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Online soft measurement for wastewater treatment system based on hybrid deep learning

Frontiers of Environmental Science & engineering 2024年第2期18卷 77-89页

作者： Wenjie Mai Zhenguo Chen Xiaoyong Li Xiaohui Yi Yingzhong Zhao Xinzhong He Xiang Xu Mingzhi Huang SCNU Environmental Research Institute Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety&MOE Key Laboratory of Theoretical Chemistry of EnvironmentSchool of EnvironmentSouth China Normal UniversityGuangzhou 510006China Fujian Environmental Protection Design Institute Co.Ltd. Fuzhou 350000China Huashi(Fujian)Environment Technology Co.Ltd. Quanzhou362001China SCNU Qingyuan Institute of Science and Technology Innovation Co.Ltd. Qingyuan 511517China Econ Technology Co.Ltd. Yantai 265503China

The existing automated wastewater treatment control systems encounter challenges such as the utilization of specialized testing instruments, equipment repair complications, high operational costs, substantial operational errors, and low detection accuracy. An effective soft measure model offers a viable approach for real-time monitoring and the development of automated control in the wastewater treatment process. Consequently, a novel hybrid deep learning CNN-BNLSTM-Attention (CBNLSMA) model, which incorporates convolutional neural networks (CNN), bidirectional nested long and short-term memory neural networks (BNLSTM), attention mechanisms (AM), and Tree-structure Parzen Estimators (TPE), has been developed for monitoring effluent water quality during the wastewater treatment process. The CBNLSMA model is divided into four stages: the CNN module for feature extraction and data filtering to expedite operations;the BNLSTM module for temporal data’s temporal information extraction;the AM module for model weight reassignment;and the TPE optimization algorithm for the CBNLSMA model’s hyperparameter search optimization. In comparison with other models (TPE-CNN-BNLSTM, TPE-BNLSTM-AM, TPE-CNN-AM, PSO-CBNLSTMA), the CBNLSMA model reduced the RMSE for effluent COD prediction by 25.4%, decreased the MAPE by 32.9%, and enhanced the R2 by 14.9%. For the effluent SS prediction, the CBNLSMA model reduced the RMSE by 26.4%, the MAPE by 21.0%, and improved the R2 by 35.7% compared to other models. The simulation results demonstrate that the proposed CBNLSMA model holds significant potential for real-time effluent quality monitoring, indicating its high potential for automated control in wastewater treatment processes.

关键词： Prediction model Soft measurement CNN-BNLSTM-AM model TPE optimization algorithm

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Prototype observation and influencing factors of environmental vibrationinduced by flood discharge

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Water Science and engineering 2017年第1期10卷 78-85页

作者： Xin Wang Ya-an Hu Shao-ze Luo Lu-chen Zhang Bo Wu Hydraulic engineering Department Nanjing Hydraulic Research InstituteNanjing 210029China State Key laboratory of Hydrology-Water Resources and Hydraulic engineering Nanjing Hydraulic Research InstituteNanjing 210029China Key laboratory of Navigation structure Construction Technology of Ministry of Transport Nanjing Hydraulic Research InstituteNanjing 210029China

Due to a wide range of field vibration problems caused by flood discharge at the Xiangjiaba Hydropower Station, vibration characteristics and influencing factors were investigated based on prototype observation. The results indicate that field vibrations caused by flood discharge have distinctive characteristics of constancy, low frequency, small amplitude, and randomness with impact, which significantly differ from the common high-frequency vibration characteristics. Field vibrations have a main frequency of about 0.5-3.0 Hz and the characteristics of long propagation distance and large-scale impact. The vibration of a stilling basin slab runs mainly in the vertical direction. The vibration response of the guide wall perpendicular to the flow is significantly stronger than it is in other directions and decreases linearly downstream along the guide wall. The vibration response of the underground turbine floor is mainly caused by the load of unit operation. Urban environmental vibration has particular distribution characteristics and change patterns, and is greatly affected by discharge, scheduling modes, and geological conditions. Along with the increase of the height of residential buildings, vibration responses show a significant amplification effect. The horizontal and vertical vibrations of the 7th floor are, respectively, about 6 times and 1.5 times stronger than the corresponding vibrations of the 1st floor. The vibration of a large-scale chemical plant presents the combined action of flood discharge and working machines. Meanwhile, it is very difficult to reduce the low-frequency environmental vibrations. Optimization of the discharge scheduling mode is one of the effective measures of reducing the flow impact loads at present. Choosing reasonable dam sites is crucial.

