This paper proposes a comprehensive summary and analysis of an important issue during municipal solid waste(MSW)gasification-sulfur and nitrogen pollution.It provides an overview of the fundamentals of MSW and the bas...This paper proposes a comprehensive summary and analysis of an important issue during municipal solid waste(MSW)gasification-sulfur and nitrogen pollution.It provides an overview of the fundamentals of MSW and the basic aspects of nitrogen and sulfur elements.Their characteristics of immigration,transformation and distribution during gasification with control solutions in realized or potential engineering are also concluded.The analysis indicates that the complete scenario of the occurrence form of sulfur and nitrogen elements in MSW is difficult to obtain,owing to the diverse sources and complicated compositions.However,with the assistance of advanced characterization and quantification methods(XPS,XRD,TG-FTIR,et al.),the common sulfur-and nitrogen-containing compounds in both organic and inorganic states can be detected.Adjustment of gasification conditions can regulate the transformation of these elements for emission control.The multiple pollutants including H_(2)S,SO_(x),COS,NH_(3),HCN and NO_(x)cannot be eliminated by one-step treatment but a combination of adsorption and catalytic treatments may realize the control goal.This research aims to benefit meeting emission standards during MSW gasification and to provide a reference for other processes such as incineration,pyrolysis and other feedstocks like biomass and refuse derived fuel(RDF).展开更多
To improve the accuracy and robustness of rolling bearing fault diagnosis under complex conditions, a novel method based on multi-view feature fusion is proposed. Firstly, multi-view features from perspectives of the ...To improve the accuracy and robustness of rolling bearing fault diagnosis under complex conditions, a novel method based on multi-view feature fusion is proposed. Firstly, multi-view features from perspectives of the time domain, frequency domain and time-frequency domain are extracted through the Fourier transform, Hilbert transform and empirical mode decomposition (EMD).Then, the random forest model (RF) is applied to select features which are highly correlated with the bearing operating state. Subsequently, the selected features are fused via the autoencoder (AE) to further reduce the redundancy. Finally, the effectiveness of the fused features is evaluated by the support vector machine (SVM). The experimental results indicate that the proposed method based on the multi-view feature fusion can effectively reflect the difference in the state of the rolling bearing, and improve the accuracy of fault diagnosis.展开更多
Based on the theory of modal acoustic emission(AE),when the convolutional neural network(CNN)is used to identify rotor rub-impact faults,the training data has a small sample size,and the AE sound segment belongs to a ...Based on the theory of modal acoustic emission(AE),when the convolutional neural network(CNN)is used to identify rotor rub-impact faults,the training data has a small sample size,and the AE sound segment belongs to a single channel signal with less pixel-level information and strong local correlation.Due to the convolutional pooling operations of CNN,coarse-grained and edge information are lost,and the top-level information dimension in CNN network is low,which can easily lead to overfitting.To solve the above problems,we first propose the use of sound spectrograms and their differential features to construct multi-channel image input features suitable for CNN and fully exploit the intrinsic characteristics of the sound spectra.Then,the traditional CNN network structure is improved,and the outputs of all convolutional layers are connected as one layer constitutes a fused feature that contains information at each layer,and is input into the network’s fully connected layer for classification and identification.Experiments indicate that the improved CNN recognition algorithm has significantly improved recognition rate compared with CNN and dynamical neural network(DNN)algorithms.展开更多
In order to acquire the degradation state of rolling bearings and achieve predictive maintenance,this paper proposed a novel Remaining Useful Life(RUL)prediction of rolling bearings based on Long Short Term Memory(LST...In order to acquire the degradation state of rolling bearings and achieve predictive maintenance,this paper proposed a novel Remaining Useful Life(RUL)prediction of rolling bearings based on Long Short Term Memory(LSTM)neural network.The method is divided into two parts:feature extraction and RUL prediction.Firstly,a large number of features are extracted from the original vibration signal.After correlation analysis,the features that can better reflect the degradation trend of rolling bearings are selected as input of prediction model.In the part of RUL prediction,LSTM that making full use of the network’s memory in time is used to improve the accuracy of RUL prediction.The proposed method is validated by life cycle experimental data of bearings,and the RUL prediction results of LSTM model are compared with Support Vector Regression(SVR)and Light Gradient Boosting Machine(LightGBM)models respectively.The results show that the proposed method is more suitable for RUL prediction of rolling bearings.展开更多
