China College of Light Industry and Food Engineering Nanjing Forestry UniversityJiangsu210037China Key Laboratory of Animal Protein Food Deep Processing Technology of Zhejiang Province College of Food and Pharmaceutical SciencesNingbo UniversityNingbo315832China Department of Food Science University of MassachusettsAmherstMA01060United States

Ideally,packaging materials should ensure the safety and quality of foods,without contributing to environmental ***,there is interest in the development of biodegradable films assembled from natural materials,such as polysaccharides,proteins,lipids,and their *** this review,the physicochemical attributes and functional characteristics of biodegradable films prepared from these food-grade natural substances are *** advances in the production of active and intelligent packaging materials are also *** packaging is designed to improve the shelf life of packaged foods by including antimicrobials or antioxidants,such as essential *** packaging is designed to provide real-time information about the quality,freshness,or safety of packaged foods by including indicators in the film that are responsive to changes in storage conditions,gas levels,pH,*** applications of intelligent and active packaging materials to fruit,vegetable,meat,seafood,and dairy products are discussed.

关键词： Biodegradable packaging Food-grade natural substances Active agents Smart packaging Food preservation

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Predicting the Risk of Arterial Stiffness in Coal Miners Based on Different Machine Learning Models

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Biomedical and Environmental sciences 2024年第1期37卷 108-111页

作者： CHEN Qian Wei HUANG Xue Zan DING Yu ZHU Feng Ren WANG Jia ZOU Yuan Jie DU Yuan Zhen ZHANG Ya Jun HUI Zi Wen ZHU Feng Lin MU Min The Project was supported by Open Research Grant of Joint National-Local Engineering Research Centre for Safe and Precise Coal Mining Anhui University of Science and TechnologyHefei 232001AnhuiChina School of Public Health Anhui University of Science and TechnologyHefei 232001AnhuiChina Key Laboratory of Industrial Dust Prevention and Control Occupational Safety and HealthMinistry of EducationAnhui University of Science and TechnologyHefei 232001AnhuiChina Anhui Institute of Occupational Safety and Health Anhui University of Science and TechnologyHefei 232001AnhuiChina Joint Research Center of Occupational Medicine and Health Institute of Grand HealthHefei Comprehensive National Science CenterAnhui University of Science and TechnologyHefei 232001AnhuiChina Department of Occupational&Environmental Health School of Public HealthTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030HubeiChina Key Laboratory of Environment and Health Ministry of Education&Ministry of Environmental Protectionand State Key Laboratory of Environmental Health(Incubating)School of Public HealthTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030HubeiChina

Coal is one of the world’s main energy resources,accounting for approximately 68%of China’s current total power ***,several studies have demonstrated that dust,exhaust fumes,and other harmful factors in coal mines increase the risk of cardiovascular disease(CVD)among miners^([1]).Arterial stiffness(AS)is an independent risk factor of CVD,and epidemiological studies have shown that AS plays a vital role in assessing the risk of CVD^([2]).

关键词： Arterial cardiovascular assessing

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Detection-Triggered Recursive Impact Mitigation against Secondary False Data Injection Attacks in Microgrids

arXiv

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arXiv 2024年

作者： Liu, Mengxiang Zhang, Xin Zhang, Rui Zhou, Zhuoran Zhang, Zhenyong Deng, Ruilong The Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom The National Grid ESO Wokingham United Kingdom The State Key Laboratory of Industrial Control Technology The College of Control Science and Engineering Zhejiang University Hangzhou China

The cybersecurity of microgrid has received widespread attentions due to the frequently reported attack accidents against distributed energy resource (DER) manufactures. Numerous impact mitigation schemes have been proposed to reduce or eliminate the impacts of false data injection attacks (FDIAs). Nevertheless, the existing methods either requires at least one neighboring trustworthy agent or may bring in unacceptable cost burdens. This paper aims to propose a detection-triggered recursive impact mitigation scheme that can timely and precisely counter the secondary FDIAs (SFDIAs) against the communication links among DERs. Once triggering attack alarms, the power line current readings will be utilised to observe the voltage bias injections through the physical interconnections among DERs, based on which the current bias injections can be recursively reconstructed from the residuals generated by unknown input observers (UIOs). The attack impacts are eliminated by subtracting the reconstructed bias from the incoming compromised data. The proposed mitigation method can work even in the worst case where all communication links are under SFDIAs and only require extra current sensors. The bias reconstruction performance under initial errors and system noises is theoretically analysed and the reconstruction error is proved to be bounded regardless of the electrical parameters. To avoid deploying current sensors on all power lines, a cost-effective deployment strategy is presented to secure a spanning tree set of communication links that can guarantee the secondary control performance. Extensive validation studies are conducted in MATLAB/SIMULINK and hardware-in-the-loop (HIL) testbeds to validate the proposed method’s effectiveness against single/multiple and continuous/discontinuous SFDIAs. Copyright © 2024, The Authors. All rights reserved.