关键词： Flood discharge Environmental vibration Vibration characteristics Influencing factor Prototype observation

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Optimized ANN for LiFePO4 battery charge estimation using principal components based feature generation

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Green Energy and Intelligent Transportation 2024年第4期3卷 59-71页

作者： Chaitali Mehta Amit V.Sant Paawan Sharma Department of Computer Science and engineering School of TechnologyPandit Deendayal Energy UniversityRaysanGandhinagar 382007India Department of Electrical engineering School of Energy TechnologyPandit Deendayal Energy UniversityRaysanGandhinagar 382007India Department of Information and Communication Technology School of TechnologyPandit Deendayal Energy UniversityRaysanGandhinagar 382007India

Electric vehicles(EVs)have gained prominence in the present energy transition *** adoption of EVs necessitates an accurate State of Charge estimation(SoC)*** predictive SoC estimations with smart charging strategies not only optimizes charging efficiency and grid reliability but also extends battery lifespan while continuously enhancing the accuracy of SoC predictions,marking a crucial milestone in sustainable electric vehicle *** this research study,machine learning methods,particularly Artificial Neural Networks(ANN),are employed for SoC estimation of LiFePO4 batteries,resulting in efficient and accurate estimation *** investigation first focuses on developing a custom-designed battery pack with 12V,4 Ah capacity with a facility for real-time data collection through a dedicated hardware *** voltage,current and open-circuit voltage of the battery are monitored with computerized battery *** battery temperature is sensed with a DHT22 temperature sensor interfaced with Raspberry *** components are derived for the collected battery data set and analyzed for feature *** principal components were generated as input parameters for the developed *** Stopping for the ANN was also implemented to achieve faster convergence of the *** considering eleven combinations for ten different optimizers loss function is *** analysis of hyperparameter tuning and optimizer selection revealed that the Adafactor optimizer with specific settings produced the best results with an RMSE value of 0.4083 and an R2 Score of *** proposed algorithm was also implemented for two different types of datasets,a UDDS drive cycle and a standard cell-level *** results obtained were in line with the results obtained with the ANN model developed based on the data collected from the developed experimental setup.

关键词： Battery Feature engineering Principal component analysis Artificial neural networks Optimizers

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Polydopamine-coated metal-organic framework-based composite phase change materials for photothermal conversion and storage

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中国化学快报:英文版 2023年第8期34卷 343-347页

作者： Ang Li Mengke Huang Die Hu Zhaodi Tang Jianhang Xu Yang Li Xiaowei Zhang Xiao Chen Ge Wang School of Materials Science and engineering Suzhou University of Science and TechnologySuzhou 215009China Beijing Advanced Innovation Center for Materials Genome engineering Beijing Key Laboratory of Function Materials for Molecule&Structure ConstructionSchool of Materials Science and EngineeringUniversity of Science and Technology BeijingBeijing 100083China Institute of Advanced Materials Beijing Normal UniversityBeijing 100875China Key laboratory of Low-grade Energy Utilization Technologies and Systems(Chongqing University) Ministry of Education of ChinaChongqing UniversityChongqing 400044China Shunde Graduate School University of Science and Technology BeijingShunde 528399China

The liquid leakage and weak solar absorption capacity of organic phase change materials(PCMs)seriously hinder the efficient utilization of solar energy and thermal energy *** address these issues,we prepared nanoporous metal organic framework(Ni-MOF)for the vacuum infiltration of paraffin wax(PW),followed by the coating of solar-absorbing functional polydopamine(PDA)on the surface of PW@MOF for photothermal conversion and *** an efficient photon harvester,PDA coating endows PW@MOF/PDA composite PCMs with excellent photothermal conversion and storage properties due to the robust broadband solar absorption capability in the UV–vis ***,our prepared PW@MOF/PDA composite PCMs exhibit a high photothermal conversion and storage efficiency of 91.2%,while that of PW@MOF composite PCMs is only *** addition,PW@MOF/PDA composite PCMs also exhibit excellent thermal stability,shape stability,energy storage stability,and photothermal conversion *** importantly,this coating strategy is universal by integrating different MOFs and solar absorbers,showing the potential to accelerate the major breakthroughs of high-efficiency MOF-based photothermal composite PCMs in solar energy utilization.