The influence of a mechanical structure's volume increment on the volume power density (VPD) of triboelectric nanogenerators (TENGs) is often neglected when considering surface charge density and surface power den...The influence of a mechanical structure's volume increment on the volume power density (VPD) of triboelectric nanogenerators (TENGs) is often neglected when considering surface charge density and surface power density. This paper aims to address this gap by introducing a standardized VPD metric for a more comprehensive evaluation of TENG performance. The study specifically focuses on 2 frequency-up mechanisms, namely, the integration of planetary gears (PG-TENG) and the implementation of a double-cantilever structure (DC-TENG), to investigate their impact on VPD. The study reveals that the PG-TENG achieves the highest volume average power density, measuring at 0.92 W/m^(3). This value surpasses the DC-TENG by 1.26 times and the counterpart TENG by a magnitude of 69.9 times. Additionally, the PG-TENG demonstrates superior average power output. These findings introduce a new approach for enhancing TENGs by incorporating frequency-up mechanisms, and highlight the importance of VPD as a key performance metric for evaluating TENGs.展开更多
Solid-state thermoelectric energy conversion devices attract broad research interests because of their great promises in waste heat recycling,space power generation,deep water power generation,and temperature control,...Solid-state thermoelectric energy conversion devices attract broad research interests because of their great promises in waste heat recycling,space power generation,deep water power generation,and temperature control,but the search for essential thermoelectric materials with high performance still remains a great challenge.As an emerging low cost,solution-processed thermoelectric material,inorganic metal halide perovskites CsPb(I_(1–x)Br_(x))_(3) under mechanical deformation is systematically investigated using the first-principle calculations and the Boltzmann transport theory.It is demonstrated that halogen mixing and mechanical deformation are efficient methods to tailor electronic structures and charge transport properties in CsPb(I_(1–x)Br_(x))_(3) synergistically.Halogen mixing leads to band splitting and anisotropic charge transport due to symmetry-breakinginduced intrinsic strains.Such band splitting reconstructs the band edge and can decrease the charge carrier effective mass,leading to excellent charge transport properties.Mechanical deformation can further push the orbital energies apart from each other in a more controllable manner,surpassing the impact from intrinsic strains.Both anisotropic charge transport properties and ZT values are sensitive to the direction and magnitude of strain,showing a wide range of variation from 20%to 400%(with a ZT value of up to 1.85)compared with unstrained cases.The power generation efficiency of the thermoelectric device can reach as high as approximately 12%using mixed halide perovskites under tailored mechanical deformation when the heat-source is at 500 K and the cold side is maintained at 300 K,surpassing the performance of many existing bulk thermoelectric materials.展开更多
基金supported by the National Key R&D Program of China(Grant No.2019YFC1906803).
文摘This paper proposes a comprehensive summary and analysis of an important issue during municipal solid waste(MSW)gasification-sulfur and nitrogen pollution.It provides an overview of the fundamentals of MSW and the basic aspects of nitrogen and sulfur elements.Their characteristics of immigration,transformation and distribution during gasification with control solutions in realized or potential engineering are also concluded.The analysis indicates that the complete scenario of the occurrence form of sulfur and nitrogen elements in MSW is difficult to obtain,owing to the diverse sources and complicated compositions.However,with the assistance of advanced characterization and quantification methods(XPS,XRD,TG-FTIR,et al.),the common sulfur-and nitrogen-containing compounds in both organic and inorganic states can be detected.Adjustment of gasification conditions can regulate the transformation of these elements for emission control.The multiple pollutants including H_(2)S,SO_(x),COS,NH_(3),HCN and NO_(x)cannot be eliminated by one-step treatment but a combination of adsorption and catalytic treatments may realize the control goal.This research aims to benefit meeting emission standards during MSW gasification and to provide a reference for other processes such as incineration,pyrolysis and other feedstocks like biomass and refuse derived fuel(RDF).
基金The National Natural Science Foundation of China(No.51875100)
文摘To improve the accuracy and robustness of rolling bearing fault diagnosis under complex conditions, a novel method based on multi-view feature fusion is proposed. Firstly, multi-view features from perspectives of the time domain, frequency domain and time-frequency domain are extracted through the Fourier transform, Hilbert transform and empirical mode decomposition (EMD).Then, the random forest model (RF) is applied to select features which are highly correlated with the bearing operating state. Subsequently, the selected features are fused via the autoencoder (AE) to further reduce the redundancy. Finally, the effectiveness of the fused features is evaluated by the support vector machine (SVM). The experimental results indicate that the proposed method based on the multi-view feature fusion can effectively reflect the difference in the state of the rolling bearing, and improve the accuracy of fault diagnosis.
