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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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Active power filter module function to improve power quality conditions using GWO and PSO techniques for solar photovoltaic arrays and battery energy storage systems

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JOURNAL OF ENERGY STORAGE 2023年 72卷

作者： Abou Houran, Mohamad Sabzevari, Kiomars Hassan, Alaaeldien Oubelaid, Adel Tostado-Veliz, Marcos Khosravi, Nima Xi An Jiao Tong Univ Sch Elect Engn Xian 710049 Peoples R China Tech & Vocat Univ TVU Dept Elect Engn Tehran Iran Univ Bejaia Fac Technol Lab Technol Ind & Informat Bejaia 06000 Algeria Univ Jaen Dept Elect Engn Linares 23700 Spain R&D Management NPC Dept Elect & Instrumentat Engn Tehran Iran MOP Deputy Res & Dev Tehran Iran

This study aims to unbalanced power quality (PQ) conditions analysis of solar photovoltaic arrays and battery energy storage system (PV-BESS) integrated active power filter module (APFM). Here, the APFM's role is to mitigate the PQ issues that existed by the nonlinear loads. The standalone PV-APFM design is negligibly reliable approximated to a hybrid PV-BESS system because of its fluctuation and high environmental reliance. Further, here the research challenge is to optimise the APFM controller gain coefficients by the grey wolf optimization (GWO) algorithm and apply this technique to current and voltage harmonic loops (CHL-VHL) to achieve the best answer to enhance the unbalanced PQ conditions. The multi-objective functions (MOFs) to crack the problem include the total harmonic distortion (THD) of current and voltage components. The particle swarm optimization (PSO) approach will be considered a complementary method for validation and comparison with the outcomes obtained from the GWO procedure. Four case studies in imbalance conditions have been considered to confirm the proposed method in MATLAB-Simulink software.

关键词： Harmonic condition Total harmonic distortion Active power filter module Grey wolf optimization particle swarm optimization algorithm

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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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The effect of formation thickness on the performance of deterministic and machine learning models for rate of penetration management in inclined and horizontal wells

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2020年 191卷 107160-107160页

作者： Najjarpour, Mohammad Jalalifar, Hossein Norouzi-Apourvari, Saeid Shahid Bahonar Univ Kerman Dept Petr Engn Kerman Iran

Rate of penetration (ROP) is one of the most important parameters in reducing drilling expenditure. In this paper, a ROP management study has been conducted for a well in Southwest of Iran. As a part of this study, the best approach for ROP prediction was determined by comparing the performance of several methods and particle swarm optimization (PSO) algorithm was implemented for optimization of ROP. This paper highlights the effects of formation thickness and the magnitude of working data-set on the performance of different algorithms which are being used for ROP management studies, especially in horizontal and inclined wells. This topic has special importance, when the results show a massive difference in thin and thick formations for some ROP prediction methods. Comparing the results of different ROP prediction methods showed that hybrid Bingham model has the best performance in ROP prediction;only if a powerful mathematical tool like trust-region method is being used for the determination of its unknown coefficients. This superiority was not generalized in all individual formations and there were different results in the cases of thin and thick formations;so, application of the bag-of-algorithms strategy by using the most accurate method in each formation is suggested for the prediction of ROP. Performing this ROP management project resulted in the prediction of ROP with a total relative error of 13.59% and also 48.30% improvement in ROP.

关键词： Rate of penetration Machine learning Deterministic models Data-driven methods Trust-region method particle swarm optimization algorithm

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Research on site selection of agricultural internet of things nodes based on rapid terrain sampling

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2023年 204卷

作者： Xie, Jiaxing Liang, Gaotian Gao, Peng Wang, Weixing Yin, Dongxiao Li, Jun South China Agr Univ Coll Elect Engn Coll Artificial Intelligence Guangzhou 510642 Peoples R China South China Agr Univ Guangdong Lab Lingnan Modern Agr Guangzhou 510642 Peoples R China Luoding Polytech Yunfu 527200 Peoples R China South China Agr Univ Coll Engn Guangzhou 510642 Peoples R China

With the development of Internet of Things (IoT) technology, modern agriculture is moving in the direction of 4.0. The Agricultural IoT is inseparable from wireless communication. However, in traditional agricultural IoT router and gateway site selection, the influence of the actual terrain on transmission loss is not considered, which results in node power wastage and increased maintenance costs. Based on a multi-sensor fusion algorithm, a fast terrain sampler is designed in this study to collect point-cloud data of the experimental site terrain. A reasonable objective function is then designed under the premise of consideration of the electromagnetic wave free-space and diffraction losses, and the locations of the routers and gateway are optimized based on k-means and particle swarm optimization (PSO) algorithm. Simulations show that the running time of the PSO algorithm is very sensitive to the changes in the execution parameters, and the improved PSO algorithm converges faster than the genetic algorithm (GA) in all three initialization methods. After collecting field terrain data, five interpolation methods were compared, and the nearest-neighbor algorithm is used to obtain the terrain model. On-site collection of received signal-strength indication (RSSI) shows that the communication quality of the optimal point selected via this algorithm is significantly higher than those of nearby points. At the same time, it is proved that the RSSI data has serious discontinuities, so the traditional gradient descent method is not suitable for solving the objective function in this work. Therefore, the algorithm used herein is of great significance for the site selection of agricultural Internet of Things nodes. However, since there are many ways to calculate the diffraction loss, the objective function of this work still needs more correction studies. The tool proposed in this work can obtain a 3D model of farmland faster than traditional surveying and mapping methods. Wit

