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Operation parameter optimization of a gas hydrate reservoir developed by cyclic hot water stimulation with a separated-zone horizontal well based on particle swarm algorithm

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Energy 2016年 96卷 581-591页

作者： Jian Hou Zhizeng Xia Shuxia Li Kang Zhou Nu Lu State Key Laboratory of Heavy Oil Processing China University of Petroleum Qingdao Shandong 266580 China College of Petroleum Engineering China University of Petroleum Qingdao Shandong 266580 China

This paper numerically investigated the production characteristics of a gas hydrate reservoir by cyclic hot water stimulation with a separated-zone horizontal well, which combines depressurization and thermal stimulation to improve the development effect. A class II conception model of a heterogeneous gas hydrate reservoir was established based on the reservoir parameters of the China Shenhu area. Optimal operation parameters of this method was determined by maximizing the net energy via PSO (particle swarm optimization). The results show that: (1) high gas production rate (>2 × 10 4 m 3 /d) and high energy ratio (>100) can be obtained using this method. (2) The gas hydrate in the low-permeability area can be recovered effectively, and there is a comparatively uniform cumulative gas production along the horizontal well, indicating the feasibility of the production scheme in recovering heterogeneous gas hydrate reservoirs. (3) To gain high net energy, the temperature of the injected water, the lower gas production rate limit of each cycle, the total amount of injected water and the injection rate should be maintained at high levels within the allowed range; For each segment, the amount of water and the injection rate should remain at a comparable level between areas with high and low permeability, whereas the injection rate should be lowered in the middle areas.

关键词： Gas hydrate reservoir Cyclic hot water stimulation Parameter optimization Separated-zone horizontal well particle swarm algorithm HydrateResSim

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Optimal Design of a Coreless Stator Axial Flux Permanent-Magnet Motor Based on particle swarm algorithm 33

Optimal Design of a Coreless Stator Axial Flux Permanent-Mag...

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33rd Chinese Control Conference (CCC)

作者： Wu Menghua Cao Yongjuan Li Qiang Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Jiangsu Peoples R China Southeast Univ Sch Elect Engn Nanjing 210096 Jiangsu Peoples R China

ISBN: (纸本)9781479947249

The particle swarm optimization (PSO) algorithm and the improved algorithm are used to optimize an three-phase axial flux permanent-magnet (AFPM) motor with 8 pair poles. The objective is to maximize the motor power density, while ensuring the output power, acceptable current density, desired peak value of magnetic flux density and efficiency. Five variables are selected which are the PM thickness, air-gap clearance, pole width to pole pitch ratio, stator winding thickness and inner radius. Firstly, the performance of the AFPM motor is calculated and the model of optimization is built. Then three-dimensional finite-element analysis (FEA) is carried out to simulate the boundaries of the variables. Finally, the AFPM motor is optimized based on the PSO algorithm. The results indicate that the motor power density increases obviously with all the constraint conditions satisfied. PSO algorithm combined with EFA shows great advantage in the optimal of motor.

关键词： Axial Flux Coreless Stator Permanent-magnet motor Optimization Design particle swarm algorithm Three-Dimensional Finite-Element Analysis (FEA)

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Improving Distributed Renewable Energy Power Planning Through particle swarm algorithm

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INTERNATIONAL JOURNAL OF DISTRIBUTED SYSTEMS AND TECHNOLOGIES 2024年第1期15卷 1页

作者： Yao, Honglei Liu, Xin Chang, Yingxian Liu, Donglan Wang, Rui State Grid Shandong Elect Power Res Inst Jinan Peoples R China Shandong Smart Grid Technol Innovat Jinan Peoples R China

The ongoing energy structure reform in our country has led to the emergence of distributed renewable energy as a primary source of energy development and utilization, primarily due to its utilization of local resources. However, challenges such as undefined objectives and ineffective planning have impeded its progress. This study specifically investigates distributed renewable energy power planning by enhancing a particle swarm algorithm with a strategy for updating local optimal solutions. The refined algorithm tackles issues related to renewable energy variability and economic efficiency, thereby optimizing the planning of distributed renewable energy power systems. The outcomes illustrate improvements in system operation, economic viability, and environmental sustainability. This research contributes to the progression of particle swarm algorithms for the planning of distributed renewable energy power systems.

关键词： Distributed particle swarm algorithm Power Planning Renewable Energy

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Comprehensive Rainstorm Intensity Formula Based on particle swarm algorithm

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Meteorological and Environmental Research 2010年第9期1卷 1-3,14页

作者：赵吉武邹长武卢晓宁成都信息工程学院四川成都610225

[Objective] The research aimed to simplify the traditional method and gain the method which could directly construct the comprehensive rainstorm intensity formula.[Method] The particle swarm optimization was used to optimize the parameters of uniform comprehensive rainstorm intensity formula in every return period and directly construct the comprehensive rainstorm intensity ***,took the comprehensive rainstorm intensity formula which was established by the hourly precipitation data in wuhu City as an example,the calculation result compared with the computed result of traditional method.[Result] The calculation result precision of particle swarm algorithm was higher than the traditional method,and the calculation process was simpler.[Conclusion] The particle swarm algorithm could directly construct the comprehensive rainstorm intensity formula.

