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Shape and size determination of plasmonic nano particles using particle swarm optimization algorithm based absorption coefficient

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OPTIK 2017年 130卷 44-49页

作者： Goodarzi, M. Fakharzadeh, A. J. Shiraz Univ Technol Dept Appl Math Shiraz Iran

Plasmonic nano particles can be greatly enhancing the optical absorption coefficient spectrum. Since optical properties of these particles strongly depends on the size and shape of the nano particles, in this paper particle swarm optimization algorithm (pso) is used to optimize the nano particles shape and size in order to amplification of the absorption coefficient. In pso a swarm consists of a matrix with decimal numbers, controls the particles shape and size in order to increase the absorption coefficient in the visible part of light spectrum. It is found that significant plasmonic enhancement of above 100000 can be obtained by optimize selection of particle shapes and sizes. (C) 2016 Elsevier GmbH. All rights reserved.

关键词： Plasmonic nano particle Optimization algorithm pso algorithm

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Development of formation leakage model and process simulation for bullheading well control

GEOENERGY SCIENCE AND ENGINEERING

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GEOENERGY SCIENCE AND ENGINEERING 2024年 243卷

作者： Yin, Hu Zhong, Zhiyong Duan, Mubai Wu, Xianzhu Li, Qian Southwest Petr Univ Petr Engn Sch Chengdu 610500 Peoples R China Southwest Petr Univ Oil & Gas Well Construction Safety Inst Chengdu 610500 Peoples R China CNPC Chuanqing Drilling Engn Co Ltd Chengdu 610051 Peoples R China CNPC Chuanqing Drilling Engn Co Ltd Drilling & Prod Technol Res Inst Guanghan 618300 Peoples R China

In the drilling process of exploiting natural gas, improper wellbore pressure control will lead to gas kicks, which will cause formation gas to enter the wellbore. If the gas reaches the ground, toxic gases such as H2S will cause environmental pollution and even large amount of drilling fluid overflow will further cause major safety accidents such as blowout. Bullheading killing is to inject killing fluid by applying pressure at the wellhead, and push the gas in the wellbore back to the formation. However, it is difficult to obtain formation parameters in high temperature and high pressure environment, and the distribution in wellbore gas is unclear, which makes it difficult to accurately simulate the well killing process and provide reference for actual construction. Therefore, through the characteristics of wellhead overflow in the process of gas kicks, combined with particle swarm optimization (pso) algorithm, the formation parameters are solved, and then a new leakage model is derived to describe the resistance of gas being pushed back to the formation. Well W2 and Well X3 are taken as examples to verify the model. The results show that the error between wellhead pressure and actual wellhead pressure data is 6.3% and 6.9% respectively, which shows satisfactory calculation accuracy. The specific process of obtaining construction parameters for this engineering technology is put forward. The research in this paper is of great significance for modeling and guiding well killing operation by bullheading killing.

关键词： Bullheading Gas kicks Leakage model pso algorithm Well control

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Lyapunov-based adaptive PID controller design for buck converter

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SOFT COMPUTING 2023年第9期27卷 5741-5750页

作者： Ghamari, Seyyed Morteza Khavari, Fatemeh Mollaee, Hasan Shahrood Univ Technol Fac Elect Engn Shahrood Iran

In this paper, a Lyapunov-based model reference proportional-integrated-derivative (PID) is designed using this approach for a DC / DC Buck converter. However, under a wider range of disturbances, the PID strategy is not suitable for practical applications, and the parameters must be tuned again for more reliable operations. To this end, a Lyapunov definition-based adaptive mechanism is adopted for the PID strategy, which can increase the stability and robustness of this scheme under various disturbances. Moreover, the system is considered a black-box without the need for accurate mathematical modeling of the system that can result in a lower computational burden as well as ease of implementation. The Lyapunov concept is a modern adaptive algorithm that can find optimal answers in shorter periods with more accuracy and reliable stability insurance. To examine the strength of this method, conventional fractional-order PID (FOPID) and PID control techniques are also tested to be compared with this work using the pso algorithm to tune their parameters. Finally, the results related to both simulations and experimental outputs are tested, showing faster dynamics and significant robustness of Lyapunov-based PID strategy in different challenging scenarios.

