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SSRN 2024年

作者： Xu, Dongbing Wang, Chi Chang, Chao Tan, Boren Yu, Xiaohua Duan, Xiaofei Wang, Yong Chen, Feiyue National Engineering Research Center of Green Recycling for Strategic Metal Resources Institute of Process Engineering Chinese Academy of Sciences Beijing100090 China University of Chinese Academy of Sciences Beijing100049 China School of Chemical Engineering and Technology China University of Mining and Technology Xuzhou221116 China ARC Research Hub for Smart Process Design and Control Department of Chemical & Biological Engineering Monash University ClaytonVIC3800 Australia Kunming University of Science and Technology China Melbourne TrACEES Platform and School of Chemistry Australia Nanjing IPE Institute of Green Manufacturing Industry China

A detailed experimental study is performed to understand the effect of scaling up on the hydrodynamic behavior of Pulsed Disc and Doughnut Columns (PDDCs) using three different systems without mass transfer. The phase system used in the experiments consists of tap water as the dispersed phase and 10% P507/kerosene with different saponification rates as the continuous phase. Dispersed phase holdup and Sauter mean diameter (d32) were measured under different operating conditions. The operating parameters that have varied are the pulsation intensity and flow rate of both liquid phases. The geometrical parameters are plate spacing spacing and column diameter. Further, the empirical correlations based on the similarity principle have been proposed to predict the dispersed phased holdup and Sauter mean diameter for a better understanding of the scale-up process. © 2024, The Authors. All rights reserved.

关键词： Mass transfer

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Deep Autoencoder for Non-destructive Testing of Defects in Polymer Composites

Deep Autoencoder for Non-destructive Testing of Defects in P...

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2021 International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2021

作者： Zheng, Mingkai Liu, Kaixin Li, Nanxin Yao, Yuan Liu, Yi Zhejiang University of Technology Institute of Process Equipment and Control Engineering Hangzhou310023 China China E-Port Information Data Center Shanghai Branch Shanghai200120 China National Tsing Hua University Department of Chemical Engineering Hsinchu30013 Taiwan

ISBN: (纸本)9781665402453

Infrared thermography (IRT) is an efficient non-destructive testing technique, which is widely applied in defect detection of polymer composites. However, the nonlinear nature of the thermographic data and the adverse effects of noise and inhomogeneous background prevent IRT from delivering satisfactory results. A novel deep autoencoder thermography (DAT) method is developed to enhance the contrast between defects and background. The multi-layer structure of the deep autoencoder is used to extract the features. Then, the results of the middle-hidden layer are visualized to show the effects of removing noise and uneven background. As a result, the defect is highlighted in the visualized images. The feasibility of the DAT method is verified using the experiment of carbon fiber reinforced polymer specimen. © 2021 IEEE.

关键词： Nondestructive examination

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Improving Autoencoder Training with novel Goal Functions based on Multivariable control Concepts

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IFAC-PapersOnLine 2021年第3期54卷 73-78页

作者： Rafael H. Martello Lucas Ranzan Marcelo Farenzena Jorge O. Trierweiler Group of Intensification Modeling Simulation Control and Optimization of Process GIMSCOP Federal University of Rio Grande do Sul Chemical Engineering Department 90040-040 Porto Alegre RS Brazil

Autoencoders are becoming more representative in all fields of knowledge, due to their ability to classify, compress, and identify data patterns. This study objective was to propose entirely new objective functions using multivariable process control concepts as the gain matrix and Relative Gain Array to improve the quality of prediction and classification of an autoencoder. The advantages of the proposed approach are illustrated through a pulp-and-paper industry. The new function results show an improvement in the detection, leading to savings of up to 22 to 38 thousand dollars per month compared to a model using only MSE.

