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End-point prediction of 260 tons basic oxygen furnace (BOF) steelmaking based on WNPSVR and WOA

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第2期41卷 2923-2937页

作者： Liu, Liming Li, Ping Chu, Maoxiang Gao, Chuang Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan Peoples R China

Basic oxygen furnace (BOF) steelmaking plays an important role in steelmaking process. Hence, it is necessary to study BOF steelmaking modeling. In this paper, a novel regression algorithm is proposed by using nonparallel support vector regression with weight information (WNPSVR) for the end-point prediction of BOF steelmaking. The weight information is excavated by K-nearest neighbors (KNNs) algorithm. Since the whale optimization algorithm (WOA) has the characteristics of fast convergence speed and a few adjustment parameters, WOA is applied to optimize the parameters in the objective function of WNPSVR. Compared with traditional prediction models, WNPSVR-WOA is not easy to fall into local minimum values and is insensitive to noise. Thus, the prediction and control of molten steel end-point information are more accurate. Experimental results verify the effectiveness and feasibility of the proposed model. Within different error bounds (0.005 wt.% for carbon content model and 10 degrees C for temperature model), the hit rates of carbon content and temperature are 89% and 95%, respectively. Meanwhile, a double hit rate of 85% is achieved. The above results conclude that our WNPSVR-WOA has important reference value for actual BOF application and can improve the steel product quality. Moreover, WNPSVR-WOA can also be used to other fields.

关键词： Basic oxygen furnace end-point information prediction nonparallel support vector regression weight information whale optimization algorithm

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Multi-objective optimization model of micro-grid access to 5G base station under the background of China's carbon peak shaving and carbon neutrality targets

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FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Guo, Weishang Mao, Yihua Chen, Yufen Zhang, Xuexing Zhejiang Univ Coll Civil Engn & Architecture Hangzhou Zhejiang Peoples R China Taizhou Univ Sch Civil Engn & Architecture Taizhou Zhejiang Peoples R China Fangyuan Construction Grp Co Ltd Taizhou Zhejiang Peoples R China

a large number of 5G base station are connected, which provides a new possibility for the future low-carbon development of power systems. By encouraging 5G base station to participate in demand response and incorporating it into the Microgrid, it can reduce the power consumption cost of 5G base stations and promote the efficient utilization of renewable energy. Based on the microgrid operation structure, 5G base station and multi-objective problem algorithm, a multi-objective optimization operation model of microgrid access to 5G base station is built. Considering the physical constraints of Microgrid, the objective is to minimize the operating cost and carbon emission. Through the joint dispatching of distributed clean energy generation, micro gas turbine, energy storage system and 5G base station in Microgrid, the comprehensive optimization of system economy and low-carbon benefits can be achieved. In this paper, a microgrid in Beijing is taken as the research object, and the whale optimization algorithm algorithm is used to solve the multiobjective problem. The analysis results show that 5G base station can flexibly respond to microgrid scheduling, which helps microgrid to improve the consumption and utilization efficiency of renewable energy, thus bringing higher economic benefits and low-carbon benefits, and helping China to achieve the goal of carbon peak shaving and carbon neutrality at an early date.

关键词： microgrid multi objective optimization 5G base station carbon peak shaving and carbon neutrality whale optimization algorithm

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Interface Reliability Modeling of Coaxial Through Silicon Via Based on WOA-BP Neural Network

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JOURNAL OF ELECTRONIC PACKAGING 2024年第3期146卷 031003-031003页

作者： Zhang, Liwen Yang, Chen Yang, He Wang, Jinchan Zhang, Jincan Henan Univ Sci & Technol Coll Informat Engn 263 Kaiyuan Ave Luoyang 471023 Henan Province Peoples R China