关键词： S ite selection planning Fast terrain sampling modeling particle swarm optimization algorithm Wireless transmission loss Extended Kalman filter

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A Novel Flood Forecasting Method Based on Initial State Variable Correction

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WATER 2018年第1期10卷 12页

作者： Li, Kuang Kan, Guangyuan Ding, Liuqian Dong, Qianjin Liu, Kexin Liang, Lili Minist Water Resources China Inst Water Resources & Hydropower Res Res Ctr Flood & Drought Disaster Reduct State Key Lab Simulat & Regulat Water Cycle River Beijing 100038 Peoples R China Beijing IWHR Technol Co Ltd Beijing 100038 Peoples R China Tsinghua Univ Dept Hydraul Engn State Key Lab Hydrosci & Engn Beijing 100084 Peoples R China Wuhan Univ State Key Lab Water Resources & Hydropower Engn S Wuhan 430072 Peoples R China

The influence of initial state variables on flood forecasting accuracy by using conceptual hydrological models is analyzed in this paper and a novel flood forecasting method based on correction of initial state variables is proposed. The new method is abbreviated as ISVC (Initial State Variable Correction). The ISVC takes the residual between the measured and forecasted flows during the initial period of the flood event as the objective function, and it uses a particle swarm optimization algorithm to correct the initial state variables, which are then used to drive the flood forecasting model. The historical flood events of 11 watersheds in south China are forecasted and verified, and important issues concerning the ISVC application are then discussed. The study results show that the ISVC is effective and applicable in flood forecasting tasks. It can significantly improve the flood forecasting accuracy in most cases.

关键词： flood forecasting forecast accuracy conceptual hydrological model initial state variable particle swarm optimization algorithm

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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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Thermodynamic Multi-Field Coupling optimization of Microsystem Based on Artificial Intelligence

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MICROMACHINES 2023年第2期14卷 411-411页

作者： Shan, Guangbao Wu, Xudong Li, Guoliang Xing, Chaoyang Zhang, Shengchang Fu, Yu Xidian Univ Sch Microelect Xian 710071 Peoples R China Beijing Inst Aerosp Control Devices Beijing 100039 Peoples R China China Acad Aerosp Standardizat & Prod Assurance Beijing 100071 Peoples R China

An efficient multi-objective optimization method of temperature and stress for a microsystem based on particle swarm optimization (PSO) was established, which is used to map the relationship between through-silicon via (TSV) structural design parameters and performance objectives in the microsystem, and complete optimization temperature, stress and thermal expansion deformation efficiently. The relationship between the design and performance parameters is obtained by a finite element method (FEM) simulation model. The neural network is built and trained in order to understand the mapping relationship. Then, the design parameters are iteratively optimized using the PSO algorithm, and the FEM results are used to verify the efficiency and reliability of the optimization methods. When the optimization target of peak temperature, bump temperature, TSV temperature, maximum stress and maximum thermal deformation are set as 100 degrees C, 55 degrees C, 35 degrees C, 180 Mpa and 12 mu m, the optimization results are as follows: the peak temperature is 97.90 degrees C, the bump temperature is 56.01 degrees C, the TSV temperature is 31.52 degrees C, the maximum stress is 247.4 Mpa and the maximum expansion deformation is 11.14 mu m. The corresponding TSV structure design parameters are as follows: the radius of TSV is 10.28 mu m, the pitch is 65 mu m and the thickness of SiO2 is 0.83 mu m. The error between the optimization result and the target temperature is 2.1%, 1.8%, 9.9%, 37.4% and 7.2% respectively. The PSO method has been verified by regression analysis, and the difference between the temperature and deformation optimization results of the FEM method is not more than 3%. The stress error has been analyzed, and the reliability of the developed method has been verified. While ensuring the accuracy of the results, the proposed optimization method reduces the time consumption of a single simulation from 2 h to 70 s, saves a lot of time and human resources, greatly improves

关键词： TSV optimization particle swarm optimization algorithm microsystem

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Optimal Overcurrent Relays Coordination in Microgrid Using Cuckoo algorithm

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Energy Procedia 2016年 100卷 280-286页

作者： Amir Ahmarinejad Seyed Mohsen Hasanpour Mojtaba Babaei Mohammad Tabrizian Department of Electrical Engineering Yadegar-e-Imam Khomeini (RAH) Shahr-e-Rey Branch Islamic Azad University Tehran Iran

Microgrid(MG) is a controlled branch of Distributed Generations(DGs) and loads in distribution network and offers more potential in DGs operation and control. MG can operate with and without utility grid (Island mode). The operation of this system in islanded mode requires sophisticated control and protection schemes. MG protection should be capable of fault detection with appropriate sensitivity and selectivity in both rigid-connected and islanded modes. This paper presents solving the issue of short circuit level difference in both modes using fault current limiter (FCL). Overcurrent(O/C) relays coordination and fault current limiter impedance are selected in such a way that protection system has a suitable operation for both modes. For this purpose, artificial intelligence methods of particle swarm optimization (PSO) algorithm and Cuckoo algorithm have been used to achieve optimal relays coordination and fault current limiter impedance. The under study grid is a small MG related to Tehran Oil Refinery and the simulations have been running using MATLAB. The results indicate that fault current limiter can be used to solve the problem of short current level difference in both modes and Cuckoo algorithm is a more effective method for optimization applications.

关键词： Microgrid Overcurrent Relays Fault Current Limiters Cuckoo algorithm particle swarm optimization algorithm

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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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