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Research on Oil Well Production Prediction Based on GRU-KAN Model Optimized by PSO

ENERGIES 2024年第21期17卷

作者： Qiu, Bo Zhang, Jian Yang, Yun Qin, Guangyuan Zhou, Zhongyi Ying, Cunrui Southwest Petr Univ Sch Comp Sci & Software Engn Chengdu 610500 Peoples R China

Accurately predicting oil well production volume is of great significance in oilfield production. To overcome the shortcomings in the current study of oil well production prediction, we propose a hybrid model (GRU-KAN) with the gated recurrent unit (GRU) and Kolmogorov-Arnold network (KAN). The GRU-KAN model utilizes GRU to extract temporal features and KAN to capture complex nonlinear relationships. First, the MissForest algorithm is employed to handle anomalous data, improving data quality. The Pearson correlation coefficient is used to select the most significant features. These selected features are used as input to the GRU-KAN model to establish the oil well production prediction model. Then, the particle swarm optimization (PSO) algorithm is used to enhance the predictive performance. Finally, the model is evaluated on the test set. The validity of the model was verified on two oil wells and the results on well F14 show that the proposed GRU-KAN model achieves a Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Coefficient of Determination (R2) values of 11.90, 9.18, 6.0% and 0.95, respectively. Compared to popular single and hybrid models, the GRU-KAN model achieves higher production-prediction accuracy and higher computational efficiency. The model can be applied to the formulation of oilfield-development plans, which is of great theoretical and practical significance to the advancement of oilfield technology levels.

关键词： oil well production prediction GRU-KAN model particle swarm optimization algorithm MissForest

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Optimal threshold selection for segmentation of Chest X-Ray images using opposition-based swarm-inspired algorithm for diagnosis of pneumonia

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第9期83卷 27089-27119页

作者： Khosla, Tejna Verma, Om Prakash Delhi Technol Univ Comp Sci Engn Dept Bawana Delhi India Delhi Technol Univ Elect & Commun Engn Dept Bawana Delhi India

Image Segmentation using thresholding is one of the most significant areas of image processing. However, the challenge lies in accurately and effectively segmenting medical images, which is a crucial step in many applications of medical image analysis. It necessitates the development of an effective and robust segmentation approach that can handle the complexity and diversity of medical images. To address this problem, we propose a novel image segmentation technique based on minimizing the cross-entropy function using a hybrid approach that combines the features of Opposition-Based Learning (OBL), Chameleon swarm algorithm (CSA), and particle swarm optimization algorithm (PSO). The opposition-based technique generates the initial population and improves convergence. Then, PSO and CSA are run in parallel on an unequal population set to improve the optimal results. The proposed approach, named the Opposition-based Chameleon swarm algorithm improved by particle swarm algorithm (CSAPSO), is evaluated on twelve Chest X-Ray (CXR) images of patients for the detection of Pneumonia. It is further tested on a large data set related to COVID-19. We conducted extensive comparisons with other state-of-the-art methods and the Deep Learning algorithms and used the performance indicators, namely Root Mean Square Error (RMSE), Peak Signal to Noise Ratio (PSNR) and Structure Similarity Index (SSIM), Classification Accuracy, Area Under Curve for evaluating the performance. The proposed approach is statically analyzed using the Friedman rank-sum test. Through the analysis, CSAPSO demonstrates better global optimal results compared to state-of-the-art techniques.

关键词： Chameleon swarm algorithm Cross entropy thresholding Medical imaging particle swarm optimization algorithm Pneumonia Segmentation

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Cloud-based estimation of lithium-ion battery life for electric vehicles using equivalent circuit model and recurrent neural network

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JOURNAL OF ENERGY STORAGE 2025年 114卷

作者： Chen, Ziqing Chen, Jianguo Zhu, Zhicheng Chen, Jian Lv, Taolin Qiao, Dongdong Zheng, Yuejiu Univ Shanghai Sci & Technol Coll Mech Engn Shanghai 200093 Peoples R China Shanghai Inst Space Power Sources State Key Lab Space Power Sources Shanghai 200245 Peoples R China