关键词： Classifier Neural-network models Paper industry Predictive control Optimization problems

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Analysis of concentration polarization in reverse osmosis and nanofiltration: zero-, one-, and two-dimensional models

arXiv

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arXiv 2024年

作者： Biesheuvel, P.M. Porada, S. Blankert, B. Ryzhkov, I. Elimelech, M. Wetsus European Centre of Excellence for Sustainable Water Technology Netherlands Department of Process Engineering and Technology of Polymer and Carbon Materials Wroclaw University of Science and Technology Poland Water Desalination and Reuse Center King Abdullah University of Science and Technology Saudi Arabia Institute of Computational Modeling SB RAS Krasnoyarsk Russia Siberian Federal University Krasnoyarsk Russia Department of Chemical and Environmental Engineering Yale University United States

Reverse osmosis and nanofiltration are membrane-based methods that remove solutes from solvent, for instance they remove salts from water (desalination). In these methods, an applied pressure is the driving force for solvent to pass the membrane, while most of the solutes are blocked. Very important in the theory of mass transport is the concentration polarization layer (CP layer), which develops on the upstream side of the membrane. Because of the CP layer, the solvent flux through the membrane is reduced while leakage of solutes through the membrane increases, and both these effects must be minimized. So it is very important to understand and describe the nature of the CP layer accurately, especially to find a good estimate of the CP layer mass transfer coefficient, k. This is also important for the accurate characterization of membranes in a test cell geometry. We theoretically analyse the structure of the CP layer using three levels of mathematical models. First, we present a modification of an equation for k by Sherwood et al. (1965) and show that it works very well in a zero dimensional model. Second, we evaluate a one-dimensional model that is more accurate, which can incorporate any equation for the flow of solvent and solutes through the membrane, and which also makes use of the new modified Sherwood equation. Finally, we fully resolve the complete channel in a two-dimensional geometry, to validate the lower-order models and to illustrate the structure of the CP layer. The overall conclusion is that for typical test cell conditions, the modified Sherwood equation can be used to characterize the CP layer, also when solvent flux through the membrane changes between inlet and outlet of the test cell. Furthermore, the one-dimensional model accurately describes solute removal (for instance water desalination) not just in a short test cell but also in a longer module. Copyright © 2024, The Authors. All rights reserved.

关键词： Solvents

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OSF-JS: Orthonormal Slow Feature-Based Quality-Related Data Decomposition Network With Jensen–Shannon Divergence for chemical processes Incipient Fault Detection

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Wang, Jiayu Yao, Le Xiong, Weili Cui, Xiaohui Yu, Wei Young, Brent Ministry of Education Key Laboratory of Advanced Process Control for Light Industry Wuxi214122 China Jiangnan University School of Internet of Things Engineering Wuxi214122 China Hangzhou Normal University School of Mathematics Hangzhou311121 China Jiangnan University Science Center for Future Foods Wuxi214122 China Wuhan University School of Cyber Science and Engineering Hubei Wuhan430072 China University of Auckland Department of Chemical and Materials Engineering Auckland1010 New Zealand

In chemical processes, there are both nonlinearity and slow dynamics among process variables. Meanwhile, at the beginning of faults, slight variation may not prompt the significant parametric changes. Traditional fault detection methods, based on mean and variation shifts, often fail to achieve satisfactory performance. To improve the detection of incipient quality-related faults, a novel orthonormal slow feature-based quality-related data decomposition network with Jensen-Shannon divergence (OSF-JS) fault detection method is proposed. The orthonormal subspace analysis (OSA) is embedded with Siamese to extract orthonormal slow features (OSFs) from decomposed subspaces. Enhancing the detection of incipient faults, JS divergence combined with a sliding window algorithm is introduced to construct statistics. The effectiveness of OSF-JS is validated for a wastewater treatment process and a real-world chemical process. Results show that the proposed OSF-JS method not only improves the fault detection rate (FDR) and reduces false alarm rate (FAR) but also identifies whether faults occur in the quality-related component. © 1963-2012 IEEE.

关键词： Fault detection Feature extraction chemical processes Monitoring Kernel Principal component analysis Windows Wastewater treatment Industries Decoding

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Large Scale and High-Precision Crop Recognition Using Satellite Remote Sensing Images

Large Scale and High-Precision Crop Recognition Using Satell...

