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Leaching kinetics and permeability of polyethyleneimine added ammonium sulfate on weathered crust elution-deposited rare earth ores

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Journal of Rare Earths 2024年第8期42卷 1610-1619,I0008页

作者： Huifang Yang Aoyang Sha Zhengyan He Chenjie Wu Yuanlai Xu Jingjing Hu Zhigao Xu Ru'an Chi Key Laboratory for Green Chemical Process of Ministry of Education School of Resources&Safety EngineeringWuhan Institute of TechnologyWuhan 430073China Key Laboratory of Resources Conversion and Pollution Control of the State Ethnic Affairs Commission College of Resources and Environmental ScienceSouth-Central MinzuUniversityWuhan430074China

At present,in-situ leaching of weathered crust elution-deposited rare earth ores(WCE-DREOs) encounter with problems such as long leaching cycles,slow infiltration rate and low product *** order to solve the above problems,the conventional leaching agent ammonium sulfate((NH_(4))_(2)SO_(4)) was compounded with polymeric surfactant polyethyleneimine(PEI) to form a composite leaching *** effects of leaching temperature,PEI *** rate and pH on leaching kinetics and permeability of rare earths(RE) and aluminum(AI) in orebody were *** is found that with temperature increasing,the time required to reach leaching equilibrium for both RE and Al is shortened,the apparent activation energies of RE and AI are 14.79 and 13.45 kJ/mol,respectively,and the leaching processes are in accordance with the outer diffusion control,When the concentrations of(NH_(4))_(2)SO_(4) and PEI in the composite leaching agent are 2.0 wt% and 0.4 wt%;the time required to reach leaching equilibrium for RE and AI is about 50% shorter than that using(NH_(4))_(2)SO_(4) alone,and the leaching efficiencies of RE are slightly higher than that of *** increasing the temperature and flow rate of the composite leaching agent can improve the leaching efficiencies of RE and Al,but pH has neglected effects on the leaching efficiencies of *** PEI concentrations below 0,4 wt%, the addition of PEI promotes the leaching of RE and *** column leaching studies of the WCE-DREO,the addition of 0.4 wt% PEI to the traditional leaching agent(NH_(4))_(2)SO_(4) has no impact on the leaching efficiencies of ***,it can significantly increase the infiltration rate of WCE-DREO,shortening the leaching time per 10 mL effluent from about 30 min for(NH_(4))_(2)SO_(4) leaching system to 20 min for the composite leaching *** leaching time is shortened by one-third,and the leaching cost is reduced,which can provide theoretical guidance for the development and commercial implementa

关键词： Polyethyleneimine Kinetics Rare earths Permeability Activation energy

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Recent advances in heterogeneous catalysis of solar-driven carbon dioxide conversion

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Journal of Environmental Sciences 2024年第6期140卷 165-182页

作者： Jun Xu Farzaneh Arabpour Roghabadi Ying Luo Vahid Ahmadi Qian Wang Zheng Wang Hong He State Key Joint Laboratory of Environment Simulation and Pollution Control Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing 100085China College of Chemistry and Chemical Engineering Henan Polytechnic UniversityJiaozuoHenan 454000China Department of Process Engineering Faculty of Chemical EngineeringTarbiat Modares UniversityTehranIran Optoelectronics and Nanophotonics Research Group Faculty of Electrical and Computer EngineeringTarbiat Modares UniversityTehranIran Graduate School of Engineering Nagoya UniversityFuro-choChikusa-kuNagoya 464-8603Japan

Solar-driven carbon dioxide(CO_(2))conversion including photocatalytic(PC),photoelectrochemical(PEC),photovoltaic plus electrochemical(PV/EC)systems,offers a renewable and scalable way to produce fuels and high-value chemicals for environment and energy *** review summarizes the basic fundament and the recent advances in the field of solar-driven CO_(2)*** the visible-light absorption is an important strategy to improve solar energy conversion *** separation and migration of photogenerated charges carriers to surface sites and the surface catalytic processes also determine the photocatalytic *** engineering including co-catalyst loading,defect engineering,morphology control,surface modification,surface phase junction,and Z-scheme photocatalytic system construction,have become fundamental strategies to obtain high-efficiency *** to photocatalysis,these strategies have been applied to improve the conversion efficiency and Faradaic efficiency of typical PEC *** PV/EC systems,the electrode surface structure and morphology,electrolyte effects,and mass transport conditions affect the activity and selectivity of electrochemical CO_(2)***,the challenges and prospects are addressed for the development of solar-driven CO_(2)conversion system with high energy conversion efficiency,high product selectivity and stability.

