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The evaluation of pressure swing thermal coupling technology based on intelligent optimization algorithms in the ethylbenzene/styrene separation process

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SEPARATION AND PURIFICATION TECHNOLOGY 2025年 360卷

作者： Fu, Hao Yang, Xin Liu, Yifeng Chen, Zhouyu Ran, Jinye Zhai, Chi Zhang, Hao Chongqing Univ Technol Sch Chem & Chem Engn Chongqing 400054 Peoples R China Kyoto Univ Fac Engn Kyoto 6068317 Japan Southwest Univ Sch Chem & Chem Engn Chongqing 400715 Peoples R China Kunming Univ Sci & Technol Fac Chem Engn Kunming 650500 Yunnan Peoples R China

Due to the close-boiling system of ethylbenzene (EB) and styrene monomer (SM), conventional distillation columns (CDiC) in industry exhibit high energy consumption and substantial steam usage, posing challenges to achieve optimal economic benefits. In this paper, the application of pressure swing thermal coupling technology (PSTC) to the EB/SM separation process is presented, optimizing the CDiC, PSTC, and previously demonstrated self-heat recuperation technology (SHRT) using particle swarm optimization (PSO) and artificial gorilla troops optimizer (GTO), followed by comparative analysis. From an economic perspective, the longer the payback period (PBP) enhance the economic benefits of PSTC and SHRT. Specifically, with a 3-year PBP, the total annual cost (TAC) for PSTC and SHRT decreased by 18.74 % and 19.08 %, respectively, compared to the optimal CDiC. At a 5-year PBP, the TAC reductions for PSTC and SHRT are 36.1% and 35.3%, respectively. Additionally, prior studies have primarily focused on the economic and technical aspects of the EB/SM separation process, neglecting comprehensive assessments of environmental factors and exergy. This paper addresses this gap by establishing a multi-criteria optimization and compromise solution evaluation model (AHP-CV-VIKOR) based on analytical hierarchical processes, coefficient of variation and ViseKriterijumska Optimizacija I Kompromisno Resenje, facilitating a holistic evaluation of the entire EB/SM separation process. Ultimately, considering longterm global factors, the top-ranked EB/SM separation process identified through this comprehensive evaluation is the thermally coupled distillation technique enhanced by PSTC technology.

关键词： Pressure swing thermal coupling technology Multi-criteria optimization and compromise solution evaluation model EB/SM separation process intelligent optimization algorithm Conventional distillation columns

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Test Scenario Design and optimization of Automated Driving Lane Keeping System Based On PCA and intelligent algorithm

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Procedia Computer Science 2025年 261卷 237-246页

作者： Linfeng Hao Robert Morris University Moon Township PA 15108 US

With the rapid development of autonomous driving technology, lane keeping system (LKS) has become an important function to improve vehicle driving safety. It effectively reduces the risk of accidents caused by lane departure by ensuring stable running of vehicles in the lane. Therefore, before large-scale application, it is particularly important to ensure its safety and reliability in complex environments. Aiming at the shortcomings of existing test methods in efficiency and scene quality, this paper proposes a test scene design and optimization method based on principal component analysis (PCA) and intelligent optimization algorithm to improve the comprehensiveness and scientificity of the test. Through the analysis of LKS system function characteristics, automatic classification and operation domain, the test requirements are defined, four types of typical test scenarios are designed, and hierarchical mathematical models are established. Based on real accident data, the key variables of discrete scene elements were extracted by PCA method to enhance scene authenticity. Aiming at the elements of the continuous scene, an intelligent optimization model based on safety boundary was constructed, which was solved by combining genetic algorithm (GA) and particle swarm optimization algorithm (PSO), and finally formed a key test scenario set covering 14 lane keeping, 34 front vehicle stationary, 34 front vehicle braking and 17 neighboring vehicles entering the scene. Through the hardware-in-the-loop test platform to verify the effectiveness of the scenario, the experimental results show that the proposed method significantly improves the test efficiency and scene quality, and provides a strong support for the full-scale LKS test and large-scale application of autonomous driving technology.

关键词： Automated Driving Test Lane Keeping System optimization Test Scenario Modeling Principal Component Analysis (PCA) intelligent optimization algorithm

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Comparison and discussion of intelligent optimization algorithms based on magnetic localization systems for improving the initial value defects of the L-M algorithm

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Procedia Computer Science 2023年 226卷 22-28页

作者： Qi Zhang Li Zhu Han Xu School of Electronic Engineering and Automation Guilin University of Electronic Technology Guilin 541004 China

Magnetic localization techniques are used in in vivo therapy, drug delivery, and surgical localization widely due to their ability to locate in an unobstructed environment precisely. The Levenberg-Marquardt (L-M) algorithm is one of the most common magnetic localization algorithms due to its fast optimization speed and high precision, but it requires high initial values such that improper initial values can easily lead to local optimization. In this paper, the performance of ten commonly used intelligent optimization algorithms is compared based on the mathematical model of the permanent magnet. The final position of the magnet is obtained by a fusion algorithm that uses the result of the intelligent optimization algorithm as the initial value of L-M. The real-time localization accuracy of this fusion algorithm is verified on a hardware platform and experiments demonstrate that the integration of the TSA algorithm and L-M algorithm fusion yields higher performance of the magnetic positioning system.