Due to the complex structure and thermal mismatch of coaxial through silicon via (TSV), cracks easily occur under thermal load, leading to interface delamination or spalling failure. The reliability issue of coaxial TSV is important for its application in three-dimensional packaging, so it is of great significance to predict the crack trend and evaluate the reliability of coaxial TSV. In this paper, an algorithm model with the combination of whale optimization algorithm (WOA) and back propagation (BP) neural network for the reliability prediction of coaxial TSV is proposed. Based on finite element method (FEM), the training and validation datasets of the energy release rates (ERR) of the crack at the critical interface are calculated to construct the deep learning neural network. Six key structure parameters affecting the reliability of coaxial TSV are selected as the input values of the BP neural network. The maximum relative error of whale optimization algorithm optimized back propagation (WOA-BP) neural network model is 0.88%, which is better than the prediction results of the traditional BP and genetic algorithm (GA) optimized BP models. The WOA-BP neural network model was also compared with BP and GA-BP neural network models with four error metric models. It is verified that WOA-BP neural network model has the best prediction performance. The proposed model can be used to achieve improved prediction accuracy for the interface reliability of coaxial TSV under complex structural conditions since it has higher accuracy and stronger robustness. Graphical Abstract Figure The Schematic Diagram of Coaxial TSV Structure

关键词： interface reliability coaxial through silicon via whale optimization algorithm back propagation neural network finite element method

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A Temperature Control Method of Lysozyme Fermentation Based on LRWOA-LSTM-PID

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PROCESSES 2024年第5期12卷 866页

作者： Ding, Chenhua Li, Xungen Zhou, Hanlin Yu, Jianming Du, Juling Zhao, Shixiang Hangzhou Dianzi Univ Coll Elect & Informat Hangzhou 310018 Peoples R China Hangzhou Dianzi Univ Pujiang Microelect & Intelligent Mfg Res Inst Jinhua 322200 Peoples R China China Jiliang Univ Coll Econ & Management Hangzhou 310018 Peoples R China

In order to overcome the difficulty of parameter tuning caused by the large lag and time-varying nonlinearity of the tank for lysozyme fermentation, a temperature control method based on LRWOA-LSTM-PID is proposed in this paper. Firstly, according to the intrinsic mechanism of the fermenter, a temperature mechanism model based on a dynamic equation is designed, which can better reflect the temperature changes in the fermenter. Secondly, a Proportional Integral Derivative (PID) parameter tuning method based on a Long-Short Term Memory Network (LSTM) is proposed, which takes advantage of the ability of LSTM to learn time sequence information and obtains the variation trend between error sequences under continuous time sampling, thereby adjusting network weights more reasonably and accelerating PID parameter tuning. Finally, a whale optimization algorithm (WOA) based on the L & eacute;vy flight and random walk strategy (LRWOA) is proposed for the initialization of LSTM parameters;this algorithm has excellent optimization capabilities and overcomes the problem of LSTM falling into local optimal solutions prematurely during parameter randomization. The results show that the method proposed in this paper can achieve rapid tuning of PID parameters, thereby improving the convergence speed of the system and reducing system overshoot.

关键词： PID controller fermentation process mechanism model whale optimization algorithm L & eacute vy flight strategy random walk strategy

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A conformable fractional-order grey Bernoulli model with optimized parameters and its application in forecasting Chongqing's energy consumption

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartB期255卷

作者： Wu, Wenqing Ma, Xin Zeng, Bo Zhang, Yuanyuan Civil Aviat Flight Univ China Sch Sci Guanghan 618307 Peoples R China Southwest Univ Sci & Technol Sch Math & Phys Mianyang 621010 Peoples R China Chongqing Technol & Business Univ Coll Business Planning Chongqing 400067 Peoples R China

Chongqing is the only province and city with net energy input in southwest China, and its energy security has been in a tight balance for a long time. To propose scientific and reasonable energy policies for Chongqing under the "dual carbon"goal, this paper studies Chongqing's total energy consumption, coal consumption, natural gas consumption, and oil consumption by a conformable fractional -order grey Bernoulli model with optimized parameters. The new model is studied with its linearized form, the conformable fractional -order accumulation operation, the grey modelling technique, the least squares method, and the whale optimization algorithm. According to historical data, several forecasting models are built. From calculation results, the mean absolute validation percentage errors of the new model are smaller than those of other models in the total energy consumption, coal consumption and natural gas consumption, with values of 1.31%, 1.43%, and 2.41%, respectively. In the oil consumption, the MAPE valid is 3.85%, which is the second smallest among these models. The results illustrate that the new model can provide competitive results. Further, the values in the next five years are calculated.

