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Evaluating urban agglomeration resilience to disaster in the Yangtze Delta city group in China

SUSTAINABLE CITIES AND SOCIETY 2022年 76卷 103464-103464页

作者： Lu, Hao Lu, Xin Jiao, Liudan Zhang, Yu Chongqing Jiaotong Univ Sch Econ & Management Chongqing Peoples R China

With the rapid acceleration of global urbanization, progressively more natural disasters and public safety problems are encountered in cities. Previous studies have shown that highly resilient cities can promptly and effectively respond to disasters. Therefore, in this study, a mixed-methods approach to urban agglomeration resilience estimation is proposed. First, the particle swarm optimization algorithm is used to optimize the back propagation neural network in order to evaluate the resilience of subsystems, including economy, society, environment, and science and technology resilience subsystems. Then, the entropy weight method is integrated to obtain the urban agglomeration resilience. Finally, the kernel density estimation and Moran's I are utilized for comprehensive analysis of the dynamic evolution and spatial correlation of the urban agglomeration resilience. The Yangtze River Delta cities are adopted as a case study, and the results indicated that the resilience in the most developed urban agglomeration in China showed the pyramidal spatial distribution. From the perspective of evolution, the resilience level is constantly improving, and the differences among cities are gradually decreasing from 2015 to 2019. The results indicate that the model is valuable for evaluating urban resilience, and thus it can help policymakers formulate proposals to effectively improve the resilience of urban agglomerations.

关键词： Urban resilience Urban agglomeration Resilience evaluation particle swarm optimization algorithm The Back Propagation Neural Network China

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Prediction and evaluation of fuel properties of hydrochar from waste solid biomass: Machine learning algorithm based on proposed PSO-NN model

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FUEL 2022年 318卷 123644-123644页

作者： Mu, Lin Wang, Zhen Wu, Di Zhao, Liang Yin, Hongchao Dalian Univ Technol Sch Energy & Power Engn Dalian 116024 Peoples R China Minist Educ Key Lab Ocean Energy Utilizat & Energy Conservat Dalian 116024 Peoples R China

Hydrothermal carbonization is an effective and environmentally friendly biomass pretreatment technology, which converts high moisture biomass into homogeneous, carbon-rich, and high calorific value solid hydrochar. This study aimed to predict the fuel properties of the hydrochar based on hydrothermal conditions and biomass characteristics by machine learning (ML) models. Artificial neural network (ANN) combined with particle swarm optimization (PSO) algorithm was proposed and developed based on 296 data points collected from abundant previous studies, and the prediction capability is analyzed with ordinary ANN model. The results showed that particle swarm optimization-neural network (PSO-NN) model with optimal hyper-parameters can reduce iteration time, and improve the stability and accuracy of ANN model. Fuels properties of hydrochar were predicted by PSO-NN model with R-2 greater than 0.85 and the convergence speed is increased by 26.8%. Feature importance and correlation were explored by the integration of PSO-NN model and model explainer based on SHAP methodology. The result showed that the carbon content in raw biomass was the significant feature impacting mass yield, and the mass yield of hydrochar mainly depended on elemental composition of feedstock. The HTC temperature of water is the most important factor affecting HHV of the hydrochar, so raising hydro thermal temperature is the best way to improve the HHV. N content was considered as the most important parameter for the N/C molar ratio among all the evaluated features. The O content of the raw biomass had obvious influence on the ASH content of hydrochar, and the influence of operating conditions for ash content changes only accounted for 14.3%, which indicated that the removing efficiency of ash from biomass was low only by changing the operating conditions. Both DHD and DCD of hydrochars were most affected by temperature, and the ash content played a significant role in the prediction of the DHD. Furth

关键词： Hydrothermal carbonization particle swarm optimization algorithm Artificial neural network Single-task prediction Feature analysis

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A high-efficiency and eco-friendly design for coal-fired power plants: Combined waste heat recovery and electron beam irradiation

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ENERGY 2022年第0期258卷

作者： Xie, Shutao Qin, Peijia Zhang, Mingliang Xu, Jisong Ouyang, Tiancheng Guangxi Univ Sch Mech Engn Nanning 530004 Peoples R China Guangxi Univ Guangxi Key Lab Petrochem Resource Proc & Proc Int Nanning 530004 Peoples R China