关键词： particle swarm algorithm Comprehensive rainstorm intensity formula Optimization China

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An improved particle swarm algorithm-based method for kinetic modeling study of ammonia/air laminar flame speed

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FUEL 2024年 363卷

作者： Hu, Yu Li, Jun Chen, Haie Li, Kang Wang, Lei Zhang, Fu Wuhan Univ Technol Sch Automot Engn Wuhan 430070 Peoples R China Foshan Xianhu Lab Natl Energy Key Lab New Hydrogen Ammonia Energy Te Foshan 528200 Peoples R China Tsinghua Univ Sch Vehicle & Mobil Beijing 100084 Peoples R China

In recent years, ammonia has garnered increasing attention as a promising carbon-free fuel. Laminar flame speed is a critical property of ammonia fuel that has been extensively studied by researchers using chemical kinetics mechanisms. However, some deviations still remain in the numerical predictions. To further improve the prediction accuracy of the laminar flame speed for ammonia/air flame, an ammonia kinetic mechanism is developed in this work. Reactions from Gri-Mech 3.0, Li et al. mechanism, and Han et al. mechanism that have significant impact on laminar flame speed were first assembled to the Okafor et al. mechanism, and H/OH sub-mechanisms were merged to establish a kinetic mechanism containing 43 species and 142 reactions. A particle swarm algorithm with improved inertia weights is proposed and the fitness function for outputting laminar flame speed prediction error is customized based on Cantera codes, then forming the algorithm framework used to optimize the kinetic mechanism. The pre-exponential factor A, the temperature exponent n and the activation energy Ea of the four reactions in the merged mechanism that have high sensitivity to the laminar flame speed are selected as the independent variables for optimization, and the reactions rate constants corresponding to the high prediction accuracy of the laminar flame speed is finally obtained. The numerical prediction indicates a reduction in mean absolute percentage error of laminar flame speed from 22.563 % to 10.649 % using the optimization mechanism. The results of the sensitivity and reaction pathways analyses demonstrated that more H-related reactions were considered in the optimized mechanism, and the relative ROP of H-related reactions were adjusted at different equivalence ratios, resulting in higher or lower predictions of laminar flame speed compared with the unoptimized Okafor et al. mechanism. Ignition delay time as well as the species distribution of NH3, NO, and N2O was studied. The ignitio

关键词： Ammonia/air flame particle swarm algorithm Kinetic modeling Optimization Laminar flame speed

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particle swarm Optimization algorithm for a Campaign Planning Problem in Process Industries

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INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH 2008年第22期47卷 8775-8784页

作者： Tang, Lixin Yan, Ping Northeastern Univ Logist Inst Shenyang 110004 Peoples R China

Campaign planning problem (CPP) is to determine the number and length of campaigns for different products over a planning horizon such that the setup and inventory holding costs are minimized. This problem can be found frequently in a multiproduct batch processing plant in the processing industry, such as chemical or pharmaceutical industries. This paper investigates a typical CPP and proposes a hybrid approach of heuristic and particle swarm optimization (PSO) algorithms where the PSO is applied to solve one subproblem with binary variables while the heuristic is applied to the other subproblem with remaining variables by fixing binary variables. As for the evaluation of particles, we take the whole objective function of the primal problem as a fitness function which can be calculated by solving the two subproblems. In implementing the PSO, by designing a "product-to-period" representation for a discrete particle, we redefine the particle position and velocity which are different from the standard PSO. Furthermore, a new strategy is developed to move a particle to the new position. To escape from local minima, a disturbance strategy is also introduced during the iteration process of the PSO. Computational results show that the proposed PSO may find optimal or near optimal solutions for the 180 instances generated randomly within a reasonable computational time.

关键词： Heuristics Planning Horizon optimal solution Iterative Process Manufacturing industry particle swarm optimization computational time CPP gene campaign life parametric subharmonic oscillator particle swarm algorithm

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Identifying drunk driving behavior through a support vector machine model based on particle swarm algorithm

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ADVANCES IN MECHANICAL ENGINEERING 2017年第6期9卷

作者： Li, Min Wang, Wuhong Ranjitkar, Prakash Chen, Tao Beijing Inst Technol Dept Transportat Engn 5 South Zhongguancun St Beijing 100081 Peoples R China Univ Auckland Dept Civil & Environm Engn Fac Engn Auckland New Zealand Changan Univ Minist Transport Key Lab Automot Transportat Safety Tech Xian Peoples R China

Drunk driving is among the main causes of urban road traffic accidents. Currently, contact-type and non-real-time random inspection are methods used to verify whether drivers are drunk driving. However, these techniques cannot meet the actual demand of drunk driving testing. This study considers the following traffic parameters as inputs: speed-up, even-speed, and sharp-turn road segments;vehicle speed;acceleration and accelerator pedal position;and engine speed. Thereafter, this study adopts the support vector machine model to identify drivers' driving behaviors to determine whether they are drunk driving, as well as the particle swarm optimization algorithm to optimize the model, thereby improving training speed. Results show that the support vector machine model based on the particle swarm optimization algorithm can immediately and accurately determine the drunk driving state of drivers, provide theoretical support to non-contact drunk driving test, and realize the foundation of safety driving assistance system toward the adoption of the corresponding measures. Therefore, this study has positive significance in improving traffic safety.