With the increasing adoption of electric vehicles, the limitations of BMS in terms of storage capacity and computational power lead to a gradual accumulation of errors in battery capacity estimation over time. This growing inaccuracy significantly compromises the effective management of on-board power battery states. The challenge of battery capacity estimation based on extensive cloud-stored data has become a key focus in current research. In this paper, we propose an enhanced method that combines the ECM with a RNN to address this issue. By incorporating the relationship between OCV and SOC into the ECM, and employing PSO for direct capacity identification, we achieve accurate estimation. Furthermore, the use of RNN dynamically adjusts the observation noise in Kalman filtering, significantly improving the precision of the estimation. Experimental results demonstrate that the proposed `method yields a RMSE of <3 % and an average relative error below 2 %, compared to traditional approaches. This study presents a high-precision, efficient solution for estimating battery capacity using cloud-based data from electric vehicles, offering substantial application value.

关键词： Electric vehicle cloud data Equivalent circuit model particle swarm optimization algorithm Recurrent neural network

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optimization algorithm-Based Fault-Tolerant Resilient Control for Helicopter System: The Finite-Time Case

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JOURNAL OF COMPUTATIONAL AND NONLINEAR DYNAMICS 2025年第2期20卷 021007页

作者： Satheesh, T. Anisha, T. M. Sakthivel, R. Elayabharath, V. T. Ren, Y. Karpagam Acad Higher Educ Dept Math Coimbatore 641021 India Bharathiar Univ Dept Appl Math Coimbatore 641046 India Sungkyunkwan Univ Dept Math Suwon 440746 South Korea Anhui Normal Univ Sch Math & Stat Wuhu 241000 Anhui Peoples R China

This paper centers on the finite-time control problem of the helicopter system in the midst of external disturbances, gain fluctuations, and actuator faults. Precisely, the finite-time boundedness and input-output finite-time stability are obtained concurrently to preclude huge undesirable values for both state and output, respectively, during specific transients. In particular, a particle swarm optimization setup is put forward for lowering the cost function and enhancing the overall reliability of the system. Furthermore, the fault-tolerant resilient control scheme is tailored to exhibit strong resistance to failures in the actuator and variations in the gain matrix. By blending the Lyapunov stability theory with the concept of finite time, we attain the essential requirements to ensure the stability of the closed-loop system over a finite span of time. Subsequently, the explicit methodology for obtaining the gain matrix is laid out pursuant to the established requirements. In particular, simulation results are presented to analyze the potential and significance of the devised control scheme.

关键词： helicopter system fault-tolerant resilient control finite-time theory particle swarm optimization algorithm

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An effective optimization algorithm for hydrogen fuel cell-based hybrid energy system: A sustainable microgrid approach

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2025年 98卷 1341-1355页

作者： Abu, Sayem M. Hannan, M. A. Rahman, S. A. Long, Chua Yaw Ker, Pin Jern Wong, Richard T. K. Jang, Gilsoo Univ Tenaga Nas Dept Mech Engn COE Kajang 43000 Malaysia Sunway Univ Sch Engn & Technol Bandar Sunway 47500 Malaysia Korea Univ Sch Elect Engn Seoul 136701 South Korea Univ Tenaga Nas Dept Elect & Elect Engn COE Kajang 43000 Malaysia

--This paper presents the optimization of the performance of hybrid energy system (HES) integrating PV systems, hydrogen fuel cells, batteries, and supercapacitors for improved energy management and reduced hydrogen consumption in microgrid applications. The optimization is achieved using a particle swarm optimization (PSO) algorithm to adjust the proportional-integral controller. Key performance metrics, such as fuel flow rate, hydrogen consumption, and system stability, are targeted. Comparative analysis shows hydrogen consumption reduced by 18.35%, fuel flow rate by 19.87%, and oxygen utilization improved by 1.04%, resulting in better fuel cell efficiency. PV power consumption decreased by 17.53%, and battery and supercapacitor performance improved by 9.71% and 32%, respectively. The PSO-optimized system also reduces grid dependence by 2.21% and enhances stability by 7.58%. These results demonstrate the critical role of the fuel cell in the HES and highlight how PSO can advance sustainable energy systems by improving microgrid efficiency and reducing grid reliance.

