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G-CNTs/PVDF mixed matrix membranes with improved antifouling properties and filtration performance

Frontiers of Environmental science & engineering 2019年第6期13卷 23-33页

作者： Xiaoyan Guo Chunyu Li Chenghao Li Tingting Wei Lin Tong Huaiqi Shao Qixing Zhou Lan Wang Yuan Liao Ministry of Education Key Laboratory of Pollution Processes and Environmental Criteria Tianjin Key Laboratory of Environmental Technology for Complex Trans-Media PollutionTianjin Key Laboratory of Environmental Remediation and Pollution ControlCollege of Environmental Science and EngineeringNankai UniversityTianjin 300350China Sino-Canada Joint R&D Centre for Water and Environmental Safety College of Environmental Science and EngineeringNankai UniversityTianjin 300350China College of Chemical Engineering and Materials Science Tianjin University of Science&TechnologyTianjin 300457China

Although carbon nanomaterials have been widely used as effective nanofillers for fabrication of mixed matrix membranes(MMMs)with outstanding performances,the reproducibility of the fabricated MMMs is still hindered by the non-homogenous dispersion of these carbon nanofillers in membrane ***,we report an effective way to improve the compatibility of carbon-based nanomaterials with membrane *** chemically conjugating the oxidized CNTs(o-CNTs)and GO using hexanediamine as cross-linker,a novel carbon nanohybrid material(G-CNTs)was synthesized,which inherited both the advanced properties of multi-walled carbon nanotubes(CNTs)and graphene oxide(G0).The G-CNTs incorporated polyvinylidene fluoride(PVDF)MMMs(GCNTs/PVDF)were fabricated via a non-solvent induced phase separation(NIPS)*** filtration and antifouling performances of G-CNTs/PVDF were evaluated using distillate water and a1g/L bovine serum albumin(BSA)aqueous solution under 0.10 *** to the MMMs prepared with o-CNTs,GO,the physical mixture of o-CNTs and GO and pure PVDF membrane,the G-CNTs/PVDF membrane exhibited the highest water flux up to 220 L/m%and a flux recovery ratio as high as 90%,as well as the best BSA rejection *** excellent performances should be attributed to the increased membrane pore size,porosity and hydrophilicity of the resulted *** successful synthesis of the novel nanohybrid G-CNTs provides a new type of nanofillers for MMMs fabrication.

关键词： Carbon nanotubes Graphene oxide Mixed matrix membrane Nanohybrid Antifouling membrane Membrane hydrophilicity

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Application of neural-network hybrid models in estimating the infection functions of nonlinear epidemic models

arXiv

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arXiv 2022年

作者： Li, Chentong Zhou, Changsheng Liu, Junmin Rong, Yao Guangdong Key Laboratory of Modern Control Technology Institute of Intelligent Manufacturing Guangdong Academy of Science Guangdong Guangzhou510070 China School of Mathematics and Information science Guangzhou University Guangdong Guangzhou510006 China School of Mathematics and Statistics Xi’an Jiaotong University Shaanxi Xi’an710049 China College of Engineering Physics Shenzhen Technology University Guangdong Shenzhen518118 China

Hybrid neural-network models combine the advantages of a neural network’s fitting functionality with differential equation models to reflect actual physical processes and are widely used in analyzing time-series data. Most related studies have focused on linear hybrid models, but only a few have examined nonlinear problems. In this work, we use a hybrid nonlinear epidemic neural network as the entry point to study its power in predicting the correct infection function of an epidemic model. To achieve this goal, we combine the bifurcation theory of the nonlinear differential model with the mean-squared error loss and design a novel loss function to ensure model trainability. Furthermore, we find the unique existence conditions supporting ordinary differential equations to estimate the correct infection function. Using the Runge–Kutta method, we perform numerical experiments on our proposed model and verify its soundness. We also apply it to real COVID-19 data to accurately discover the change law of its infectivity. © 2022, CC BY.