关键词： Phase change materials Metal organic frameworks Photothermal conversion Thermal energy storage Photon harvester

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Multi-node CdS hetero-nanowires grown with defect-rich oxygen-doped MoS2 ultrathin nanosheets for efficient visible-light photocatalytic H2 evolution

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Nano Research 2017年第4期10卷 1377-1392页

作者： Haifeng Lin Yanyan Li Haoyi Li Xun Wang Key Lab of Organic Optoelectronics and Molecular engineering Department of Chemistry Tsinghua University Beijing 100084 China Key laboratory of Optoelectronic Materials Chemistry and Physics Fujian Institute of Research on the Structure of Matter Chinese Academy of Sciences Fuzhou 350002 China

Developing low-cost and high-efficiency photocatalysts for hydrogen production from solar water splitting is intriguing but challenging. In this study, unique one-dimensional （1D） multi-node MoS2/CdS hetero-nanowires （NWs） for efficient visible-light photocatalytic H2 evolution are synthesized via a facile hydrothermal method. Flower-like sheaths are assembled from numerous_ defect-rich O-incorporated {0001} MoS2 facet surrounded CdS NW stems are ultrathin nanosheets （NSs）, and {1120}- grown preferentially along the c-axis. Interestingly, the defects in the MoS2 NSs provide additional active S atoms on the exposed edge sites, and the incorporation of O reduces the energy barrier for H2 evolution and increases the electric conductivity of the MoS2 NSs. Moreover, the recombination of photoinduced charge carriers is significantly inhibited by the heterojunction formed between the MoS2 NSs and CdS NWs. Therefore, in the absence of noble metals as co-catalysts, the 1D MoS2 NS/CdS NW hybrids exhibit an excellent H2-generation rate of 10.85 mmol·g^-1·h^-1 and a quantum yield of 22.0% at ,λ = 475 nm, which is far better than those of Pt/CdS NWs, pure MoS2 NSs, and CdS NWs as well as their physical mixtures. Our results contribute to the rational construction of highly reactive nanostructures for various catalytic applications.

关键词： CdS hetero-nanowires defect-rich oxygen-doped MoS2 ultrathin nanosheets active sites charge separation visible-light photocatalysis

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An intriguing electrochemical impedance aptasensor based on a porous organic framework supported silver nanoparticles for ultrasensitively detecting theophylline

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Chinese Chemical Letters 2021年第9期32卷 2865-2868页

作者： Zhang-Ye Han Hong-Kai Li Qian-Qian Zhu Rongrong Yuan Hongming He Tianjin Key laboratory of structure and Performance for functional Molecules College of ChemistryTianjin Normal UniversityTianjin 300387China Department of Materials Science and engineering Jilin Jianzhu UniversityChangchun 130118China

Porous organic frameworks(POFs)are excellently stable porous materials,which can be employed as host platforms to support metal nanoparticles as functional composites for various ***,a novel POF is successfully prepared via Friedel-Crafts *** nanoparticles(AgNPs)are embedded in the prepared POF to generate an Ag@POF composite,which not only possesses high surface area,outstanding physicochemical stability and outstretchedπ-conjugation skeleton,but also exhibits preferable electrochemical stability and *** composite is able to immobilize a mass of aptamer strands to fabricate an intriguing electrochemical *** impedance spectroscopy(EIS)is a commonly used technology to analyze the electrochemical signal *** Ag@POF-based biosensor shows the excellent electrochemical detection behavior through analyzing *** instance theophylline as a research mode,the Ag@POF based electrochemical aptasensor reveals ultra-sensitiveness,high selectivity,remarkable stability,good repeatability and simple operability even in various real ***,this aptasensor has the sensitive detection performance with the limit of detection of 0.191 pg/m L(1.06 pmol/L)in a wide concentration range of 5.0×10^(-4)–5.0 ng/m L(2.78×10^(-3)–27.8 nmol/L).