关键词： Driving behavior drunk driving particle swarm algorithm support vector machine

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New particle swarm algorithm for Job Shop Scheduling Problems

New Particle Swarm Algorithm for Job Shop Scheduling Problem...

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7th World Congress on Intelligent Control and Automation

作者： Song, Xiaoyu Chang, Chunguang Cao, Yang Shenyang Jianzhu Univ Sch Informat & Control Engn Shenyang Liaoning Prov Peoples R China

ISBN: (纸本)9781424421138

A new particle swarm algorithm is proposed to make up for the drawback of conventional one on solving job shop scheduling problem, and improve the performance of searching solutions. In the new algorithm, the particle swarm algorithm is adopted to search in the solution space globally. According to the feature of job shop problem solutions, a critical operation-based selection method is proposed, and the TS algorithm based on it is adopted as the local algorithm to strengthen the capability of local search. The new particle swarm algorithm has been tested with 13 hard benchmark problems. The result demonstrates that the obtained best solution and the average value of ten times result are better than parallel genetic algorithm and taboo search algorithm. So the validity of the proposed new particle swarm algorithm is validated.

关键词： particle swarm algorithm Taboo search Job shop scheduling Hybrid algorithm

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Node spatial distribution and layout optimization based on the improved particle swarm algorithm

Node spatial distribution and layout optimization based on t...

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2016 International Conference on Automotive Engineering,Mechanical and Electrical Engineering(AEMEE 2016)

作者： Hongyang Yu Fangwei Cui Yan Wang College of Electronic Information and Optical Engineering Nankai University College of Science Purdue University-West Lafayette College of Mathematics and Computer Science Hunan Normal University

particle swarm Optimization algorithm(PSO) is a kind of adaptive random optimization algorithm based on search strategies. Because the algorithm is simple yet effective, it catches the interest of many scholars. It has showed wide application in function optimization, neural network training, etc. The PSO algorithm development was firstly proposed a decade ago, and is far from being mature neither theoretically nor practically up to now. This paper focuses on improving the traditional particle swarm algorithm and applying it in the wireless sensor network node spatial distribution and layout optimization. Wireless Sensor Network(WSN) is a distributed sensor network which includes sheer numbers of sensors that detects and monitors the outside world. In WSNs, the network settings and the equipment locations can change at any time, can also vary with the Internet connection cable or wireless link. This manuscript adapts the PSO algorithm into WSN design and provides a novel node spatial distribution and layout optimization strategy for optimal network coverage.

关键词： Node spatial distribution particle swarm algorithm Wireless sensor network Distance Vector-Hop Novel Improved PSO & DVH(NIPD)

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An efficient wind measurement method with chaotic-sequence improved genetic-particle swarm optimization algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第3期44卷 5309-5320页

作者： Wang, Encheng Mao, Zichen Wang, Jie Lin, Daming North China Univ Technol Informat Engn Coll Beijing 100144 Peoples R China Minist Transport Res Inst Highway Beijing Peoples R China

Wind power is widely used in industry, meteorology, shipping and so on. Accurate measurement of wind parameters is the key to improve the efficiency of wind power application. But at present, wind parameters are largely measured by different devices based on time difference method, which is easily influnced by enviromental noise. Beam-forming algorithm can improve the ability to resist environmental noise and the accuracy of hardware itself. Therefore, the beam-forming algorithm can be used to measure wind parameters in the high noise environment. However, the efficiency of the algorithm depends on how to search for spectral peak. In this paper, a three-dimensional wind measurement method with chaoticsequence improved genetic-particle swarm optimization algorithm is proposed to improve the waveform searching efficiency of beamforming algorithm. It first searches for rough target wind parameters globally, and then searches for precise target wind parameters locally. Through simulation verification, the proposed algorithm can measure the wind parameters after 0.087s under the condition of system error of 50dB and environmental noise of 20dB, the accuracy of wind speed is 0.5%, the accuracy of wind direction is 1%, and the accuracy of pitch angle is 0.5%. Compared with the wind measurement by traversal method, the proposed algorithm can improve the wind measurement efficiency by about 20 times, and has similar or even better measurement results.. And by comparing with other algorithms, the advantages of this algorithm are verified.

关键词： Three-dimensional wind measurement beam-forming algorithm chaotic sequence genetic algorithm particle swarm algorithm

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