关键词： Solar-driven CO_(2)reduction Photocatalytic system Photoelectrochemical system Photovoltaic plus electrochemical system Surface/interface engineering

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Unscented Kalman Filter for State of Charge Estimation of Lithium Titanate Battery 20

Unscented Kalman Filter for State of Charge Estimation of Li...

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20th International Conference on Electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2023

作者： Dardchuntuk, Joshua Chun-Ken Banjerdpongchai, David Faculty of Engineering Chulalongkorn University Center of Excellence in Intelligent Control Automation of Process Systems Department of Electrical Engineering Pathumwan 254 Phayathai Road Bangkok10330 Thailand

ISBN: (纸本)9798350310467

An accurate estimation of the state of charge (SoC) of lithium titanate (LTO) batteries is required for their effective operation and management. In this study, we propose an unscented Kalman filter (UKF) approach for estimating the SoC of LTO batteries, which are challenging to assess due to the nonlinear voltage-SoC relationship and aging impact. Our approach uses a state and measurement model based on LTO's electrochemical characteristics and employs sigma points and weights to address nonlinearities. According to the findings of our research, the UKF-based methodology has high accuracy, rapid convergence, and resilience to discharge rate, outperforming or matching the capabilities of existing state-of-the-art approaches. This work provides a novel and effective solution for LTO battery SoC estimation, useful for applications in electric vehicles, energy storage, and smart grid energy systems. © 2023 IEEE.

关键词： Battery management systems

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Performance Comparison of Lightweight CNN Models for Drone Collision Avoidance Dataset 20

Performance Comparison of Lightweight CNN Models for Drone C...

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20th International Conference on Electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2023

作者： Zufar, Rifqi Nabila Banjerdpongchai, David Chulalongkorn University Center of Excellence in Intelligent Control Automation of Process Systems Department of Electrical Engineering Faculty of Engineering 254 Phayathai Road Bangkok Pathumwan10330 Thailand

ISBN: (纸本)9798350310467

Collision Avoidance System (CAS) is important for drone safety. CAS consists of three steps e.g., obstacle sensing, collision prediction, and collision avoidance. Collision prediction enables drones to gain information, process information, and estimate whether the object has a risk of collision. Convolution Neural Network (CNN) is one of the methods that can be employed for collision prediction. However, CNN is a method that needs a large data in the training. Dario Pedro et al. provided a dataset called the CoLANet dataset that consists of VDOs of collision drones. Subsequently, they proposed a new algorithm called Neural Network Pipeline which has a Convolution Neural Network (CNN) part to extract the feature from a couple of images. CNN extracts images by using MobileNetV2 as a pre-trained model. They chose MobileNetV2 due to training performance from another dataset. This paper aims to assess the performance of lightweight CNN models using the CoLANet dataset. The models will be trained on the Keras library with parameters of fewer than ten million. The models will be validated by Confusion Matrix and Receiver Operating Characteristics. In conclusion, we examine which pre-trained CNN model has the best performance and suggest ongoing work. © 2023 IEEE.

关键词： Drones

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Green innovation unleashed:Harnessing tungsten-based nanomaterials for catalyzing solar-driven carbon dioxide conversion

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Chinese Chemical Letters 2025年第2期36卷 108-117页

作者： Weidan Meng Yanbo Zhou Yi Zhou State Environmental Protection Key Laboratory of Environmental Risk Assessment and Control on Chemical Process East China University of Science and TechnologyShanghai 200237China National Engineering Research Center of Industrial Wastewater Detoxication and Resource Recovery East China University of Science and TechnologyShanghai 200237China Shanghai Engineering Research Center for Multi-media Environmental Catalysis and Resource Utilization East China University of Science and TechnologyShanghai 200237China Shanghai Institute of Pollution Control and Ecological Security Shanghai 200092China

Carbon dioxide photocatalytic reduction (CO_(2)-PR) is an efficient method for controlling CO_(2)emissions and generating cleaner energy while mitigating global *** oxides (WxOy) have attracted considerable attention for CO_(2)-PR due to their excellent spectral ***,comprehensive reviews are lacking on the use of WxOyfor CO_(2)-***,this review provides a detailed summary of t research progress made with WxOy-based catalysts in CO_(2)-*** also explains the fundamental principles of CO_(2)-PR and evaluates key performance indicators that affect the activity of WxOy-based photocatalysts,including yield,selectivity,stability,and apparent quantum ***,this review explores opportunities for synthesizing high-performance WxOy-based photocatalysts and highlights their potential for the green preparation of C1/C2 products through CO_(2)-*** innovative strategies aim to address the challenges and pressures associated with energy and environmental issues,particularly by enhancing artificial photosynthesis efficiency.