关键词： Buck converter Lyapunov stability PID controller pso algorithm Noise

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Comprehensive energy, exergy, and environmental evaluation of integrated heat pipe photovoltaic/thermal systems using multi-objective optimization

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APPLIED THERMAL ENGINEERING 2024年 257卷

作者： Ghasempour, Roghayeh Salehi, Mohammad Fakouriyan, Samaneh Univ Tehran Coll Interdisciplinary Sci & Technol Sch Energy Engn Renewable Energy Dept Tehran Iran Univ Tehran Coll Interdisciplinary Sci & Technol Sch Energy Engn Nanofluid & Graphene Based Nanostruct Res Lab NGNR Tehran Iran Tyndall Natl Inst Int Energy Res Ctr IERC Cork Ireland

The depletion of fossil fuels and their environmental emissions have led to a tendency toward the implementation of sustainable energy sources. This study aims to evaluate the energy, exergy, and environmental performance of photovoltaic/thermal systems-based heat pipes. Furthermore, the electrical exergy and thermal efficiency of the photovoltaic/thermal system are analyzed. In this regard, two methods of multi-objective evolutionary algorithm based on non-dominated sorting genetic algorithm II (NSGA II) and multi-objective particle swarm optimization (MOpso) are utilized. The simulated heat pipes are regarded as thermosyphon and pulsating type with water and acetone as the working fluid. The novelty of this study lies in the comparative analysis of thermosyphon and pulsating heat pipes with water and acetone as working fluids in PV/T systems, combined with the application of advanced multi-objective optimization algorithms, offering a comprehensive. The result demonstrates that acetone, as a working fluid, can help achieve better performance compared to water, and also pulsating heat pipe illustrates greater performance rather than the thermosyphon heat pipe. Moreover, the result reveals that the electrical exergy efficiency of the photovoltaic panel equipped with thermosyphon heat pipe with acetone has increased by 14.2 % compared to the photovoltaic panels without the heat pipe. In addition, utilizing thermosyphon base photovoltaic/thermal system with acetone reduces the production of greenhouse gases by 894.5 CO2 eq kg/m(2).year, which is a further reduction of 156.8 CO2 eq kg/m(2).year compared to the photovoltaic panel without a heat pipe.

关键词： Photovoltaic/thermal system Thermosyphon heat pipe Pulsating heat pipe Exergy analysis Environmental analysis pso algorithm

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GWO-Based Tuning of LQR-PID Controller for a 3-DOF Parallel Manipulator

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IETE JOURNAL OF RESEARCH 2023年第7期69卷 4378-4393页

作者： Choubey, Chandan Ohri, Jyoti Natl Inst Technol Elect Dept Kurukshetra Haryana India

This paper presents the mathematical modeling and optimal trajectory tracking control of a 3-degree-of-freedom parallel manipulator, commonly known as Maryland manipulator. Three unlike sequential trajectories are considered, and the trajectory-tracking control of a manipulator is performed by the LQR-PID controller. The tuning of LQR-PID is done by using the Grey Wolf Optimizer (GWO), a meta-heuristic method. The two other traditional benchmarking algorithms are Particle Swarm Optimizer (pso) and Genetic algorithm (GA). The main goal of this work is to calculate the optimum values of LQR gain parameters i.e. Q and R matrices and PID controller's gains i.e. K-p, K-i and K-d using three different optimization algorithms, and their performance comparisons are highlighted in simulation results and discussion section. According to the obtained simulation results, the proposed GWO methodology is more efficient and more accurate in trajectory tracking control and generates optimal torques to the input links of the manipulator when compared with other evolutionary optimization algorithms such as pso and GA.