To ensure the sustainable development of the economy and society, energy-saving and emission reduction is an inevitable choice to solve resource scarcity and control environmental pollution, while coal-fired power plants are an important industry to implement this strategy. A novel design combining organic Rankine cycle with electron beam flue gas treatment is proposed to reduce pollutant emissions and recover flue gas waste heat of 600 MW power plant. The flue gas flows through the organic Rankine cycle to recover waste heat, then enters the electron beam irradiation device for desulfurization-denitrification, and eventually generates by-products of ammonium sulfate and ammonium nitrate that could be applied in agriculture. The particle swarm optimization algorithm is employed to improve the thermodynamic, economic and environmental performance, then the most suitable scheme is selected by combining TOPSIS and the entropy weight method. The optimization results indicate that the integrated system could produce equivalent electric quantity of 22.58 MW, and the payback period is 4.91 years. Meanwhile, ammonium sulfate and nitrate with production of 3544 kg/h and 6165 kg/h are obtained. Compared with the referenced power plant, the thermal efficiency of the improved scheme increases by 1.26% and the standard coal consumption rate decreases by 2.72%.

关键词： Organic rankine cycle Electron beam flue gas treatment Desulfurization-denitrification particle swarm optimization algorithm Waste heat recovery

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Optimal Reconfiguration of Thermoelectric Generation System Under Heterogeneous Temperature Difference

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FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Zheng, You Zhang, Enyou An, Peng Ningbo Univ Technol Sch Elect & Informat Engn Ningbo Peoples R China Ningbo Jianan Elect Co Ltd Ningbo Peoples R China

In view of the low efficiency of thermoelectric generation systems in different regions, this paper designs an optimization and reconfiguration of thermoelectric power generation system under heterogeneous temperature difference (HTD) based on particle swarm optimization (PSO) algorithm, so as to make full use of various thermal energy resources and obtain higher electrical output power, which realize multi-directional utilization of energy. In addition, PSO algorithm is a simple optimization strategy with a straightforward operation mechanism and fewer parameters to control during calculation. The research shows that when TEG array is in multiple HTD states, PSO algorithm has a stronger ability to get rid of local optimization, which can reduce power loss and improve energy conversion efficiency. On this basis, PSO algorithm is used to reconfigure the 15 x 15 symmetric TEG array. The experimental simulation analysis based on MATLAB platform is carried out to verify the feasibility of PSO algorithm.

关键词： thermoelectric generation system heterogeneous temperature difference reconfiguration particle swarm optimization algorithm symmetric

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optimization design and analysis of honeycomb micro-perforated plate broadband sound absorber

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APPLIED ACOUSTICS 2022年 186卷 108487-108487页

作者： Yan, Shanlin Wu, Jinwu Chen, Jie Xiong, Yin Mao, Qibo Zhang, Xiang Nanchang Hangkong Univ Sch Aircraft Engn Nanchang 330063 Jiangxi Peoples R China Harbin Inst Technol Sch Mech & Elect Engn Harbin 150006 Peoples R China

The sound absorption performance of micro-perforated plate (MPP) is mainly restricted by four parameters: cavity depth, aperture, plate thickness and perforation rate. The concept of change rate of sound absorption coefficient is introduced, which is taken as the measuring standard to judge the influence of changing these parameters on the sound absorption performance of MPP. Generally, the parameter sensitivity of MPP sound absorber from large to small is as follows: cavity depth, aperture, perforation rate, plate thickness. According to the characteristic that the resonant frequency of MPP will shift when the cavity depth changes, combined with the acoustoelectric analogy principle, two kinds of single-layer honeycomb micro-perforated plate (HMPP) structures with different cavity depth are designed by using particle swarm optimization algorithm. Simulation and experimental results show that the structures can use their own different depth of honeycomb cores to achieve the purpose of broadband sound absorption. The feasibility of using particle swarm optimization algorithm to design broadband sound absorber is verified. In addition, through the contrastive analysis of the two structures, compared to the four regions HMPP, the experimental results of the seven regions HMPP are closer to the theoretical and simulation results, which is consistent with the characteristics of particle swarm optimization (PSO): the more optimization parameters are, the better the performance is. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Micro-perforated plate Change rate of sound absorption coefficient Cavity depth particle swarm optimization algorithm Broadband sound absorption