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Chinese Automation Congress (CAC)

作者： Xuanting Tao Jinglu Tan Jianguo Zhang Suleyman I. Allakhverdiev Zhenyu Xu Ya Guo Key Laboratory of Advanced Process Control for Light Industry Ministry of Education Jiangnan University Wuxi China Department of Chemical & Biomedical Engineering University of Missouri Columbia Missouri USA Wuxi Agricultural Equipment Technology Extension Center Wuxi China Timiryazev Institute of Plant Physiology RAS Moscow Russia Longcom Internet of Things Co. Ltd Hefei China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Precise large-scale crop recognition is crucial for estimating crop yield and carbon assimilation. Current research on crop type recognition based on deep learning is often limited to a single crop and small areas, with poor model transferability. When performing multi-crop recognition over large areas, the accuracy significantly decreases. Therefore, it is necessary to establish a high-precision crop type recognition model with strong generalization capability. A deep learning model (LSTM-Transformer Parallel, LTP) based on the combination of LSTM (Long Short-Term Memory) network and Transformer self-attention mechanism architecture is proposed in this paper. The model captures the dependencies among crop NDVI time series data to achieve high-precision recognition of large-scale crop types. Experimental results show that in crop recognition experiments using small-scale agricultural area data, the recognition results of the LTP, TSMixer, and SegRNN models all performed excellently, with average accuracies of 94.55%, 94.41%, and 94.79%, respectively. In contrast, in crop recognition experiments using large-scale agricultural area data, the average accuracy of the LTP model reaches 93.77%, while the accuracies of the TSMixer and SegRNN models are 82.30% and 84.13%, respectively. This indicates that the LTP model not only performs well on small-scale agricultural area datasets but also significantly outperforms models in the literature on large-scale agricultural area datasets, demonstrating strong generalization capability. It provides a reliable method for large-scale multi-crop recognition, contributing to global food security and carbon cycle assessment.

关键词： Deep learning Analytical models Accuracy Satellites Time series analysis Crops Transformers Data models Long short term memory Remote sensing

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Significant chlorine emissions from biomass burning affect the long-term atmospheric chemistry in Asia

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National Science Review 2024年第9期11卷 213-222页

作者： Di Chang Qinyi Li Zhe Wang Jianing Dai Xiao Fu Jia Guo Lei Zhu Dongchuan Pu Carlos A.Cuevas Rafael P.Fernandez Weigang Wang Maofa Ge Jimmy C.H.Fung Alexis K.H.Lau Claire Granier Guy Brasseur Andrea Pozzer Alfonso Saiz-Lopez Yu Song Tao Wang Atmospheric Chemistry Department Max Planck Institute for Chemistry Department of Atmospheric Chemistry and Climate Institute of Physical Chemistry Blas CabreraCSIC Department of Civil and Environmental Engineering The Hong Kong Polytechnic University Environment Research Institute Shandong University Division of Environment and Sustainability The Hong Kong University of Science and Technology Environmental Modeling Group Max Planck Institute for Meteorology Institute of Environment and Ecology Tsinghua Shenzhen International Graduate SchoolTsinghua University Environmental Central Facility Institute for the Environment The Hong Kong University of Science and Technology Atmospheric Chemistry Modeling& Remote Sensing Research Group Southern University of Science and Technology Institute for Interdisciplinary Science (ICB) National Research Council (CONICET)FCEN-UNCuyo Institute of Chemistry Chinese Academy of Sciences NOAA Chemical Sciences Laboratory/CIRES University of Colorado Laboratoire d’Aerologie CNRSUniversity of Toulouse UPS Atmospheric Chemistry Observation & Modeling Laboratory National Center for Atmospheric Research State Key Joint Laboratory of Environmental Simulation and Pollution Control Department of Environmental Science Peking University

Biomass burning（BB） is a major source of trace gases and particles in the atmosphere,influencing air quality,radiative balance,and *** studies have mainly focused on the BB emissions of carbon and nitrogen species with less attention on *** chlorine chemistry has significant effects on atmospheric chemistry and air ***,quantitative information on chlorine emissions from BB,particularly the long-term trend and associated atmospheric impacts,is limited both on regional and global ***,we report a long-term（20 01-2018） high-resolution BB emission inventory for the major chlorine-containing compounds（HCl,chloride,and CH3Cl） in Asia based on satellite *** estimate an average of 730 Gg yr-1chlorine emitted from BB activity in Asia,with China contributing the largest share at 24.2%(177 Gg yr-1),followed by Myanmar at 18.7% and India at 18.3%.Distinct seasonal patterns and significant spatial and interannual variability are observe d,mainly driven by human-mediated changes in agricultural *** incorporating the newly developed chlorine emission inventory into a global chemistry-climate model（CAM-Chem）,we find that the BB-chlorine emissions lead to elevated levels of HCl and CH3Cl(monthly average up to 2062 and 1421 parts per trillion by volume（pptv）,respectively),subsequently resulting in noticeable changes in oxidants（up to 3.1% in O3and 17% in OH radicals）.The results demonstrate that BB is not only a significant source of air pollutants but also of oxidants,suggesting a larger role of BB emissions in the atmospheric chemistry and oxidation process than previously *** light of the projected increase in BB activity toward the end of the century and the extensive control of anthropogenic emissions worldwide,the contribution of BB emissions may become fundamental to air quality composition in the future.