关键词： Carbon dioxide photoreduction Tungsten-based nanocatalyst Mechanism Performance evaluation Selectivity

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Deep convolutional autoencoder thermography for artwork defect detection

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Quantitative InfraRed Thermography Journal 2024年第6期21卷 367-383页

作者： Liu, Yi Wang, Fumin Liu, Kaixin Mostacci, Miranda Yao, Yuan Sfarra, Stefano Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou China Shanxi Key Laboratory of Signal Capturing & Processing North University of China Taiyuan China Professional Restorer Celano Italy Department of Chemical Engineering National Tsing Hua University Hsinchu Taiwan Department of Industrial and Information Engineering and Economics University of L’Aquila L’Aquila Italy

Infrared thermography is a cost-effective non-destructive evaluation technique that plays a critical role in extracting information about defects in cultural heritage such as works of art. However, in-depth studies on the internal structure of contemporary artworks using infrared thermography remain lacking, and therefore, a deep convolutional autoencoder thermography (DCAT) data analysis method is proposed for defect detection of contemporary artworks. In the proposed method, original data are enhanced by convolution to reduce the noise and inhomogeneous background in the original thermal images;subsequently, a deep autoencoder is used to extract nonlinear features from the enhanced thermographic data, and the results of the hidden layer are visualised. These visualised images highlight information regarding the internal structure and defects of the artwork. The case study on a handmade replica of Picasso’s ‘La Bouteille de Suze’, reproduced exactly as the original, has shown that DCAT significantly improved the accuracy of defect detection when compared with that of commonly used methods. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Nondestructive examination

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Integrating field images and microclimate data to realize multi-day ahead forecasting of maize crop coverage using CNN-LSTM

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International Journal of Agricultural and Biological engineering 2023年第2期16卷 199-206页

作者： Xin Wang Yu Yang Xin Zhao Min Huang Qibing Zhu Key Laboratory of Advanced Process Control for Light Industry Ministry of EducationJiangnan UniversityWuxi 214122JiangsuChina School of Physics and Electronic Electrical Engineering Huaiyin Normal UniversityHuai’an223300JiangsuChina

Crop coverage(CC)is an important parameter to represent crop growth characteristics,and the ahead forecasting of CC is helpful to track crop growth trends and guide agricultural management *** this study,a novel CNN-LSTM model that combined the advantages of convolutional neural network(CNN)in feature extraction and long short-term memory(LSTM)in time series processing was proposed for multi-day ahead forecasting of maize *** the influence of climate change on maize growth,five microclimatic factors were combined with historical maize CC estimated from field images as the input variables of the forecasting *** field experimental data of four observation points for more than three years were used to evaluate the performance of CNN-LSTM at the forecasting horizon of three to seven days ahead and compared the forecasting results to CNN and *** results demonstrated that CNN-LSTM obtained the lowest RMSE and the highest R2 at all forecasting ***,the performance of CNN-LSTM under univariate(historical maize CC)and multivariate(historical maize CC+microclimatic factors)input was compared,and the results indicated that additional microclimatic factors were effective in improving the forecasting ***,the 3-day ahead forecasting results of CNN-LSTM in different growth stages of maize were also analyzed,and the results showed that the highest forecasting accuracy was obtained in the seven leaves ***,CNN-LSTM can be considered a useful tool to forecast maize CC.

关键词： maize crop coverage multi-day ahead forecasting CNN-LSTM field images microclimatic factors

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Electroreduction of hexavalent chromium using a porous titanium flow-through electrode and intelligent prediction based on a back propagation neural network

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Frontiers of Environmental Science & engineering 2023年第8期17卷 69-79页

作者： Xinwan Zhang Guangyuan Meng Jinwen Hu Wanzi Xiao Tong Li Lehua Zhang Peng Chen National Engineering Laboratory for Industrial Wastewater Treatment School of Resources and Environmental EngineeringEast China University of Science and TechnologyShanghai 200237China Continuing Education Center Bozhou UniversityBozhou 236800China State Environmental Protection Key Laboratory of Environmental Risk Assessment and Control on Chemical Process East China University of Science and TechnologyShanghai 200237China Shanghai Institute of Pollution Control and Ecological Security Shanghai 200092China

