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Research on the production and characteristics of carbon materials with high CO2 adsorption performance: Based on machine learning and dung beetle optimizer methods

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CARBON 2025年 239卷

作者： Yin, Qianqian Wang, Moyi Zhu, Xiaoxun Gao, Xiaoxia Wang, Ruikun Zhao, Zhenghui North China Elect Power Univ Dept Energy Power & Mech Engn Baoding 071003 Hebei Peoples R China North China Elect Power Univ Hebei Key Lab Low Carbon & High Efficiency Power G Baoding 071003 Hebei Peoples R China North China Elect Power Univ Baoding Key Lab Low Carbon & High Efficiency Power Baoding 071003 Hebei Peoples R China

Excessive CO2 emissions have caused global climate change, leading to significant environmental impacts. Porous carbon materials are promising for CO2 adsorption due to their high capacity and selectivity. Machine learning (ML) is a valuable tool in the search for ideal carbon materials for CO2 adsorption. This study employs various machine learning (ML) methods to predict CO2 adsorption capacity and examine the influence of carbon material characteristics. Results reveal that the pore structure, particularly micropore volume, significantly impacts CO2 adsorption more than elemental composition. Subsequently, the impact of biomass feedstock properties and experimental conditions on the CO2 adsorption capacity of the resulting carbon materials were explored. The content of hydrogen (H) in biomass feedstocks, the ratio of activating agents, and the activation temperature are the three most significant factors affecting the micropore volume of the carbon materials, which then influences the CO2 adsorption performance. The optimal experimental conditions for the preparation of carbon materials with high CO2 adsorption performance were determined by combining the dung beetle optimizer(DBO) algorithm with machine learning methods. The integration of these approaches can effectively guide the synthesis of carbon materials for CO2 adsorption.

关键词： CO2 adsorption Carbon materials Machine learning Feature importance dung beetle optimizer algorithm

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An Enhanced algorithm Combining P&O with dung beetle optimizer for Photovoltaic Systems under Partial Shading Conditions 3

An Enhanced Algorithm Combining P&O with Dung Beetle Optimiz...

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3rd International Conference on New Energy and Power Engineering, ICNEPE 2023

作者： Wang, Hao Li, Lin Ye, Haoshen Zhao, Weiwei Wuhan University of Technology School of Mechanical and Electronic Engineering Wuhan430070 China

ISBN: (纸本)9798350393224

Partial shading conditions (PSCs) induce a multi-peak state in the P-V characteristics curve, posing challenges to the precise tracking of the maximum power point (MPP) in photovoltaic (PV) systems. This paper introduces a maximum power point tracking (MPPT) algorithm named Enhanced dung beetle optimizer combined with Perturb and Observe (EDBO&PO). The proposed technique strategically integrates the DBO algorithm in the initial phase and the P&O algorithm in the subsequent stage, providing an effective solution for tracking the Global Maximum Power Point under PSCs. The proposed approach involves the DBO algorithm determining the range where the MPP is situated, followed by the rapid locking onto the MPP facilitated by the P&O algorithm. The collaborative integration of the DBO's global search ability with the P&O's local search capability guarantees efficient convergence and rapid tracking. In diverse test scenarios, the introduced EDBO&PO algorithm showcases its proficiency in GMPP tracking, achieving an average efficiency of 99.97% and an average tracking time of 0.5 seconds. © 2023 IEEE.

关键词： dung beetle optimizer algorithm Maximum power point tracking Partial shading conditions

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State of Charge Prediction for Electric Loader Battery Based on Extreme Learning Machine

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IEEE ACCESS 2025年 13卷 3696-3706页

作者： Ding, Wei Zou, Fumin Liu, Jishun Zhang, Cheng Fujian Univ Technol Sch Elect Elect Engn & Phys Fuzhou 350118 Peoples R China Fujian Prov Key Lab Automot Elect & Elect Drive Te Fuzhou 350118 Peoples R China Fujian Univ Technol Ningde New Energy Technol Res Inst Fuzhou 352101 Fujian Peoples R China