关键词： Ag@POF composite Aptamer Electrochemical aptasensor Theophylline Sensitive detection

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Can urban forests provide acoustic refuges for birds?Investigating the influence of vegetation structure and anthropogenic noise on bird sound diversity

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Journal of Forestry Research 2024年第2期35卷 163-175页

作者： Zezhou Hao Chengyun Zhang Le Li Bing Sun Shuixing Luo Juyang Liao Qingfei Wang Ruichen Wu Xinhui Xu Christopher A.Lepczyk Nancai Pei Research Institute of Tropical Forestry Chinese Academy of ForestryGuangzhou 510520People’s Republic of China School of Electronics and Communication engineering Guangzhou UniversityGuangzhou 510006People’s Republic of China Hunan Botanical Barden Changsha 410116People’s Republic of China Guangdong Eco-engineering Polytechnic Guangzhou 510520People’s Republic of China College of Forestry Wildlife and EnvironmentAuburn UniversityAuburnAL 36849USA

As a crucial component of terrestrial ecosystems,urban forests play a pivotal role in protecting urban biodiversity by providing suitable habitats for acoustic *** studies note that vegetation structure is a key factor influencing bird sounds in urban forests;hence,adjusting the frequency composition may be a strategy for birds to avoid anthropogenic noise to mask their ***,it is unknown whether the response mechanisms of bird vocalizations to vegetation structure remain consistent despite being impacted by anthropogenic *** was hypothesized that anthropogenic noise in urban forests occupies the low-frequency space of bird songs,leading to a possible reshaping of the acoustic niches of forests,and the vegetation structure of urban forests is the critical factor that shapes the acoustic space for bird *** acoustic monitoring in various urban forests was used to monitor natural and anthropogenic noises,and sounds were classified into three acoustic scenes(bird sounds,human sounds,and bird-human sounds)to determine interconnections between bird sounds,anthropogenic noise,and vegetation *** noise altered the acoustic niche of urban forests by intruding into the low-frequency space used by birds,and vegetation structures related to volume(trunk volume and branch volume)and density(number of branches and leaf area index)significantly impact the diversity of bird *** findings indicate that the response to low and high frequency signals to vegetation structure is *** clarifying this relationship,our results contribute to understanding of how vegetation structure influences bird sounds in urban forests impacted by anthropogenic noise.

关键词： Anthropogenic noise Bird sounds Urban forests Vegetation structure

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Combinatorial mutation on the β-glycosidase specific to 7-β-xylosyltaxanes and increasing the mutated enzyme production by engineering the recombinant yeast

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Acta Pharmaceutica Sinica B 2019年第3期9卷 626-638页

作者： Jing-Jing Chen Xiao Liang Fen Wang Yan-Hua Wen Tian-Jiao Chen Wan-Cang Liu Ting Gong Jin-Ling Yang Ping Zhu State Key laboratory of Bioactive Substance and function of Natural Medicines & NHC Key laboratory of Biosynthesis of Natural Products Institute of Materia Medica Chinese Academy of Medical Sciences & Peking Union Medical College

Taxol is a 'blockbuster' antitumor drug produced by Taxus species with extremely low amount, while its analogue 7-β-xylosyl-10-deacetyltaxol is generally much higher in the plants. Both the fungal enzymes LXYL-P1à1 and LXYL-P1à2 can convert 7-β-xylosyl-10-deacetyltaxol into10-deacetyltaxol for Taxol semi-synthesis. Of them, LXYL-P1à2 is twice more active than LXYLP1à1, but there are only 11 significantly different amino acids in terms of the polarity and acidic-basic properties between them. In this study, single and multiple site-directed mutations at the 11 sites from LXYL-P1à1 to LXYL-P1à2 were performed to define the amino acids with upward bias in activities and to acquire variants with improved catalytic properties. Among all the 17 mutants, E12(A72 T/V91 S) was the most active and even displayed 2.8-and 3-fold higher than LXYL-P1à2 on β-xylosidase andβ-glucosidase activities. The possible mechanism for such improvement was proposed by homology modeling and molecular docking between E12 and 7-β-xylosyl-10-deacetyltaxol. The recombinant yeast GS115-P1 E12-7 was constructed by introducing variant E12, the molecular chaperone gene pdi and the bacterial hemoglobin gene vhb. This engineered yeast rendered 4 times higher biomass enzyme activity than GS115-3.5 K-P1à2 that had been used for demo-scale fermentation. Thus, GS115-P1 E12-7 becomes a promising candidate to replace GS115-3.5 K-P1à2 for industrial purpose.