关键词： Grey Bernoulli model Optimized parameters Linearized form whale optimization algorithm Energy consumption of Chongqing

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A hybrid model for VNF deployment capable of responding to online requests at network edge

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COMPUTER NETWORKS 2024年 247卷

作者： Zahedi, Seyed Reza Jamali, Shahram Islamic Azad Univ Dept Comp Engn Khalkhal Branch Khalkal Iran Univ Mohaghegh Ardabili Dept Comp Engn Ardebil Iran

Network function virtualization (NFV) is a new network architecture that replaces dedicated hardware appliances with software instances and run them via software virtualization on general-purpose servers at edge clouds. As edge clouds are resource-constrained, optimal placement of virtual network functions (VNFs) is a challenging task. This problem has proven to be NP-hard, and thus, metaheuristic algorithms have been widely used to solve it. However, these techniques suffer from high computational complexity due to their iterative, time-consuming process and cannot be performed in real-time for online requests. This paper proposes a hybrid model based on heuristic algorithms and the whale optimization algorithm (WOA) to optimally assign VNFs to physical servers at edge clouds. In this method, a multi-criteria heuristic model is used for online VNF deployment, while WOA is performed offline to tune the hyperparameters of the heuristic model. Simulation results demonstrate the efficiency of the proposed method against existing techniques in terms of power consumption and acceptance rate.

关键词： Virtual network functions Edge computing Placement Real-time requests whale optimization algorithm

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Optimal Location and Compensation Using D-STATCOM: A Hybrid Hunting algorithm

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JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS 2021年第4期32卷 1002-1023页

作者： Tejaswini, V. Susitra, D. Sathyabama Inst Sci & Technol Elect Engn Chennai 600119 Tamil Nadu India

This paper intends to propose a power quality design model for the distributed system through nonlinear functions, and hence the prerequisite of power quality enhancements can be precisely quantified. As the model is adaptable, it needs a robust optimization algorithm for estimating the optimal location and compensation of the D-STATCOM. Hence, this paper develops a hybrid meta-heuristic optimization algorithm based on the prey targeting behavior of whales. The proposed hybrid whale optimization, whale with Grey Wolf optimization (WG), is used for determining the optimal placing and sizing of D-STATCOM by solving the power quality model. The solutions will be reactive power-encoded with two bound constraints to address both the localizing and sizing problems. Besides, along with the renowned literature, we determine the Mean Voltage Stability Index. The updating algorithm of the whale optimization will be hybridized with the hunting behavior of grey wolves so that the location and sizing of D-STATCOM can be estimated precisely. The proposed WG algorithm compares its performance over other conventional methods such as GA, ABC, PSO, GWO, and WOA in terms of convergence analysis, cost analysis, and total loss and determines the effectiveness of the proposed power quality model.

关键词： Power quality model D-STATCOM whale optimization algorithm GWO whale with grey wolf optimization

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Improved Spotted Hyena Optimizer Fused with Multiple Strategies 40th

Improved Spotted Hyena Optimizer Fused with Multiple Strateg...

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40th National Conference of Theoretical Computer Science (NCTCS)

作者： Mo, Chunhui Wang, Xiaofeng Zhang, Lin North Minzu Univ Coll Comp Sci & Engn Yinchuan 750021 Ningxia Peoples R China North Minzu Univ Ningxia Key Lab Informat & Big Data Proc Yinchuan 750021 Ningxia Peoples R China

ISBN: (纸本)9789811981517;9789811981524

Aiming at the shortcomings of spotted hyena optimizer, such as slow convergence speed, low searching accuracy and easy to fall into local optimal, this paper proposes an improved spotted hyena optimization algorithm (OWSHO) integrating multiple strategies. The reverse population is constructed by using the Opposition-Based Learning strategy to increase the diversity of the population and further improve the convergence speed of the algorithm. At the same time, the spiral exploration mechanism of whale optimization algorithm is combined to enhance the ability of exploring unknown regions and improve the global search performance of the algorithm. Then adaptive weight strategy is introduced to balance and improve the global exploration and local development ability of the algorithm. In this paper, 8 benchmark functions of CEC test set are used for simulation experiments, and compared with 3 heuristic algorithms, the test results show that: The improved spotted hyena optimization algorithm based on the combination of reverse learning, spiral exploration mechanism and adaptive weight strategy has a great improvement in the search accuracy and convergence speed, and gets rid of the local optimal to a certain extent, which proves the effectiveness and advancement of the algorithm.