In order to accurately predict the state of charge (SOC) of electric loader battery, a multi-strategy improved dung beetle optimization algorithm (MIDBO) is proposed to optimize the SOC prediction method of the extreme learning machine (ELM). Firstly, principal component analysis (PCA) is used to screen the input features and reduce dimensionality. Secondly, logistic chaos mapping is introduced in the population initialization stage to improve the diversity and quality of the population. The dynamic spiral search strategy is utilized in the breeding stage of the dung beetle algorithm, and the L & eacute;vy flight mechanism is incorporated in the foraging stage to escape local optima. Finally, adaptive t-distribution variation and a dynamic selection strategy are employed to update the position of the dung beetle, enhancing the convergence speed of the algorithm. This approach enables accurate prediction of the electric loader battery SOC. Experiments using real vehicle data from electric loaders show that the accuracy of the MIDBO-ELM model reaches 99.985%, with an error of less than 5 parts per thousand. The robustness of the model is verified by adding different types of noise to simulate measurement errors. Additionally, the accuracy of the MIDBO algorithm is validated through test functions, and the generalization capability of the algorithm for SOC prediction is confirmed using a public data set.

关键词： Prediction algorithms Heuristic algorithms Optimization Adaptation models Standards Predictive models Load modeling Lithium batteries Estimation Electric loader state of charge principal component analysis dung beetle optimizer algorithm extreme learning machine extreme learning machine

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Multi-Objective Optimization for Turning Process of 304 Stainless Steel Based on dung beetle optimizer-Back Propagation Neural Network and Improved Particle Swarm Optimization

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JOURNAL OF MATERIALS ENGINEERING AND PERFORMANCE 2024年第8期33卷 3787-3800页

作者： Xue, Huan Li, Tao Li, Jie Zhang, Yansong Huang, Shiyao Li, Yongchun Yang, Chongwen Zhang, Wenqian Hubei Univ Technol Sch Mech Engn Hubei Key Lab Modern Mfg Qual Engn Wuhan 430068 Peoples R China Hubei Guoan Special Steel Inspect & Testing Co Huangshi 435000 Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430076 Peoples R China

Austenite stainless steel of type 304 is one of the most difficult materials to process. During the machining process, parts easily generate higher surface residual stress in cutting direction (CD) and production costs. To solve the problem, a multi-objective optimization method that combined dung beetle optimizer-Back Propagation Neural Network (DBO-BPNN) and Improved Particle Swarm Optimization (IPSO) algorithm was adopted. Firstly, BPNN, Genetic algorithm-Back Propagation Neural Network (GA-BPNN) and DBO-BPNN were established to map the nonlinear relationship between surface residual stress in CD and turning parameters. Secondly, a dataset of surface residual stress in CD and material removal rate under different turning parameters was obtained through finite element method (FEM) turning simulation. The dataset was applied into neural network to establish a nonlinear mapping relationship between turning parameters and surface residual stress in CD. The turning parameters are used as variables, the surface residual stress in CD and material removal rate are applied as objective functions. The optimal Pareto solution set of the surface residual stress in CD and material removal rate was acquired by combining DBO-BPNN and IPSO. Finally, turning experiments were designed to verify the accuracy of the turning simulation. The study shows that the surface residual stress in CD decreased by 38.47%, and the material removal rate increased by 91.69%.

关键词： 304 stainless steel dung beetle optimizer algorithm improved particle swarm optimization algorithm multi-objective optimization residual stress turning process

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A Sound Source Location Method Based on Time Difference of Arrival with Improved dung beetle optimizer

A Sound Source Location Method Based on Time Difference of A...

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2024 International Conference on Machine Learning and Intelligent Computing, MLIC 2024

作者： Song, Chunning Zhang, Jindong School of Electrical Engineering Guangxi University Nanning530004 China

In microphone array sound source localization based on Time Difference of Arrival (TDOA), traditional methods for solving the nonlinear equations of TDOA lead to significant deviations and lower accuracy. To address this issue, this paper proposes a TDOA-based sound source localization method using an Improved dung beetle optimizer (IDBO) algorithm. This method enhances the performance of the dung beetle optimizer (DBO) by employing strategies such as chaotic mapping, golden sine, and adaptive t-distribution, and applies it to sound source localization. To evaluate the performance of the IDBO, it is compared with DBO, Harris Hawk optimizer (HHO), Gray Wolf optimizer (GWO), Bald Eagle Search (BES) algorithm, and Whale Optimization algorithm (WOA). The results showed that in solving benchmark functions and localization models, it demonstrates faster convergence speed, higher localization accuracy, and better stability. © 2024 C. Song & J. Zhang.