关键词： Hybrid energy system Hydrogen fuel cell battery Supercapacitor particle swarm optimization algorithm

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Thermodynamic performance prediction and optimization of a 1 kW ocean thermal energy cogeneration system based on artificial neural network

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ENERGY 2025年 314卷

作者： Zhang, Yuan Li, Yifan Tian, Zhen Yang, Chao Peng, Hao Kan, Ankang Gao, Wenzhong Shanghai Maritime Univ Coll Merchant Marine Shanghai 201306 Peoples R China Hainan Univ State Key Lab Marine Resource Utilizat South China Haikou 570228 Peoples R China

In this paper, a prediction model based on the artificial neural network (ANN) method was constructed, and the thermodynamic performance prediction and optimization were conducted for a system for ocean thermal energy conversion that integrates an air conditioning cycle (OTEC-AC). By employing the variable importance measure (VIM) method, ten key parameters were selected as input parameters, and three output parameters, including system power generation (Wele), cold energy efficiency (eta co), and energy efficiency (eta en) were defined. The accuracies of prediction models constructed by the back propagation neural network (BPNN) and BPNN optimized by the particle swarm optimization algorithm (BPNN-PSO) were compared. Analysis of the thermodynamic characteristics and multi-objective optimization of the system were performed, and performance differences between the experimental data-based and the BPNN-PSO model-based system were compared. Results showed that optimizing the initial weights and thresholds of the model using the PSO algorithm could enhance the model's generalization capability. Multi-objective optimal results based on prediction data were very close to those based on experimental data, with a maximum relative error of 4.4498 %. This study validates the feasibility of using the ANN model for data prediction in OTEC cogeneration systems, which could provide guidelines for the optimal design of related systems.

关键词： OTEC Air conditioning Artificial neural network particle swarm optimization algorithm Multi-objective optimization

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Research on Offshore Vessel Trajectory Prediction Based on PSO-CNN-RGRU-Attention

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APPLIED SCIENCES-BASEL 2025年第7期15卷

作者： Liu, Wei Cao, Yu Liaoning Petrochem Univ Sch Informat Sci & Control Engn Fushun 113005 Peoples R China

In busy offshore waters with high vessel density and intersecting shipping lanes, the risk of collisions and accidents is significantly increased. To address the problem of insufficient feature extraction capability of traditional recurrent neural networks (RNNs) in ship trajectory prediction in busy nearshore areas, this paper proposes a hybrid model based on particle swarm optimization (PSO), Convolutional Neural Networks (CNN), Residual Networks, Attention Mechanism, and Gated Recurrent Units (GRU), named PSO-CNN-RGRU-Attention, for ship trajectory prediction. This study utilizes real Automatic Identification System (AIS) data and applies the PSO algorithm to optimize the model and determine the optimal parameters, using a sliding window method for input and output prediction. The effectiveness and practicality of the model have been fully verified. Experimental results show that, compared to the PSO-CNN-GRU model, the proposed model improves the longitude by 7.8%, 3.4%, and 1.7% in terms of Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE), respectively, and improves the latitude by 48.3%, 62.9%, and 39.2%, respectively. This has significantly contributed to enhancing the safety of ship navigation in the Bohai Strait.

关键词： AIS information particle swarm optimization algorithm convolutional neural network attention mechanism trajectory prediction

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A hybrid deep recurrent artificial neural network with a simple exponential smoothing feedback mechanism

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INFORMATION SCIENCES 2025年 686卷

作者： Karahasan, Ozlem Bas, Eren Egrioglu, Erol Giresun Univ Fac Arts & Sci Dept Stat TR-28200 Giresun Turkiye