基金The authors would like to acknowledge the Six Talent Peaks Project in Jiangsu Province[XCL-CXTD-007]China Postdoctoral Science Foundation[2018M630559]for their financial support in this project。
文摘Based on the theory of modal acoustic emission(AE),when the convolutional neural network(CNN)is used to identify rotor rub-impact faults,the training data has a small sample size,and the AE sound segment belongs to a single channel signal with less pixel-level information and strong local correlation.Due to the convolutional pooling operations of CNN,coarse-grained and edge information are lost,and the top-level information dimension in CNN network is low,which can easily lead to overfitting.To solve the above problems,we first propose the use of sound spectrograms and their differential features to construct multi-channel image input features suitable for CNN and fully exploit the intrinsic characteristics of the sound spectra.Then,the traditional CNN network structure is improved,and the outputs of all convolutional layers are connected as one layer constitutes a fused feature that contains information at each layer,and is input into the network’s fully connected layer for classification and identification.Experiments indicate that the improved CNN recognition algorithm has significantly improved recognition rate compared with CNN and dynamical neural network(DNN)algorithms.
基金This work is supported by the National Nature Science Foundation of China(No.51875100).The authors would like to thank anonymous reviewers and the associate editor,whose constructive comments help improve the presentation of this work.
文摘In order to acquire the degradation state of rolling bearings and achieve predictive maintenance,this paper proposed a novel Remaining Useful Life(RUL)prediction of rolling bearings based on Long Short Term Memory(LSTM)neural network.The method is divided into two parts:feature extraction and RUL prediction.Firstly,a large number of features are extracted from the original vibration signal.After correlation analysis,the features that can better reflect the degradation trend of rolling bearings are selected as input of prediction model.In the part of RUL prediction,LSTM that making full use of the network’s memory in time is used to improve the accuracy of RUL prediction.The proposed method is validated by life cycle experimental data of bearings,and the RUL prediction results of LSTM model are compared with Support Vector Regression(SVR)and Light Gradient Boosting Machine(LightGBM)models respectively.The results show that the proposed method is more suitable for RUL prediction of rolling bearings.
基金funded by the National Natural Science Foundation of China(Nos.62001281 and 62225308)the Shanghai Science and Technology Committee(22dz1204300).
文摘The influence of a mechanical structure's volume increment on the volume power density (VPD) of triboelectric nanogenerators (TENGs) is often neglected when considering surface charge density and surface power density. This paper aims to address this gap by introducing a standardized VPD metric for a more comprehensive evaluation of TENG performance. The study specifically focuses on 2 frequency-up mechanisms, namely, the integration of planetary gears (PG-TENG) and the implementation of a double-cantilever structure (DC-TENG), to investigate their impact on VPD. The study reveals that the PG-TENG achieves the highest volume average power density, measuring at 0.92 W/m^(3). This value surpasses the DC-TENG by 1.26 times and the counterpart TENG by a magnitude of 69.9 times. Additionally, the PG-TENG demonstrates superior average power output. These findings introduce a new approach for enhancing TENGs by incorporating frequency-up mechanisms, and highlight the importance of VPD as a key performance metric for evaluating TENGs.
基金supported by the Thousand Talent Young Scholar Program(BE0200006)Shanghai Aerospace Science and Technology Innovation Fund(USCAST2020-13)+1 种基金the Oceanic Interdisciplinary Program from Shanghai Jiao Tong University(SL2020MS008)the National Natural Science Foundation of China(Grant No.51776041).
文摘Solid-state thermoelectric energy conversion devices attract broad research interests because of their great promises in waste heat recycling,space power generation,deep water power generation,and temperature control,but the search for essential thermoelectric materials with high performance still remains a great challenge.As an emerging low cost,solution-processed thermoelectric material,inorganic metal halide perovskites CsPb(I_(1–x)Br_(x))_(3) under mechanical deformation is systematically investigated using the first-principle calculations and the Boltzmann transport theory.It is demonstrated that halogen mixing and mechanical deformation are efficient methods to tailor electronic structures and charge transport properties in CsPb(I_(1–x)Br_(x))_(3) synergistically.Halogen mixing leads to band splitting and anisotropic charge transport due to symmetry-breakinginduced intrinsic strains.Such band splitting reconstructs the band edge and can decrease the charge carrier effective mass,leading to excellent charge transport properties.Mechanical deformation can further push the orbital energies apart from each other in a more controllable manner,surpassing the impact from intrinsic strains.Both anisotropic charge transport properties and ZT values are sensitive to the direction and magnitude of strain,showing a wide range of variation from 20%to 400%(with a ZT value of up to 1.85)compared with unstrained cases.The power generation efficiency of the thermoelectric device can reach as high as approximately 12%using mixed halide perovskites under tailored mechanical deformation when the heat-source is at 500 K and the cold side is maintained at 300 K,surpassing the performance of many existing bulk thermoelectric materials.