关键词： Adaptive t-distribution dung beetle optimizer algorithm Golden sine Microphone array Sound source localization Time difference of arrival

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Short-Term Electricity Load Forecasting Based on Improved Data Decomposition and Hybrid Deep-Learning Models

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APPLIED SCIENCES-BASEL 2024年第14期14卷 5966页

作者： Chen, Jiayu Liu, Lisang Guo, Kaiqi Liu, Shurui He, Dongwei Fujian Univ Technol Sch Elect Elect Engn & Phys Fuzhou 350118 Peoples R China Fujian Univ Technol Natl Demonstrat Ctr Expt Elect Informat & Elect Te Fuzhou 350118 Peoples R China

Short-term power load forecasting plays a key role in daily scheduling and ensuring stable power system operation. The problem of the volatility of the power load sequence and poor prediction accuracy is addressed. In this study, a learning model integrating intelligent optimization algorithms is proposed, which combines an ensemble-learning model based on long short-term memory (LSTM), variational modal decomposition (VMD) and the multi-strategy optimization dung beetle algorithm (MODBO). The aim is to address the shortcomings of the dung beetle optimizer algorithm (DBO) in power load forecasting, such as its time-consuming nature, low accuracy, and ease of falling into local optimum. In this paper, firstly, the dung beetle algorithm is initialized using a lens-imaging reverse-learning strategy to avoid premature convergence of the algorithm. Second, a spiral search strategy is used to update the dynamic positions of the breeding dung beetles to balance the local and global search capabilities. Then, the positions of the foraging dung beetles are updated using an optimal value bootstrapping strategy to avoid falling into a local optimum. Finally, the dynamic-weighting coefficients are used to update the position of the stealing dung beetle to improve the global search ability and convergence of the algorithm. The proposed new algorithm is named MVMO-LSTM. Compared to traditional intelligent algorithms, the four-quarter averages of the RMSE, MAE and R2 of MVMO-LSTM are improved by 0.1147-0.7989 KW, 0.09799-0.6937 KW, and 1.00-13.05%, respectively. The experimental results show that the MVMO-LSTM proposed in this paper not only solves the shortcomings of the DBO but also enhances the stability, global optimization capability and information utilization of the model.

关键词： power load forecasting dung beetle optimizer algorithm LSTM reverse-learning strategy spiral search strategy optimal value bootstrap strategy dynamic-weighting factors

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Cooperative Jamming Resource Allocation with Joint Multi-Domain Information Using Evolutionary Reinforcement Learning

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REMOTE SENSING 2024年第11期16卷 1955页

作者： Xin, Qi Xin, Zengxian Chen, Tao Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China Shanghai Radio Equipment Res Inst Shanghai 201109 Peoples R China

Addressing the formidable challenges posed by multiple jammers jamming multiple radars, which arise from spatial discretization, many degrees of freedom, numerous model input parameters, and the complexity of constraints, along with a multi-peaked objective function, this paper proposes a cooperative jamming resource allocation method, based on evolutionary reinforcement learning, that uses joint multi-domain information. Firstly, an adversarial scenario model is established, characterizing the interaction between multiple jammers and radars based on a multi-beam jammer model and a radar detection model. Subsequently, considering real-world scenarios, this paper analyzes the constraints and objective function involved in cooperative jamming resource allocation by multiple jammers. Finally, accounting for the impact of spatial, frequency, and energy domain information on jamming resource allocation, matrices representing spatial condition constraints, jamming beam allocation, and jamming power allocation are formulated to characterize the cooperative jamming resource allocation problem. Based on this foundation, the joint allocation of the jamming beam and jamming power is optimized under the constraints of jamming resources. Through simulation experiments, it was determined that, compared to the dung beetle optimizer (DBO) algorithm and the particle swarm optimization (PSO) algorithm, the proposed evolutionary reinforcement learning algorithm based on DBO and Q-Learning (DBO-QL) offers 3.03% and 6.25% improvements in terms of jamming benefit and 26.33% and 50.26% improvements in terms of optimization success rate, respectively. In terms of algorithm response time, the proposed hybrid DBO-QL algorithm has a response time of 0.11 s, which is 97.35% and 96.57% lower than the response times of the DBO and PSO algorithms, respectively. The results show that the method proposed in this paper has good convergence, stability, and timeliness.

关键词： electronic countermeasures jamming resource allocation cooperative jamming reinforcement learning dung beetle optimizer algorithm

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