关键词： biomass burning emission chlorine emission inventory tropospheric halogen chemistry atmospheric oxidants CAM-Chem model

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Effects of phosphate on the transport of graphene oxide nanoparticles in saturated clean and iron oxide-coated sand columns

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Journal of Environmental Sciences 2021年第5期33卷 80-92页

作者： Jiuyan Chen Weifeng Chen Taotao Lu Yumeng Song Haojing Zhang Mengjie Wang Xinhai Wang Zhichong Qi Minghua Lu Henan International Joint Laboratory of Medicinal Plants Utilization Henan Joint International Research Laboratory of Environmental Pollution Control MaterialsEngineering Research Center for Industrial Recirculation Water Treatment of Henan ProvinceCollege of Chemistry and Chemical EngineeringHenan UniversityKaifeng 475004China Ministry of Education Key Laboratory of Pollution Processes and Environmental Criteria Nankai UniversityTianjin 300350China Ministry of Education Key Laboratory of Humid Subtropical Eco-geographical Process Fujian Provincial Key Laboratory for Plant Eco-physiologyCollege of Geographical ScienceFujian Normal UniversityFujian 350007China Department of Hydrology University of BayreuthBayreuth D-95440Germany

In this study, transport behaviors of graphene oxide(GO) in saturated uncoated(i.e., clean sand) and goethite-coated sand porous media were examined as a function of the phosphate. We found that phosphate enhanced the transport of GO over a wide range of solution chemistry(i.e., pH 5.0–9.0 and the presence of 10 mmol/L Na^(+) or 0.5 mmol/L Ca^(2+)). The results were mainly ascribed to the increase of electrostatic repulsion between nanoparticles and porous media. Meanwhile, deposition site competition induced by the retained phosphate was another important mechanism leading to promote GO transport. Interestingly, when the phosphate concentration increased from 0.1 to 1.0 mmol/L, the transportenhancement effect of phosphate in goethite-coated sand was to a much larger extent than that in clean sand. The observations were primarily related to the difference in the total mass of retained phosphate between the iron oxide-coated sand and clean sand columns, which resulted in different degrees of the electrostatic repulsion and competitive effect of phosphate. When the background solution contained 0.5 mmol/L Ca^(2+), phosphate could be bind to sand/goethite-coated sand surface by cation bridging;and consequently, promoted competition between phosphate and nanoparticles for deposition sites, which was an important mechanism for the enhanced effect of phosphate. Moreover, the DLVO theory was applicable to describe GO transport behaviors in porous media in the absence or presence of phosphate. Taken together, these findings highlight the important status and role of phosphate on the transport and fate of colloidal graphene oxide in the subsurface environment.

关键词： Graphene oxide Phosphate Goethite Transport Sand

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From mild to explosive: understanding the aggravating effects of ZrB2's reaction in the water vapor oxygen coupled environment

SSRN

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SSRN 2024年

作者： Liu, Zhi-Jie Zhang, Lin-Bo Jiang, Xian-Yu Zhao, Yu-Jie Agathopoulos, Simeon Runowski, Marcin Ahmed, Jahangeer Chen, Hai-Yan Han, Tian-Cheng Lu, Hai-Peng Xie, Jian-Liang Yin, Liang-Jun Liu, Tong National Engineering Research Center of Electromagnetic Radiation Control Materials State Key Laboratory of Electronic Thin Film and Integrated Devices University of Electronic Science and Technology of China 2006 Xiyuan Road Chengdu611731 China Key Laboratory of Multi-spectral Absorbing Materials and Structures of Ministry of Education University of Electronic Science and Technology of China 2006 Xiyuan Road Chengdu611731 China School of Materials Science and Engineering Henan University of Science and Technology Luoyang471023 China Materials Science and Engineering Department University of Ioannina Greece Department of Rare Earths Faculty of Chemistry Adam Mickiewicz University Uniwersytetu Poznańskiego 8 Poznań61-614 Poland Department of Chemistry College of Science King Saud University Riyadh11451 Saudi Arabia Key Laboratory of Green Chemical Process of Ministry of Education Hubei Key Laboratory of Novel Reactor and Green Chemical Technology School of Chemical Engineering and Pharmacy Wuhan Institute of Technology Wuhan430205 China