关键词： β-Glycosidases Combinatorial mutation Improved catalytic property Molecular docking Engineered yeast Taxol

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Boost VS_(2) electrochemical reactive kinetics by regulating crystallographic planes and coupling carbon matrix for high performance sodium-ion storage

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Rare Metals 2024年第1期43卷 98-112页

作者： Min Zeng Ming-Shan Wang Lin Chen En-Zhi Li Zhen-Liang Yang Dan Zhou Hai-Jiao Xie Jun-Chen Chen Zhi-Yuan Ma Bing-Shu Guo Bo Yu Xing Li School of New Energy and Materials Southwest Petroleum UniversityChengdu 610500China Energy Storage Research Institute Southwest Petroleum UniversityChengdu 610500China Institute of Materials China Academy of Engineering PhysicsMianyang 621908China Beijing Advanced Innovation Center for Materials Genome engineering University of Science and TechnologyBeijingBeijing 100083China Hangzhou Yanqu Information Technology Co. Ltd.Hangzhou 310003China

Vanadium disulfide(VS_(2)) as a typical two-dimensional transition metal chalcogenide has excellent competitiveness for sodium-ion storage due to its wide layer spacing(0.575 nm),high theoretical capacity of 932mAh·g^(-1) originating from multi-electron electrochemical ***,continuous sodiation process accompanied by crystal structural evolution and collapse cause rapid capacity ***,novel few-layer VS_(2)nanosheets with open(001) crystal planes are in-situ constructed on reduced graphene oxide to solve these issues mentioned *** indicates that few-layer VS_(2)provides more Na^(+) storage activity due to the low Na^(+)surface migration energy barrier on exposed crystal(001)*** flexible and high electronic conductivity of carbon matrix also effectively builds multi-level buffer structure and electron transport kinetics to boost the Na^(+)insertion/conversion reactive activity on VS_(2) as well as Na^(+) pseudocapacitance storage kinetics on edges and defects of nano *** coupling effects result in high rate capability and long cycling stability as a battery/capacitor *** delivers conspicuous high energy density of 81 and 40 Wh·kg^(-1) at power density of 118 and 10,286W·kg^(-1),as well as 80% energy retention rate after 5000cycles,confirming its great application potential in sodiumbased storage devices.

关键词： Vanadium disulfide Reduced graphene oxide Anode Sodium-ion battery Sodium-ion capacitor

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Analytical and NumericalMethods to Study the MFPT and SR of a Stochastic Tumor-Immune Model

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Computer Modeling in engineering & Sciences 2024年第3期138卷 2177-2199页

作者： Ying Zhang Wei Li Guidong Yang Snezana Kirin School of Mathematics and Statistics Xidian UniversityXi’an710071China Innovation Center of Faculty of Mechanical engineering Belgrade11100Serbia

The Mean First-Passage Time (MFPT) and Stochastic Resonance (SR) of a stochastic tumor-immune model withnoise perturbation are discussed in this paper. Firstly, considering environmental perturbation, Gaussian whitenoise and Gaussian colored noise are introduced into a tumor growth model under immune surveillance. Asfollows, the long-time evolution of the tumor characterized by the Stationary Probability Density (SPD) and MFPTis obtained in theory on the basis of the Approximated Fokker-Planck Equation (AFPE). Herein the recurrenceof the tumor from the extinction state to the tumor-present state is more concerned in this paper. A moreefficient algorithmof Back-Propagation Neural Network (BPNN) is utilized in order to testify the correction of thetheoretical *** the existence of aweak signal, the functional relationship between Signal-to-NoiseRatio (SNR), noise intensities and correlation time is also studied. Numerical results show that both multiplicativeGaussian colored noise and additive Gaussian white noise can promote the extinction of the tumors, and themultiplicative Gaussian colored noise can lead to the resonance-like peak on MFPT curves, while the increasingintensity of the additiveGaussian white noise results in theminimum of MFPT. In addition, the correlation timesare negatively correlated with MFPT. As for the SNR, we find the intensities of both the Gaussian white noise andthe Gaussian colored noise, as well as their correlation intensity can induce SR. Especially, SNR is monotonouslyincreased in the case ofGaussian white noisewith the change of the correlation *** last, the optimal parametersin BPNN structure are analyzed for MFPT from three aspects: the penalty factors, the number of neural networklayers and the number of nodes in each layer.

关键词： Stochastic tumor-immune model mean first-passage time stochastic resonance signal-to-noise ratio back-propagation neural network

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