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optimization control of hydrometallurgical leaching process based on IDE-MPC

MINERALS ENGINEERING 2022年第0期176卷 107341-107341页

作者： Niu, Dapeng Liu, Xudong Liu, Yuanqing Jia, Mingxing Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China

The utilization rate of raw materials during the hydrometallurgical leaching process has a great influence on the whole economic benefits of the hydrometallurgy plant, so it is necessary for the leaching process to improve the utilization of materials using optimization control methods. In this paper, the dynamic model for the hydrometallurgical leaching process of a gold hydrometallurgy plant is first built based on the reaction mechanism of the process. Then, the model parameters are identified using least-squares fitting. Thereafter, with the maximum economic benefit as the objective function, the steady-state economic optimization model of the leaching process is established, and an improved particle swarm optimization algorithm is used to solve the model. Taking the optimization results as the control objective, a model predictive control method based on an improved differential evolution algorithm is proposed to control the leaching process, so as to improve the intractability and anti-disturbance performance of the controller for the leaching process. The simulation results show that the proposed optimization and control methods achieve satisfactory effects.

关键词： Hydrometallurgical leaching process optimization control particle swarm optimization algorithm Model predictive control Differential evolution algorithm

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Model and Optimize the Magnetic Composite Fluid (MCF) Polishing Process with Machine Learning Modeling and Intelligent optimization algorithm

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INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING 2022年第9期23卷 957-967页

作者： Fan, Jinwei Ren, Xingfei Pan, Ri Wang, Peitong Tao, Haohao Beijing Univ Technol Fac Mat & Mfg Beijing 100124 Peoples R China

In the magnetic composite fluid (MCF) polishing process, appropriate polishing parameters are the basis of achieving high-quality polishing without damage. Appropriate polishing parameters are mainly based on an accurate polishing model and an excellent polishing parameters optimization algorithm. However, due to the complicated principle of MCF polishing and various influencing elements, traditional modeling methods have the limitations of low accuracy, poor application, and difficulty in correcting. Therefore, it is challenging to obtain the optimal polishing quality by optimizing the polishing parameters based on the traditional model. This study proposed an online modeling approach considering data cleaning based on machine learning modeling, and the particle swarm optimization (PSO) algorithm was used to optimize polishing parameters. Then, copper polishing experiments were carried out to validate the modeling and optimization methods. The results demonstrate that the proposed machine learning online modeling method can establish an accurate MCF polishing model, and the nano-scale fine polishing of copper can be achieved by the optimized polishing parameters of PSO, and the surface roughness of the copper sample was reduced by 85% to 0.031 mu m.

关键词： Magnetic composite fluid polishing particle swarm optimization algorithm Polishing parameters Surface roughness

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Parameter tuning of auto disturbance rejection controller based on improved glowworm swarm optimization algorithm

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ASSEMBLY AUTOMATION 2022年第4期42卷 427-444页

作者： Gao, Bingwei Shen, Wei Dai, Ye Ye, Yong Tai Harbin Univ Sci & Technol Sch Mech & Power Engn Harbin Peoples R China Harbin Univ Sci & Technol Key Lab Adv Mfg & Intelligent Technol Minist Educ Harbin Peoples R China

Purpose This paper aims to study a parameter tuning method for the active disturbance rejection control (ADRC) to improve the anti-interference ability and position tracking of the performance of the servo system, and to ensure the stability and accuracy of practical applications. Design/methodology/approach This study proposes a parameter self-tuning method for ADRC based on an improved glowworm swarm optimization algorithm. The algorithm is improved by using sine and cosine local optimization operators and an adaptive mutation strategy. The improved algorithm is then used for parameter tuning of the ADRC to improve the anti-interference ability of the control system and ensure the accuracy of the controller parameters. Findings The authors designed an optimization model based on MATLAB, selected examples of simulation and experimental research and compared it with the standard glowworm swarm optimization algorithm, particle swarm algorithm and artificial bee colony algorithm. The results show that the response time of using the improved glowworm swarm optimization algorithm to optimize the auto-disturbance rejection control is short;there is no overshoot;the tracking process is relatively stable;the anti-interference ability is strong;and the optimization effect is better. Originality/value The innovation of this study is to improve the glowworm swarm optimization algorithm, propose a sine and cosine, local optimization operator, expand the firefly search space and introduce a new adaptive mutation strategy to adaptively adjust the mutation probability based on the fitness value, improve the global search ability of the algorithm and use the improved algorithm to adjust the parameters of the active disturbance rejection controller.