关键词： Bifurcation (mathematics)

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Estimating source strengths of HCl and SO_2 emissions in the flue gas from waste incineration

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Journal of Environmental sciences 2019年第1期31卷 370-377页

作者： Hua Zhang Siyuan Yu Liming Shao Pinjing He State Key Laboratory of Pollution Control and Resource Reuse Tongji University Institute of Waste Treatment & Reclamation College of Environmental Science and Engineering Tongji University Shanghai Institute of Pollution Control and Ecological Security Centre for the Technology Research and Training on Household Waste in Small Towns & Rural Area Ministry of Housing and Urban-Rural Development

HCl and SO_2 emission is one of the major concerns related to municipal solid waste incinerator(MSWI). In this study, a material flow analysis model was developed to estimate the HCl and SO_2 concentrations in the MSWI flue gases(FGs), and their concentrations in the full-scale MSWI were monitored. The calculated concentrations of HCl and SO_2 in the FG were 770–1300 mg/Nm^3 and 150–640 mg/Nm^3, respectively, in close agreement with the monitored values. More than 99% of Cl and 92% of S from the FG were captured into solid residues by the air pollution control(APC) systems. Moreover, since only 48.4%–67.5% of Cl and 21.3%–53.4% of S were transferred to the FG from the municipal solid waste(MSW), it was more reliable to estimate the source strengths and release amounts of HCl and SO_2 in the FG based on the amounts of Cl and S in the APC residues(AR) and exhaust gas rather than in the MSW. This simple method is easily applicable and the estimated results could provide scientific basis for the appropriate design and operation of the APC systems as well as corrosion control of heat recovery systems.

关键词： Municipal solid waste incineration Acid gas Hydrogen chloride Sulfur dioxide Source strength

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Novel Production Prediction Model of Gasoline Production Processes for Energy Saving and Economic Increasing Based on Am-Gru Integrating the Umap Algorithm

SSRN

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SSRN 2022年

作者： Liu, Jintao Chen, Liangchao Xu, Wei Han, Yongming Geng, Zhiqiang Feng, Mingfei Fan, Jinzhen College of Information Science and Technology Beijing University of Chemical Technology Beijing100029 China Engineering Research Center of Intelligent PSE Ministry of Education of China Beijing100029 China State Key Laboratory of Chemical Safety Control SINOPEC Research Institute of Safety Engineering Co. Ltd China University of International Business and Economics Beijing China Facebook Reality Labs Facebook Inc. CA94010 United States

Gasoline is of great significance to ensure people's living standards and national energy security. However, the point information collected by actual gasoline industrial devices has characteristics of high dimension, high noise and time series. Therefore, a novel production prediction model using an attention mechanism (AM) based gated recurrent unit (GRU) (AM-GRU) integrating the uniform manifold approximation and projection (UMAP) is proposed. The data collected in industrial plants are processed by the box plot to remove the data outside the quartile. Then, the UMAP can remove the strong correlation between data to improve the running speed and the performance of the AM-GRU. Compared with existing time series data prediction methods, the superiority of the AM-GRU is verified based on UCI datasets. Finally, the production prediction model of actual gasoline production processes for energy saving and economic increasing based on the proposed method is built. The experiment results show that compared with other time series data prediction models, the proposed model has better stability and higher accuracy with reaching 0.9969. Moreover, according to the optimal scheme of the raw material, the inefficiency production points can increase about 0.69 tons of the gasoline yield and between $645.1 and $925.6 of economic benefits. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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GA-OMTL: Genetic algorithm optimization for multi-task neural architecture search in NIR spectroscopy

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Expert Systems with Applications 2025年 290卷

作者： Yu Yang Siqi Wang Gan Zhang Qifu Wang Yao Qin Dandan Zhai Zhiqing Yang Peng Li Key Laboratory of Grain Information Processing and Control Ministry of Education Henan University of Technology Zhengzhou 450001 China Henan Key Laboratory of Grain Photoelectric Detection and Control Henan University of Technology Zhengzhou 450001 China College of Information Science and Engineering Henan University of Technology Zhengzhou 450001 China Institute for Complexity Science Henan University of Technology Zhengzhou 450001 China Henan Academy of Science Applied Physics Institute Co. Ltd Zhengzhou 450001 China