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Development and optimization of Broadband Acoustic Metamaterial Absorber Based on Parallel-Connection Square Helmholtz Resonators

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MATERIALS 2022年第10期15卷 3417-3417页

作者： Wang, Enshuai Yang, Fei Shen, Xinmin Duan, Haiqin Zhang, Xiaonan Yin, Qin Peng, Wenqiang Yang, Xiaocui Yang, Liu Army Engn Univ PLA Coll Field Engn Nanjing 210007 Peoples R China Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha 410073 Peoples R China Nanjing Vocat Univ Ind Technol Engn Training Ctr Nanjing 210023 Peoples R China Nanjing Univ Aeronaut & Astronaut MIIT Key Lab Multifunct Lightweight Mat & Struct Nanjing 210016 Peoples R China

An acoustic metamaterial absorber of parallel-connection square Helmholtz resonators is proposed in this study, and its sound absorption coefficients are optimized to reduce the noise for the given conditions in the factory. A two-dimensional equivalent simulation model is built to obtain the initial value of parameters and a three-dimensional finite element model is constructed to simulate the sound absorption performance of the metamaterial cell, which aims to improve the research efficiency. The optimal parameters of metamaterial cells are obtained through the particle swarm optimization algorithm, and its effectiveness and accuracy are validated through preparing the experimental sample using 3D printing and measuring the sound absorption coefficient by the standing wave tube detection. The consistency between the experimental data and simulation data verifies feasibility of the proposed optimization method and usefulness of the developed acoustic metamaterial absorber, and the desired sound absorption performances for given conditions are achieved. The experimental results prove that parallel-connection square Helmholtz resonators can achieve an adjustable frequency spectrum for the low frequency noise control by parameter optimization, which is propitious to promote its application in reducing the noise in the factory.

关键词： acoustic metamaterial absorber parallel-connection square Helmholtz resonators low frequency noise control particle swarm optimization algorithm finite element simulation model adjustable frequency spectrum standing wave tube verification

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Dynamic Antenna Selection for Colocated MIMO Radar

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REMOTE SENSING 2022年第12期14卷 2912-2912页

作者： Zhang, Gangsheng Xie, Junwei Zhang, Haowei Li, Zhengjie Qi, Cheng Air Force Engn Univ Air & Missile Def Coll Xian 710051 Peoples R China

Antenna distribution plays an important role for the performance gain in multiple-input-multiple-output (MIMO) radar target tracking. Since to meet the requirements of the low probability of interception, especially in a hostile environment, only a finite number of antennas can be activated at each step. This naturally leads to a performance-driven resource management problem. In this paper, a dynamic antenna selection strategy is proposed for tracking targets in colocated MIMO radar. The derived posterior Cramer-Rao lower bound (PCRLB) of joint direction-of-arrival (DOA) and Doppler estimate were chosen as the optimization criteria. Furthermore, in the deviation, the target radar cross-section (RCS) as the determining variable and the random variable are both discussed. The objective function is related to the antenna allocation and non-convex, and an efficient fast discrete particle swarm optimization (FDPSO) algorithm is proposed for the solution exploration. Additionally, a closed-loop feedback system is established, where the main idea is that the tracking information from the current time epoch is utilized to predict the PCRLB and to guide the antenna adjustment for the next time epoch. The simulation results demonstrate the performance improvement compared with the three fixed-antenna configurations. Moreover, the FDPSO can provide close-to-optimal solutions while satisfying the real-time demand.

关键词： colocated MIMO radar antenna configuration moving target tracking posterior Cramer-Rao lower bound particle swarm optimization algorithm

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Self-Discharge Voltage Drop Estimation Method Based on Improved Gaussian Process Regression

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ENERGY TECHNOLOGY 2022年第7期10卷 2200402-2200402页

作者： Liu, Zhengyu Huang, Wei Wang, Keqing Xie, Juan He, Huijuan Hefei Univ Technol Sch Mech Engn Hefei 230009 Peoples R China Hefei Univ Technol HFUT Intelligent Mfg Inst Hefei 230009 Peoples R China