Flow-through electrodes have been demonstrated to be effective for electroreduction of Cr(VI),but shortcomings are tedious preparation and short ***,porous titanium available in the market was studied as a flow-through electrode for Cr(VI)*** addition,the intelligent prediction of electrolytic performance based on a back propagation neural network(BPNN)was *** studies revealed that Cr(VI)electroreduction was a diffusion-controlled *** of the flow-through mode achieved a high limiting diffusion current as a result of enhanced mass transfer and favorable *** of Cr(VI)in the flow-through system was 1.95 times higher than in a parallel-plate electrode *** the influent(initial pH 2.0 and 106 mg/L Cr(VI))was treated at 5.0 V and a flux of 51 L/(h·m2),a reduction efficiency of~99.9%was obtained without cyclic electrolysis *** served as the supporting electrolyte and pH regulator,as reactive CrSO72−species were formed as a result of feeding HSO4−.Cr(III)was confirmed as the final product due to the sequential three-electron transport or disproportionation of the *** developed BPNN model achieved good prediction accuracy with respect to Cr(VI)electroreduction with a high correlation coefficient(R2=0.943).Additionally,the electroreduction efficiencies for various operating inputs were predicted based on the BPNN model,which demonstrates the evolutionary role of intelligent systems in future electrochemical technologies.

关键词： Flow-through electrode Hexavalent chromium Heavy metals Neural network Artificial intelligence

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Regularization based reweighted estimation algorithm for nonlinear systems in presence of outliers

Regularization based reweighted estimation algorithm for non...

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第43届中国控制会议

作者： MAO Yawen XU Chen School of science Jiangnan University Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) School of Internet of Things EngineeringJiangnan University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Outliers usually occur during data collection process and can lead to poor system identification performance. This paper considers the parameter estimation algorithms for Hammerstein systems whose output measurements are contaminated with additive outliers. By incorporating a lasso-type penalty on the outlier vector, a regularization based least squares identification algorithm that can interactively estimate the system parameters and outliers is proposed based on the auxiliary model identification idea. To further reduce the impact of outliers on the identification effect, each observation is assigned an individual weight, which decreases as the number of iterations increases once the corresponding data is determined to be an outlier. Finally,the outliers can be excluded from the contaminated data without any prior knowledge and the estimation bias is reduced. A numerical example is given to confirm the effectiveness of the proposed algorithm.

关键词： System identification Hammerstein system Outlier Regularization Auxiliary model

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Geobacter sulfurreducens promoted the biosynthesis of reduced graphene oxide and coupled it for nitrobenzene reduction

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Journal of Environmental Sciences 2024年第4期138卷 458-469页

作者： Shoujuan Zhang Qi Liu Linrui Zhong Jianhong Jiang Xiaozhe Luo Xingxin Hu Qian Liu Yue Lu College of Environmental Science and Engineering Hunan UniversityChangsha 410082China Shenzhen Research Institute Hunan UniversityShenzhen 510082China Key Laboratory of Environment Biology and Pollution Control Hunan UniversityMinistry of EducationChangsha 410082China China Machinery International Engineering Design&Research Institute Co. LtdChangsha 410007China Hunan Engineering Research Center for Water Treatment Process&Equipment Changsha 410007China

In order to explore an efficient and green method to deal with nitrobenzene(NB)pollutant,reduced graphene oxide(r GO)as an electron shuttle was applied to enhance the extracellular electron transfer(EET)process of Geobacter sulfurreducens,which was a typical electrochemically active bacteria(EAB).In this study,r GO biosynthesis was achieved via the reduction of graphene oxide(GO)by *** PCA within 3 ***,the r GOPCA combining system completely reduced 50-200μmol/L of NB to aniline as end product within one *** characterization revealed that PCA cells were partly wrapped by rGO,and therefore the distance of electron transfer between strain PCA and r GO material was ***,the ID/IGof GO,r GO,and r GO-PCA combining system were 0.990,1.293 and 1.31,***,highest currents were observed in r GO-PCA-NB as 12.950μA/-12.560μA at -408 m V/156 m V,attributing to the faster electron transfer efficiency in EET ***,the NB reduction was mainly due to:(I)direct EET process from *** PCA to NB;(II)r GO served as electron shuttle and accelerated electron transfer to NB,which was the main degradation ***,the biosynthesis of r GO via GO reduction by Geobacter promoted the NB removal process,which provided a facile strategy to alleviate the problematic nitroaromatic pollution in the environment.

关键词： Reduced graphene oxide Geobacter Nitrobenzene Removal Extracellular electron transfer

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