关键词： Auto-disturbance rejection controller Glowworm swarm optimization algorithm Parameter tuning particle swarm optimization algorithm External disturbance

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An efficient hybrid approach based on PSO, ABC and k-means for cluster analysis

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第14期81卷 19321-19339页

作者： Pu, Qiumei Gan, Jingkai Qiu, Lirong Duan, Jiaxin Wang, Hui Minzu Univ China Sch Informat Engn Beijing 100081 Peoples R China

particle swarm optimization (PSO) algorithm is one of the typical example of swarm Intelligence (SI) algorithm. This article addresses such problems of PSO algorithm as random initial position of each particle, unsmooth speed weight change, and poor search ability, and proposes an optimization algorithm-hybrid PSO (HPSO) algorithm to solve these problems. This algorithm makes comprehensive improvements to the PSO clustering algorithm by using the K-means clustering algorithm to generate initial clustering centers, adopting a negative exponential function model to update the weight of velocity when constructing the "position-velocity" model, and introducing the "search restriction" mechanism, and the "fly-back" mechanism and auxiliary search methods such as the single point crossover operator in the Artificial Bee Colony (ABC) algorithm. Furthermore, experimental results were analyzed and verified. The experiment compares HPSO algorithm with K-Means algorithm, PSO algorithm, and other two typical improved algorithms from the literature on six of the UCI standard clustering test data sets. The results indicate that HPSO algorithm has good performance in stability, clustering effectiveness, robustness and global search ability.

关键词： swarm intelligence algorithm particle swarm optimization algorithm HPSO algorithm K-means algorithm Single point crossover operator

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Relay Protection Setting Calculation System for Nuclear Power Plant Based on B/S Architecture and Cloud Computing

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ENERGIES 2022年第24期15卷 9648页

作者： Hong, Yuan Yu, You Tian, Jingfu Ye, Han Wang, Bin Yu, Wenxiang Shandong JiaoTong Univ Sch Informat & Elect Engn Jinan 250357 Peoples R China State Grid Liaoning Elect Power Co Ltd Shenyang 110004 Peoples R China Shan Dong Cheersun Intelligent Technol Co Ltd Jinan 250101 Peoples R China Tsinghua Univ Dept Elect Engn State Key Lab Control & Simulat Power Syst & Gener Haidian Dist Beijing 100084 Peoples R China

Nuclear power plants have a complex structure and changeable operation mode, which induces low setting calculation efficiency. After analyzing the technology, architecture, and functional logic of a variety of relay protection setting calculation systems and combining the characteristics of the setting calculation of nuclear power plants, the relay protection setting calculation system in nuclear power plants based on B/S architecture and cloud computing is studied in this paper. The system adopts three-tier B/S architecture, applies two key technologies, the cloud computing task distribution synchronization mechanism and the cloud component automatic assembly mechanism, and introduces a particle swarm optimization algorithm to provide technical support for nuclear power plant setting calculation;the running example of the nuclear power plant system fully proves the efficiency and reliability of the relay protection setting calculation system of the nuclear power plant, which has high practical value.