关键词： Cost effectiveness

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Multivariate Deep Reconstruction Neural Network for Multi-step-ahead Prediction of industrial Process Quality Variables

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IFAC-PapersOnLine 2023年第2期56卷 2852-2857页

作者： Yuwei Chen Xinmin Zhang Zhihuan Song Manabu Kano State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou 310027 P. R. China Department of Systems Science Kyoto University Kyoto 606-8501 Japan

In industrial processes, deep learning has been widely used to solve the soft sensing problem. Multi-step-ahead prediction is one of the most challenging problems in the field of soft sensors. Recently, N-Beats has been proposed as a promising deep neural architecture for multi-step prediction, but it can only be used for univariate time series prediction, not for industrial soft sensor modeling. Inspired by N-Beats, a novel deep learning model, multivariate deep reconstruction neural network (MDRNN), is proposed for multivariate time series prediction in this work. MDRNN is designed on the basis of a doubly residual structure with a deep stack of fully-connect layers. MDRNN inherits the merits of N-Beats and incorporates the “doubly residual stacking” idea into the industrial soft sensor modeling to improve the prediction accuracy. To evaluate the feasibility and effectiveness of the proposed MDRNN, it is applied to the quality prediction task and validated with two real-world industrial processes. The application results demonstrated that the proposed MDRNN can achieve higher prediction accuracy compared to the existing N-Beats and Multilayer Perceptron (MLP)-based methods.

关键词： Soft sensor multi-step-ahead prediction multivariate time series deep learning multivariate deep reconstruction neural network

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Boosting Peroxymonosulfate Activation via Co-Based LDH-Derived Magnetic Catalysts:A Dynamic and Static State Assessment of Efficient Radical-Assisted Electron Transfer Processes

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Energy & Environmental Materials 2024年第5期7卷 301-312页

作者： Wenhan Yang Junming Xia Fanfan Shang Ge Ge Yang Bin Wang Hairui Cai Lingyun Jing Hao Zhu Shengchun Yang Chao Liang Guosheng Shao MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter School of PhysicsNational Innovation Platform(Center)for Industry-Education Integration of Energy Storage TechnologyXi'an Jiaotong UniversityXi'an 710000China State Key Laboratory of Organic Electronics and Information Displays&Institute of Advanced Materials(1AM) Nanjing University of Posts&Telecommunications9 Wenyuan RoadNanjing 210023China College of Petroleum and Chemical Engineering Lanzhou University of TechnologyLanzhou 730050China Key Laboratory of Western China's Environmental Systems(Ministry of Education)and Gansu Engineering Research Center of Fine Particles Pollution Control Technology and Equipment College of Earth and Environmental SciencesLanzhou UniversityLanzhou 730000China State Center for International Cooperation on Designer Low-Carbon&Environmental Materials(CDLCEM) School of Materials Science and EngineeringZhengzhou UniversityZhengzhou 450001China

Heterogeneous catalysts promoting efficient production of reactive species and dynamically stabilized electron transfer mechanisms for peroxomonosulfates(PMS)still lack systematic ***,a more stable magnetic layered double oxides(CFLDO/N-C),was designed using self-polymerization and high temperature carbonization of *** CFLDO/N-C/PMS system effectively activated PMS to remove 99%(k=0.737 min^(-1))of tetracycline(TC)within 10 *** CFLDO/N-C/PMS system exhibited favorable resistance to inorganic anions and natural organics,as well as satisfactory suitability for multiple *** magnetic properties of the catalyst facilitated the separation of catalysts from the liquid phase,resulting in excellent reproducibility and effectively reducing the leaching of metal *** electronic bridge was constructed between cobalt(the active platform of the catalyst)and PMS,inducing PMS to break the O-O bond to generate the active *** combination of static analysis and dynamic evolution confirmed the effective adsorption of PMS on the catalyst surface as well as the strong radical-assisted electron transfer ***,we further identified the sites where the reactive species attacked the TC and evaluated the toxicity of the *** findings offer innovative insights into the rapid degradation of pollutants achieved by transition metals in SR-AOPs and its mechanistic elaboration.

关键词： advanced oxidation process degradation mechanisms electron transport layered double hydroxide reactive species

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Transferable Intervalization and Midpoint De-Mixing Interval Independent Component Analysis for industrial Fault Detection Under Data Uncertainty

SSRN

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SSRN 2024年

作者： Zhang, Shumei Wang, Sijia Dong, Feng Zhao, Chunhui Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin University Tianjin373200 China School of Information and Intelligent Engineering Tianjin Renai College Tianjin301636 China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China

Due to noise disturbance, sensor drift, and some circumstance factors, process data are unavoidably contaminated with uncertainty, which may destroy the correlation structure of original data and make traditional process monitoring methods perform poorly. In this study, a transferable Gaussian mixture learning (TGML) method is developed to make full use of the important knowledge extracted from source domains to guide and improve the intervalization process of target domain with limited data. Then, a midpoint de-mixing interval independent component analysis (MDM-I2CA) is proposed for non-Gaussian processes with uncertainty. First, MDM-I2CA expands the traditional centering and whitening methods into interval domain to concentrate the data information on higher-order statistics. Second, a midpoint coding strategy is developed to depict the process information with the idea of focusing on the variation between intervals of a dataset, and the statistically interval independent components are further de-mixed. Finally, four monitoring statistics are constructed for fault detection. © 2024, The Authors. All rights reserved.

关键词： Fault detection

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