Near-infrared (NIR) spectroscopy analysis based on deep learning has emerged as a powerful tool for the quality assessment of food and agricultural products. However, most existing multi-task deep learning (MTL) network architectures rely on manual design, struggle to efficiently adapt to diverse datasets, and often neglect the interaction of task-specific (private) features across tasks. To address these challenges, this study proposes a genetic algorithm (GA)-optimized MTL model, termed GA-OMTL, which integrates the strengths of neural architecture search and GA for multi-task prediction of spectral data. The model enhances both feature extraction and task-specific feature interaction by incorporating searchable components such as residual modules (Resblock), batch normalization (BN) layers, Squeeze-and-Excitation (SE) modules, and feature interaction modules. The effectiveness of GA-OMTL was validated using two datasets: American ginseng and wheat flour. In the prediction of protopanaxatriol-type ginsenosides (PPT) and protopanaxadiol-type ginsenosides (PPD) in American ginseng, the R 2 , RMSE , and RPD values achieved by GA-OMTL were 0.93, 0.70 mg/g, and 3.83 (PPT), and 0.98, 2.03 mg/g, and 7.16 (PPD), respectively. For the prediction of protein and moisture content in wheat flour, the R 2 , RMSE , and RPD values were 0.99, 0.29 mg/g, and 8.22 (protein), and 0.97, 0.22 mg/g, and 5.67 (moisture), respectively. The experimental results demonstrate that GA-OMTL outperforms three comparison methods in prediction accuracy, highlighting its potential for complex spectral tasks and confirming the practicality and robustness of the proposed model.

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A Continuous-time Algorithm with the Sigmoid Function for Distributed Constrained Optimization of Multi-agent Systems

A Continuous-time Algorithm with the Sigmoid Function for Di...

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International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Mingfei Chen Dong Wang Zhu Wang Wei Wang Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education and School of Control Science and Engineering Dalian University of Technology Dalian PR China

ISBN: (数字)9781728162461

ISBN: (纸本)9781665419352

We study the distributed optimization problem with constraint sets in continuous-time multi-agent systems (MAS). The local cost function of each agent is strictly convex in the feasible region and agents can exchange state information with neighbors over an undirected graph. All agents cooperatively reach the state which minimizes the sum of agents' cost functions and achieves consensus satisfying constraint sets. Based on penalty function methods combined with KKT condition and projection technique, a distributed continuous-time control law is designed. By Lyapunov stability theory and LaSalles invariance principle, the states of all agents converging to the global optimum from any initial state is analyzed. Finally, the performance of the proposed algorithm is verified by a numerical example.

关键词： Cost function Distributed algorithms Cybernetics Multi-agent systems Lyapunov methods

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Reconstructing missing NOx emissions in heavy-duty diesel vehicle OBD data: A machine learning approach

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Journal of Hazardous Materials 2025年 494卷 138619页

作者： Cao, Zeping Wang, Yanan Zhao, Xiaoyang Yin, Jiawei Jia, Zhenyu Zhan, Yanjie Liu, Yan Zhang, Qijun Mao, Hongjun Tianjin Key Laboratory of Urban Transport Emission Research & State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control College of Environmental Science and Engineering Nankai University Tianjin300071 China Tianjin Youmei Environment Technology Ltd Tianjin300380 China

On-Board Diagnostic (OBD) systems enable real-time monitoring of heavy-duty diesel vehicle operations and NOx emissions. However, existing OBD systems have inherent limitations, including systematic missing values. To address this issue, this study develops a data-driven approach. The method utilizes OBD-recorded upstream and downstream NOx emissions data to build machine learning models for reconstructing real-world OBD data. The modeling results indicate that machine learning models perform well in predicting upstream emissions, achieving an R² above 0.9 on the test set. However, its performance in predicting downstream emissions was highly variable, with R² ranging from 0.05 to 0.98, and showed a positive correlation with fuel-based emission factors. A case study was conducted on a selected vehicle. The total NOx emission associated with missing data for this specific vehicle was estimated at 15,741.3 g, whereas the recorded emission from available data was 6157.3 g. Missing data were then imputed for an additional 31 vehicles, revealing that normal emitters showed significantly higher emission associated with missing data. The proposed approach is highly compatible with existing big data platforms and can be easily extended to other vehicles. This will improve the platform's representation of real-world emission, enabling policymakers to implement more targeted pollution mitigation strategies. © 2025 Elsevier B.V.