Both classical forecasting methods and machine learning approaches are used to solve forecasting problems. Deep artificial neural networks, one of the machine learning methods, are widely used today and give very good results. Recurrent neural networks, a type of deep neural network, are very important in forecasting problems. Simple recurrent artificial neural networks, which are the simplest deep recurrent neural networks, are often preferred in solving forecasting problems due to the small number of parameters they use. Simple exponential smoothing, one of the classical forecasting methods, attracts attention with its performance in solving forecasting problems. The motivation of the study is to create a new forecasting method by combining a classical and simple forecasting method with a deep recurrent artificial neural network in an architecture. In this, a new hybrid deep recurrent artificial neural network with a simple exponential smoothing feedback mechanism is proposed. The architecture of the proposed method is created as a combination of simple recurrent artificial neural networks and simple exponential smoothing methods. In the training of the proposed method, two training algorithms based on sine cosine optimization and particle swarm optimization algorithms are proposed. In these training algorithms, two different solution strategies such as restarting, and early stopping rule are used to avoid overfitting and local optimum problems. The performance of the proposed method is analysed using stock market datasets and compared with both different deep and shallow artificial neural networks and classical forecasting methods. As a result of the analyses, it is concluded that the proposed method is successful in one step ahead of forecasting performance.

关键词： Simple recurrent artificial neural network Exponential smoothing Sine cosine optimization algorithm particle swarm optimization algorithm Stock exchange Forecasting

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Prediction of rotary seal leakage rate under the influence of stress relaxation based on G A-PSO-BP hybrid algorithm

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ENGINEERING RESEARCH EXPRESS 2025年第1期7卷 015581-015581页

作者： Zhang, Yi He, Wei Hu, Ling Lou, Zicheng Zhang, Daqin Wang, Zheng Southwest Petr Univ Sch Mechatron Engn Xindu Ave 8 Chengdu 610500 Sichuan Peoples R China

Rotary combination seals of composite impactors are susceptible to failure and relaxation in high-pressure, high-temperature deep-well environments. The aim of this study is to analyze the effect of stress relaxation on the performance of the seals and to assess their performance under different operating conditions. The study adopts GA-PSO-BP algorithm to predict the leakage, simulates the contact pressure change of the sealing surface before and after stress relaxation by Abqus software, and calculates the leakage rate by using MATLAB software in combination with the thermoelastic flow lubrication model and the finite element analysis data, to establish the prediction model dataset. The study also optimised the neural network prediction model and verified the accuracy of the model experimentally. The results show that increasing pressure leads to an increase in contact pressure, but relaxation increases contact length and decreases maximum pressure, and increasing compression increases and then decreases contact pressure and increases the peak value, but relaxation decreases the peak value and increases the risk of leakage. The increase in speed and temperature both lead to an increase in leakage rate, and the GA-PSO-BP model predicts more accurately the rotary combination seals with a higher leakage rate after considering the stress relaxation.

关键词： rotary combination seal stress relaxation BP neural network genetic algorithm particle swarm optimization algorithm leakage

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An Optimal Rotating Machinery Fault Feature Extraction Method Based on the Variational Mode Extraction and Synthetic Detection Index

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JOURNAL OF VIBRATION ENGINEERING & TECHNOLOGIES 2025年第1期13卷 1-18页

作者： Lu, Na Jiang, Shuangyun Li, Zhongliang Cao, Chaofan Zhang, Guangtao Chen, Xudong Zhengzhou Univ Zhengzhou Peoples R China

PurposeSetting parameters for variational mode extraction (VME) has been a challenge in fault feature extraction. Previous studies have often optimized these parameters using fitness functions like entropy value or kurtosis to evaluate the VME decomposition results. However, these methods overlook the impact on subsequent fault classification. This paper aims to propose a more effective method for fault feature extraction in rotating *** fault feature extraction method based on VME and the synthetic detection index (SDI) is introduced. The VME parameters are optimized using the particle swarm optimization (PSO) algorithm, with the maximum value of SDI as the objective function. The optimized VME is then used to extract features from the original signals and build a fault feature set for fault *** proposed method was tested on two datasets, and the numerical results demonstrate its superior performance in fault classification compared to other *** results show that the proposed method, which optimizes VME parameters using PSO, enhances fault feature extraction and classification accuracy in rotating machinery, outperforming other comparative techniques.

关键词： Rotating machinery Feature extraction Fault diagnosis Variational mode extraction particle swarm optimization algorithm Synthetic detection index

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