关键词： setting calculation three-tier B S architecture cloud computing cloud component particle swarm optimization algorithm

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New control strategy for suppressing the local vibration of sandwich beams based on the wave propagation method

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JOURNAL OF INTELLIGENT MATERIAL SYSTEMS AND STRUCTURES 2022年第1期33卷 231-247页

作者： Xu, Jun Chen, Yandong Tai, Yongpeng Shi, Guodong Chen, Ning Yao, Jialing Nanjing Forestry Univ Dept Mech & Elect Engn 159 Longpan Rd Nanjing 210037 Peoples R China Nanjing Forestry Univ Dept Automobile & Traff Engn Nanjing Peoples R China

The general vibration control strategy of beam structures is a global vibration control method based on the principle of modal superposition. However, the vibration wave control of beams can achieve local control of the vibration energy. This paper presents a wave control method for the local vibration control of fractional viscoelastic composite beams based on the operating principle of a piezoelectric sheet. To obtain better control performance, a particle swarm optimization algorithm was adopted to optimize the parameters of the piezoelectric sheet. A linear quadratic regulator control algorithm was designed to verify the validity of the proposed method. In addition, the effects of the piezoelectric sheet number and fractional order on the amplitude response and optimization parameters were investigated. We observed that the proposed method has a good control effect on the local area vibration, and it can control the flow direction of the vibration power. The proposed method can be used to directly design the voltage and phase of a piezoelectric sheet without real-time feedback computation. This method is suitable for reducing local vibration under single repetitive operating conditions in engineering and can provide a theoretical basis for a follow-up study on the acoustic black hole phenomenon.

关键词： Fractional order composite beam piezoelectric sheet local vibration control particle swarm optimization algorithm wave control method

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Optimal Compensation of MEMS Gyroscope Noise Kalman Filter Based on Conv-DAE and MultiTCN-Attention Model in Static Base Environment

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SENSORS 2022年第19期22卷 7249-7249页

作者： Huo, Zimin Wang, Fuchao Shen, Honghai Sun, Xin Zhang, Jingzhong Li, Yaobin Chu, Hairong Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun 130033 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Key Lab Airborne Opt Imaging & Measurement Changchun 130033 Peoples R China Forest Protect Res Inst Heilongjiang Prov Harbin 150040 Peoples R China

Errors in microelectromechanical systems (MEMS) inertial measurement units (IMUs) are large, complex, nonlinear, and time varying. The traditional noise reduction and compensation methods based on traditional models are not applicable. This paper proposes a noise reduction method based on multi-layer combined deep learning for the MEMS gyroscope in the static base state. In this method, the combined model of MEMS gyroscope is constructed by Convolutional Denoising Auto-Encoder (Conv-DAE) and Multi-layer Temporal Convolutional Neural with the Attention Mechanism (MultiTCN-Attention) model. Based on the robust data processing capability of deep learning, the noise features are obtained from the past gyroscope data, and the parameter optimization of the Kalman filter (KF) by the particle swarm optimization algorithm (PSO) significantly improves the filtering and noise reduction accuracy. The experimental results show that, compared with the original data, the noise standard deviation of the filtering effect of the combined model proposed in this paper decreases by 77.81% and 76.44% on the x and y axes, respectively;compared with the existing MEMS gyroscope noise compensation method based on the Autoregressive Moving Average with Kalman filter (ARMA-KF) model, the noise standard deviation of the filtering effect of the combined model proposed in this paper decreases by 44.00% and 46.66% on the x and y axes, respectively, reducing the noise impact by nearly three times.

关键词： MEMS gyroscope convolutional denoising autoencoder temporal convolutional network attention mechanism particle swarm optimization algorithm Kalman filter

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particle swarm-differential evolution algorithm with multiple random mutation

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APPLIED SOFT COMPUTING 2022年第0期120卷

作者： Lin, Meijin Wang, Zhenyu Chen, Danfeng Zheng, Weijia Foshan Univ Sch Mechatron Engn & Automat Foshan 528000 Peoples R China