关键词： GA algorithm GWO algorithm parallel manipulator pso algorithm trajectory control tuning LQR-PID torques

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Intelligent integrated optimization of mining and ore-dressing grades in metal mines

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SOFT COMPUTING 2018年第1期22卷 283-299页

作者： He, Yong Liao, Nuo Bi, Jiajing Guangdong Univ Technol Sch Management Guangzhou 510520 Guangdong Peoples R China Univ Delaware Ctr Energy & Environm Policy Newark DE 19716 USA

An intelligent integrated method is proposed for optimizing the head grade and dressing grade in the mining and ore-dressing management of metal mines, beginning with the establishment of a nonlinear constrained optimization model with the objective function of economic benefit, two constraints comprising of the resource utilization rate and the output of concentrate, along with head grade and dressing grade as the decision variables. Particle swarm optimization (pso) algorithm is then integrated with artificial neural networks to create a pso-ANN algorithm capable of identifying the optimal grade combination. The outer layer of pso-ANN uses the pso algorithm to carry out a global search, with the head grade and dressing grade being combined as swarm particles for evolutionary computation. The constraint handling techniques of feasibility-based rules are used to update the historical best location of each particle (pbest) and the global best location of the swarm (gbest) to guide the particles toward the optimum. The inner layer uses regression model, BPNN and RBFNN to calculate the loss rate, ore-dressing metal recovery rate and costs, respectively, to facilitate the further calculation of the resource utilization rate, the concentrate output and the economic benefit of each particle. Finally, the proposed method is tested by carrying out a case study based upon Daye Iron Mine to indicate its effectiveness and reliability.

关键词： Mining and ore-dressing grades Intelligent constrained optimization pso algorithm Artificial neural networks

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Optimal trajectory generation algorithm for serial and parallel manipulators

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ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING 2017年 48卷 219-232页

作者： Kucuk, Serdar Kocaeli Univ Technol Fac Dept Biomed Engn Umuttepe Campus TR-41380 Kocaeli Turkey

In this paper, an Optimal Trajectory Generation algorithm (OTGA) is developed for generating minimum-time smooth motion trajectories for serial and parallel manipulators. OTGA is divided into two phases. The first phase encompasses derivation of minimum-time optimal trajectory using cubic spline due to its less vibration and overshoot characteristics. Although cubic splines are widely used in robotics, velocity and acceleration ripples in the first & last knots can worsen manipulator trajectory. The second phase includes changing cubic spline interpolation in the first and last knots of optimized trajectory with 7th order polynomial for having zero jerk at the beginning and end points of trajectory. Performing this modification eliminate undesired worsening in the trajectory and provide smoother start and stop of joint motions. Particle Swarm Optimization (pso) is chosen as optimization algorithm because of its easy implementation and successful optimization performance. OTGA has been tested in simulation for PUMA robot and results are compared with algorithms proposed by earlier authors. In addition, a discrete-time PID control scheme for PUMA robot is designed for comparing energy consumption of OTGA with algorithms developed by previous authors. Comparison results illustrated that OTGA is the better trajectory generation algorithm than the others.

关键词： Trajectory generation Cubic spline Jerk pso algorithm Serial and parallel manipulators

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A two-stage optimal scheduling method for active distribution networks considering uncertainty risk

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ENERGY REPORTS 2021年 7卷 4633-4641页

作者： Ma, Hengrui Liu, Zhe Li, Min Wang, Bo Si, Yang Yang, Yan Mohamed, Mohamed A. Qinghai Univ Tus Inst Renewable Energy Xining Qinghai Peoples R China Guangxi Power Grid Co Ltd Nanning Guangxi Peoples R China Wuhan Text Univ Sch Math & Comp Wuhan Hubei Peoples R China Wuhan Univ Sch Elect Engn & Automat Wuhan Hubei Peoples R China Minia Univ Fac Engn Elect Engn Dept Al Minya 61519 Egypt Fuzhou Univ Dept Elect Engn Fuzhou 350116 Peoples R China

The uncertainty of distributed energy generation and the uncertainty risk of system components failure are important means to ameliorate the economy and reliability of Active Distribution Network (ADN). In this paper, a two-stage optimal scheduling method for ADN considering uncertainty risk is proposed. Considering the uncertainty risk of renewable resources, load and electricity price, and the uncertainty risk of system component failure, the two-stage optimal dispatching process of ADN with energy storage system is investigated. Firstly, the first stage model is established to minimize the cost of operation of ADN under normal operation conditions, and then the second stage model is established to minimize the load reduction under emergency operation. Particle Swarm Optimization (pso) algorithm is proposed for model optimization. The simulation outcomes on IEEE-33 bus system simulation demonstrated high effectiveness of the proposed method. (C) 2021 The Authors. Published by Elsevier Ltd.