The self-discharge rate is an important index for determining the quality of a lithium-ion battery. Currently, the self-discharge of a battery is mainly determined through experimental tests, which are time-consuming and laborious. To provide a fast and reliable self-discharge rate estimation, an improved Gaussian process regression (GPR) model based on the charge-discharge curve is proposed to estimate the self-discharge voltage drop. In this method, the voltage features extracted from the charge-discharge curve are used as the input of the GPR model. These features reflect the self-discharge performance of batteries from different angles. The gray correlation analysis method is used to analyze the degree of correlation between the selected features and self-discharge voltage drop. Furthermore, a kernel function suitable for voltage drop estimation is selected. Simultaneously, the GPR model is adjusted, and the particle swarm optimization algorithm is used to optimize the superparameters of the GPR model to improve the estimation accuracy of the self-discharge voltage drop of the model. The self-discharge voltage drop data from the experiment are used to demonstrate the estimation effect of the proposed method. The results reveal that this method is reliable and has a high estimation accuracy for the self-discharge voltage drop.

关键词： Gaussian process regression kernel function particle swarm optimization algorithm self-discharge

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Application of simulated annealing particle swarm optimization in underwater acoustic positioning optimization

Application of simulated annealing particle swarm optimizati...

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OCEANS - Marseille Conference

作者： Li, Jiangqiao Li, Liang Yu, Fujian Ju, Yang Ren, Jiawei Sci & Technol Underwater Acoust Antagonizing Lab Beijing Peoples R China Syst Engn Res Inst Beijing Peoples R China Chinese Acad Sci Inst Acoust Key Lab Speech Acoust & Content Understanding Beijing Peoples R China

ISBN: (纸本)9781728114507

Underwater acoustic positioning system will eventually lead to the estimation error of underwater target position measurement due to factors such as array position measurement error, time delay measurement error, attitude accuracy, sound velocity measurement error and so on. Therefore, the optimal solution method in underwater acoustic positioning system is also one of the key technologies in the field of underwater acoustic positioning technology. The traditional statistical methods of target position estimation mostly use arithmetic average optimization method, Newton iteration method and so on. In this paper, the particle swarm optimization (PSO) algorithm based on simulated annealing is used to estimate the target position, which can improve the estimation accuracy of traditional underwater acoustic location algorithm. In this paper, the statistical methods of three optimal solutions are analyzed by computer simulation. It is verified that the particle swarm optimization method based on simulated annealing can better estimate the target location in low signal-to-noise ratio. In the public literature at home and abroad, arithmetic average optimization method, quality factor weighting method and Newton iteration method are often used to estimate the optimal solution.

关键词： underwater acoustic positioning simulated annealing algorithm particle swarm optimization algorithm optimal solution

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Reservoir production prediction with optimized artificial neural network and time series approaches

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2022年第PartA期215卷

作者： Li, Weirong Wang, Linjun Dong, Zhenzhen Wang, Ruifei Qu, Bochao Shiyou Univ Xi'an Shaanxi Peoples R China

Numerical simulation of oil reservoirs is one of the most commonly used methods for reservoir production prediction, but its accuracy is based on accurate geological modeling and high-quality history matching. Therefore, numerical simulation is time-consuming and costly and requires extensive information. Traditional back-propagation neural networks and their improved algorithms are widely used for production prediction, but they are not suitable for time-series prediction problems. Based on variations in oil production, this study proposes a reservoir production prediction model based on a combined convolutional neural network (CNN) and a long short-term memory (LSTM) neural network model optimized by the particle swarm optimization (PSO) algorithm. First, the model extracts important temporal data features through the upper CNN, which is next imported to the lower LSTM network to further extract correlation features in the time dimension;then, it iteratively optimizes the key hyperparameters in the CNN-LSTM model through the PSO algorithm;finally, it uses the trained model for reservoir prediction. Compared with the training results of the LSTM neural network and CNN model, the PSO-CNN-LSTM model has higher prediction accuracy in time-series production prediction. Our proposed hybrid model is a data-driven method and is based on routinely available production data. Quick and accurate production prediction can lead to better informed operational decisions and optimization of recovery and economics.

关键词： Convolutional neural network Long short-term memory network particle swarm optimization algorithm Hybrid network model Production prediction Time series forecast

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