Differential evolution (DE) algorithm has attracted considerable attention because of its effectiveness and simplicity. However, previous studies have validated that DE still suffers from some limitations such as premature convergence and slow convergence especially dealing with multimodal optimization problems. To address these concerning issues, we propose an innovative optimization method named particle swarm-differential evolution algorithm with multiple random mutation (MRPSODE) in this paper. The proposed MRPSODE algorithm is based on multiple random mutation framework cooperating with mean particle swarm mutation strategy, DE/current-to-rand/1 mutation strategy and disturbance strategy. Firstly, we incorporate the modified mean particle swarm mutation strategy into DE algorithm to improve the global convergence ability. Secondly, DE/current-to-rand/1 mutation strategy is adopted to increase the population diversity and produce perturbations to avoid the algorithm trapping into a local optimum. Thirdly, we propose a disturbance strategy to help the population escape from local optima, so as to enhance the exploration ability. Finally, to ensure that the proposed algorithm can get satisfactory solutions with a fast convergence speed, we design a multiple random mutation framework, in which these three mutation strategies can effectively play their advantages and make up for the shortcomings of others. To evaluate the performance of the proposed algorithm, three different experiments are constructed on twenty-nine classical benchmark functions. The simulation results demonstrate that, (1) MRPSODE significantly outperforms conventional PSO and DE algorithms, (2) MRPSODE can achieve better performance than nine well-known DE variants in terms of solution quality and robustness, (3) MRPSODE is superior to nine latest heuristic-based algorithms. Furthermore, MRPSODE is successfully applied to seven typical constrained optimization problems and performs better than a

关键词： particle swarm optimization algorithm Differential evolution Disturbance strategy Multiple random mutation Constrained optimization problems

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Improved Whale optimization algorithm for VSG Control

Improved Whale Optimization Algorithm for VSG Control

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2025 International Conference on Electrical Automation and Artificial Intelligence, ICEAAI 2025

作者： Xia, Mingliang Wang, Xihuai Shanghai Maritime University School of Logistics Engineering Shanghai China

ISBN: (纸本)9798331506797

With the increasing seriousness of energy problems and the extensive use of new energy sources, how to ensure that the new energy power generation system has good grid-connected stability has become a new research hotspot. Virtual synchronous generator (VSG) technology introduces the operating characteristics of synchronous generator (SG) on the basis of grid-connected inverter, so that it has the same rotor inertia and damping. However, the design of VSG system parameters is complicated, and the traditional whale optimization (WOA) algorithm often fails to achieve the best optimization effect. To address this problem, a mathematical model of the VSG system is established, and based on the WOA algorithm, a mixed-strategy improved whale optimization (IWOA) algorithm is proposed. By comparing the simulation results under the environment of grid voltage and frequency fluctuation, it is proved that the IWOA algorithm is more suitable for VSG grid stabilization. © 2025 IEEE.

关键词： grid particle swarm optimization algorithm power oscillation stability virtual synchronous generator

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Damage localization in pressure vessel using guided wave-based techniques: Optimizing the sensor array configuration to mitigate nozzle effects

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APPLIED ACOUSTICS 2022年 185卷 108393-108393页

作者： Hu, Chaojie Yang, Bin Xiao, Biao Xuan, Fu-Zhen Xiang, Yanxun East China Univ Sci & Technol Sch Mech & Power Engn Shanghai Peoples R China Tongji Univ Sch Aerosp Engn & Appl Mech Shanghai Peoples R China Shanghai Inst Special Equipment Inspect & Tech Re Shanghai Peoples R China

Ultrasonic guided waves are desirable technologies for damage monitoring in large structures such as pipes and pressure vessels since they could propagate over a long distance with low attenuation. However, due to the complex scattering of guided waves, it is a great challenge to extract the damage information when using guided wave-based techniques to monitor the pressure vessels equipped with accessories. As an attempt, we investigated the interactive behavior between guided waves and pressure vessel nozzles. Ensemble empirical mode decomposition, fast Fourier transform and ellipse-based damage localization algorithm are adopted to perform the signal processing and damage localization analysis, respectively. A sensor array configuration optimization method is presented to mitigate the nozzle effects on the propagation of guided waves. The optimal number of sensors is determined experimentally, and the sensor array placement is optimized by a particle swarm optimization algorithm. The accurate experimental damage localization results in the pressure vessel and curved plate indicate the effectiveness of our methods. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Pressure vessel nozzle Sensor array Guided wave Damage localization particle swarm optimization algorithm

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