关键词： Emission control

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Thermostable 1t-Dominant Mos2 Nanosheets Achieved by Spontaneous Intercalation of Cu Single Atoms at Room Temperature and Their Enhanced Overall-Water-Splitting Performance

SSRN

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SSRN 2022年

作者： Li, Mengyao Selvarajan, Premkumar Wang, Shuangyue Wan, Tao Xi, Shibo Wang, Xiaopeng Xue, Junmin Sathish, Clastinrusselraj Indirathankam Liu, Yanpeng Vinu, Ajayan Chu, Dewei Yi, Jiabao School of Materials Science and Engineering University of New South Wales SydneyNSW2052 Australia Global Innovative Centre for Advanced Nanomaterials College of Engineering Science and Environment The University of Newcastle CallaghanNSW2308 Australia Institute of Chemical and Engineering Sciences A*STAR Singapore627833 Singapore Department of Materials Science and Engineering National University of Singapore 7 Engineering Drive 1 Singapore117574 Singapore Key Laboratory for Intelligent Nano Materials and Devices of the Ministry of Education State Key Laboratory of Mechanics and Control of Mechanical Structures Nanjing University of Aeronautics and Astronautics Nanjing210016 China

We report a simple strategy to fabricate Cu single atoms (SAs) anchored on MoS 2 only by stirring Cu metals with MoS 2 nanosheets solution at room temperature. An ultra-high concentration (98 at%) of Cu SAs have been achieved and the intercalated Cu atoms strongly enhance the stability of the thermodynamically unstable 1T-phase dominant MoS 2 . Notably, the 1T-metallic phase after Cu intercalation is stable even at 800 o C, breaking the record to the best of our knowledge. Besides, the as-prepared MoS 2 /SAs exhibit outstanding catalytic performance owing to the improved electrical conductivity and the highly-active unsaturated Cu SAs. This strategy is confirmed as a universal method for producing SAs of other metals and other 2D nanosheets could also be as the host for SAs intercalation other than MoS 2 . The facile and low-cost approach may provide a way for fabricating SAs catalysts at industrial level. © 2022, The Authors. All rights reserved.

关键词： Atoms

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Asymptotic Stability Analysis for Switched Systems under Spatio-temporal Specifications

Asymptotic Stability Analysis for Switched Systems under Spa...

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第40届中国控制会议

作者： Qian Liu Lijun Long Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing Institute of Artificial Intelligence Faculty of Information TechnologyBeijing University of Technology College of Information Science and Engineering Northeastern University China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University

This paper provides some sufficient conditions to achieve asymptotic stability of switched systems under spatiotemporal specifications via a state-dependent switching law. A common finite-time barrier function, multiple barrier functions and multiple Lyapunov functions are merged as a tool for analyzing the asymptotic stability of a switched nonlinear system under spatio-temporal specifications. Finally, a numerical example is presented to demonstrate the effectiveness of our proposed method.

关键词： Switched systems Common finite-time barrier function Multiple barrier functions Multiple Lyapunov functions state-dependent switching law

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Adaptive fractional-order edge-stopping function-based active contour model for image segmentation

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Multimedia Tools and Applications 2025年 1-24页

作者： Yang, Lijun Zhang, Hongying Li, Xiaoxia School of Information and Control Engineering Southwest University of Science and Technology Mianyang621010 China Robot Technology Used for Special Environment Key Laboratory of Sichuan Province Mianyang621010 China Sichuan Industrial Autonomous and Controllable Artificial Intelligence Engineering Technology Research Center Mianyang621010 China

The active contour model has been a widely used method in image segmentation that remains challenging due to the noise and intensity inhomogeneity. In this paper, an adaptive fractional-order edge-stopping function-based active contour model is proposed. To better preserve the weak texture information in the image, a new adaptive fractional order matrix, defined using the gradient information of the image, is first constructed. Then, a new edge-stopping function based on adaptive fractional differentiation is utilized to improve the robustness of the proposed model to noise. Finally, the energy fitting term based on regional variance can achieve an arbitrary selection of the initial position of the active contour curve. Experimental results on grayscale and natural images in PASCAL VOC2012 demonstrate that this method can effectively segment images with noise and intensity inhomogeneity. Compared to the optimal competitive active contour model WHRSPF, the proposed method shows an average increase of 7.9% and 4.5% in IOU and DSC, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Image segmentation

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