The study of ultra-high temperature ceramic materials, specifically zirconium diboride (ZrB2), is of great interest due to their potential applications in extreme environments, such as the aerospace and nuclear industries. This research aims to comprehend the reaction dynamics of ZrB2 powders in water vapor oxygen coupled environments, which is a common scenario in high-temperature applications. The study aims to provide a comprehensive insight into the behaviour of ZrB2 in a controlled water vapor oxygen environment, highlighting the two-stage reaction process and its implications for material stability and potential industrial applications. ZrB2 powders were used to investigate their reactivity in a hydrothermal kettle through a series of experiments. This study improves our understanding of ZrB2's reactivity and suggests its potential in applications such as hydrogen production. However, it also highlights the need for improved stability in such reactive environments. © 2024, The Authors. All rights reserved.

关键词： Oxygen

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Enhanced Photocatalytic Co2 Reduction to Ch4 Using Rgo Supported and AU Coated Tio2 Nanotube Arrays (Tntas)

SSRN

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SSRN 2023年

作者： Hossen, Md. Arif Khatun, Fatema Muhammad, Aamina Din Aziz, Azrina Abd Leong, Kah Hon Sim, Lan Ching Ikreedeegh, Riyadh Ramadhan Lihua, Wu Monir, Minhaj Uddin Faculty of Chemical and Process Engineering Technology Universiti Malaysia Pahang Pahang Gambang26300 Malaysia Center for Environmental Science & Engineering Research Chittagong University of Engineering and Technology Chattogram4349 Bangladesh Faculty of Civil Engineering Technology Universiti Malaysia Pahang Pahang Gambang26300 Malaysia Department of Environmental Engineering Faculty of Engineering and Green Technology Universiti Tunku Abdul Rahman Kampar Perak 31900 Malaysia Department of Chemical Engineering Lee Kong Chian Faculty of Engineering and Science Universiti Tunku Abdul Rahman Selangor Kajang43200 Malaysia Department of Analysis and Quality Control Sarir Oil Refinery Arabian Gulf Oil Company Benghazi Libya School of Chemical and Energy Engineering Universiti Teknologi Malaysia Johor Johor Bahru81310 UTM Malaysia Kuantan Sunny Scientific Collaboration Sdn. Imbi Plaza Jalan Imbi Bhd. Suites 7.23 7th Floor Kuala Lumpur55100 Malaysia Department of Petroleum and Mining Engineering Jashore University of Science and Technology Jashore7408 Bangladesh

Photocatalytic CO2 reduction into hydrocarbon fuels is one of the most efficient processes since it serves as a renewable energy source while also lowering atmospheric CO2 levels. The development of appropriate materials and technology to attain greater yield in CO2 photoreduction is one of the key issues facing the 21st century. This study successfully fabricated novel ternary reduced graphene oxide/Au-TiO2 nanotube arrays (RGO/ Au-TNTAs) photocatalyst to promote CO2 photoreduction to *** light responsive RGO/ Au-TNTAs composite were synthesized by facile electrochemical deposition of Au nanoparticles (NPs) and immersion of RGO nanosheets onto TNTAs. Under the source of visible light, the maximum yield of CH4 was attained 35.13 ppm/cm2 for RGO/Au-TNTAs composite photocatalyst after 4 h, which was much higher than that of pure TNTAs, Au-TNTAs and RGO-TNTAs. The CO2 photoreduction of RGO/Au-TNTAs composite has been improved due to combined effects of Au NPs and RGO. Due to its surface plasmonic resonance mechanism, Au NPs plays a crucial role as a photosensitizer for the absorption of visible light. Additionally, the middle RGO layers serve as effective electron transporters, facilitating better separation of electron-hole pairs. The newly constructed composite would be a promising photocatalyst for future photocatalytic applications in other fields. © 2023, The Authors. All rights reserved.

关键词： Carbon dioxide

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