关键词： Two-stage optimal scheduling model Distributed energy Uncertainty risk Active distribution network pso algorithm

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Novel weight-adaptive fusion grey prediction model based on interval sequences and its applications

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APPLIED MATHEMATICAL MODELLING 2023年 115卷 803-818页

作者： Ye, Jing Li, Yan Ma, Zhenzhen Xiong, Pingping Nanjing Univ Finance & Econ Sch Management Sci & Engn 3 Wenyuan Rd Nanjing 210023 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 210016 Peoples R China Beijing Wuzi Univ Logist Sch Beijing 101149 Peoples R China Nanjing Univ Informat Sci & Technol Sch Management Sci & Engn Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Inst Mfg Dev Nanjing 210044 State State Peoples R China

With the increasing complexity of systems, it is difficult to describe a huge amount of in-formation accurately with real numbers. To make predictions using uncertain information, this study uses interval numbers as descriptors and proposes a weight-adaptive fusion grey prediction model based on interval sequences (WAFGM(1,N) model). Unlike most interval prediction models;the proposed model uses the adaptive weight and compensation coeffi-cient to directly predict interval grey numbers without converting interval sequences into real number sequences. Moreover, it improves the four errors of the traditional GM(1,N) model, thereby further improving model accuracy. First, by fully considering the integrity and interaction of the upper and lower boundaries of the interval, this study introduced a weight parameter to comprehensively characterize the development trend of the interval, and by optimizing the background value and adjusting the compensation coefficient, the univariate and multivariate weight-adaptive grey prediction models for interval prediction were constructed, respectively. Second, the interval development and driving coefficients, compensation coefficient, and weight parameter were estimated and optimized using the least squares method, accumulation method, and particle swarm algorithm, respectively. Finally, the WAFGM(1,N) model was constructed by combining the two models. The valid-ity and applicability of the WAFGM(1,N) model were proved using two examples: the gross agricultural output value of Northeast China and the production of dairy products in the golden milk source areas.(c) 2022 Elsevier Inc. All rights reserved.

关键词： Grey forecast GM(1N) model Weight adaptation Interval sequences pso algorithm

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Optimal capacity allocation of standalone wind/solar/battery hybrid power system based on improved particle swarm optimisation algorithm

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IET RENEWABLE POWER GENERATION 2013年第5期7卷 443-448页

作者： Wang, Jidong Yang, Fan Tianjin Univ Minist Educ Key Lab Smart Grid Tianjin 300072 Peoples R China Tianjin Univ Sch Elect Engn & Automat Tianjin 300072 Peoples R China

A standalone wind/solar/battery hybrid power system, making full use of the nature complementarity between wind and solar energy, has an extensive application prospect among various newly developed energy technologies. The capacity of the hybrid power system needs to be optimised in order to make a tradeoff between power reliability and cost. In this study, each part of the wind/solar/battery hybrid power system is analysed in detail and an objective function combining total owning cost and loss of power supply probability is built. To solve the problems with non-linearity, complexity and huge computation, an improved particle swarm optimisation (pso) algorithm is developed, which integrates the taboo list to broaden the search range and introduces 'restart' and 'disturbance' operation to enhance the global searching capability. The simulation results indicate that the proposed algorithm is more stable and provides better results in solving the optimal allocation of the capacity of the standalone wind/solar/battery hybrid power system compared with the standard pso algorithm.

关键词： battery storage plants hybrid power systems particle swarm optimisation power generation economics power generation reliability solar power stations wind power plants optimal capacity allocation standalone wind-solar-battery hybrid power system particle swarm optimisation algorithm wind energy solar energy power reliability power cost power supply probability pso algorithm

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