关键词： Spotted Hyena Optimizer whale optimization algorithm Opposition-based learning Spiral exploration Adaptive weight

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Thermal error prediction of electric spindle based on improved whale algorithm optimized neural network

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2025年

作者： Guo, Zhongfeng Liu, Siyi Yan, Mingyin Yang, Junlin Liu, Qi Li, Dongyuan Shenyang Univ Technol Sch Mech Engn 111 ShenliaoWest Rd Shenyang 110870 Peoples R China Shenyang Univ Technol Liaoning Prov Key Lab Complex Surface NC Mfg Techn 111 Shenliao West Rd Shenyang 110870 Peoples R China

To further enhance the accuracy of thermal error prediction for motorized spindles and establish a compensation model with high predictive precision and strong robustness, this paper proposes an improved whale optimization algorithm (WOA) based on a Cubic chaotic map and adaptive weight adjustment, termed CAWOA. The proposed algorithm is used to optimize a neural network, thereby developing a thermal error prediction model. The temperature field of the motorized spindle was simulated using ANSYS software, identifying the front and rear bearing groups and the motor rotor as the primary heat sources. Experiments were conducted on a VMC0656e five-axis machining center at an ambient temperature of 28 degrees C, where thermal growth and thermal error data were measured at various spindle speeds. A random forest (RF) and recursive feature elimination with cross-validation (RFECV) were employed to select the key temperature measurement points of the spindle, with the filtered temperature rise data used as inputs and the thermal error data as outputs. This data was then used to establish a thermal error prediction model based on a CAWOA optimized BP neural network (CAWOA-BP). The performance of the proposed model was compared against other prediction models, and the results demonstrate that the proposed thermal error compensation model significantly outperforms others in terms of predictive accuracy. This method provides a novel approach for thermal error modeling in motorized spindles.

关键词： CNC machine tools Heat error prediction Electric spindle whale optimization algorithm Neural network

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Static EIS multi-frequency feature points combined with WOA-BP neural network for Li-ion battery SOH estimation

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MEASUREMENT 2025年 253卷

作者： Gao, Zitong Jin, Yuhong Zhang, Yuan Zhang, Ziheng Li, Siquan Liu, Jingbing Wang, Hao Beijing Univ Technol Coll Mat Sci & Engn Key Lab Adv Funct Mat Minist Educ Beijing 100124 Peoples R China State Grid Chongqing Elect Power Co Chongqing Elect Power Res Inst Chongqing 401120 Peoples R China

Electrochemical impedance spectroscopy (EIS) has been established as an essential and non-destructive technique for estimating the state of health (SOH) of lithium-ion batteries (LIBs). However, the extraction of effective and straightforward health indicators (HIs) from either original or derived EIS data is critical for practical applications in SOH estimation. In this study, we explore static EIS multi-frequency feature points as HI based on Nyquist plots at a various SOH for commercially available batteries. Subsequently, the extracted HIs are fed into a whale optimization algorithm back propagation (WOA-BP) neural network to achieve the accurate battery SOH estimation. The developed model is validated using four battery samples cycled at the same condition, which can achieve a low root-mean-square error range from 0.23 % to 0.43 %. Notably, even when employing the untrained data with our model, it can achieve a commendable root-mean-square error during practical validation. Moreover, our WOA-BP model exhibits superior accuracy compared to other mainstream algorithms and shows the significant application potential even without historic EIS data. The results indicate that selecting characteristic frequency points can greatly reduce testing time compared to utilizing full impedance spectroscopy as HI, presenting an effective strategy for battery SOH estimation in real-world applications.

关键词： Lithium ion batteries State of health Electrochemical impedance spectroscopy whale optimization algorithm Multi-frequency feature points

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