关键词： Magnetic localization system intelligent optimization algorithm Levenberg-Marquardt algorithm

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intelligent optimization algorithms for Disruptive Anti-covering Location Problem 19th

Intelligent Optimization Algorithms for Disruptive Anti-cove...

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19th International Conference on Distributed Computing and intelligent Technology (ICDCIT)

作者： Chappidi, Edukondalu Singh, Alok Mallipeddi, Rammohan Univ Hyderabad Sch Comp & Informat Sci Hyderabad 500046 Telangana India Kyungpook Natl Univ Sch Elect Engn Dept Artificial Intelligence Daegu 41566 South Korea

ISBN: (纸本)9783031248474;9783031248481

Given a set of potential sites for locating facilities, the disruptive anti-covering location problem (DACLP) seeks to find the minimum number of facilities that can be located on these sites in such a way that each pair of facilities are separated by a distance which is more than R from one another and no more facilities can be added. DACLP is closely related with anti-covering location problem (ACLP), which is concerned with finding the maximum number of facilities that can be located such that all the facilities are separated by a distance which is more than R from each other. The disruptive anti-covering location problem is so named because it prevents the "best or maximal" packing solution of the anti-covering location problem from occurring. DACLP is an NP-hard problem and plays an important role in solving many real world problems including but not limited to forest management, locating bank branches, nuclear power plants, franchise stores and military defence units. In contrast to ACLP, DACLP is introduced only recently and is a relatively under-studied problem. In this paper, two intelligent optimization approaches namely genetic algorithm (GA) and discrete differential evolution (DDE) are proposed to solve the DACLP. These approaches are the first heuristic approaches for this problem. We have tested the proposed approaches on a total of 80 DACLP instances containing a maximum of 1577 potential sites. The effectiveness of the proposed approaches can be observed from the results on these instances.

关键词： Disruptive anti-covering location problem Facility location Genetic algorithm Discrete differential evolution intelligent optimization algorithm

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Landslide Displacement Prediction Model Based on Optimal Decomposition and Deep Attention Mechanism

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IEEE ACCESS 2025年 13卷 51573-51588页

作者： Ren, Shuai Ghazali, Kamarul Hawari Ji, Yuanfa Khan, Samra Urooj Univ Malaysia Pahang Al Sultan Abdullah Fac Elect & Elect Engn Technol Pekan 26600 Pahang Malaysia Guilin Univ Elect Technol Informat & Commun Sch Guilin 541004 Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Precis Nav Technol & Applicat Guilin 541004 Peoples R China

Landslide displacement forecasting is crucial for disaster prevention and risk management, as it enables timely warnings and effective mitigation strategies. However, the highly nonlinear and complex nature of landslide displacement poses significant challenges for accurate prediction. To address this, this study proposes an advanced forecasting framework integrating the Chebyshev Levy Flight-Sparrow Search algorithm (CLF-SSA) with Variational Mode Decomposition (VMD) to enhance decomposition accuracy and optimize parameter selection. The trend component is modeled using the Autoregressive Integrated Moving Average (ARIMA) with a grid search strategy, while the periodic component is predicted using a Bidirectional Long Short-Term Memory network with an Attention mechanism (BiLSTM-Attention), which dynamically adjusts the contribution of influencing factors. Grey Relational Analysis (GRA) is further employed to identify key external driving factors, enhancing prediction accuracy. Experimental results demonstrate that the proposed model significantly improves predictive performance, reducing the Root Mean Square Error (RMSE) by 60% compared to the traditional XGBoost model and by 33% compared to the Empirical Mode Decomposition-BiLSTM (EMD-BiLSTM) model. Moreover, the Mean Absolute Scaled Error (MASE) analysis confirms the robustness of the model in capturing both short-term fluctuations and long-term trends. Given its superior predictive accuracy and practical applicability, this approach provides valuable technical support for landslide monitoring and early warning systems.

关键词： Terrain factors Predictive models Accuracy optimization Market research Prediction algorithms Robustness Disasters Attention mechanisms Computational efficiency Landslide displacement prediction intelligent optimization algorithm variational mode decomposition bidirectional long short term memory network attention mechanism

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Machine Learning-Finite Element Mesh optimization-Based Modeling and Prediction of Excavation-Induced Shield Tunnel Ground Settlement

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INTERNATIONAL JOURNAL OF COMPUTATIONAL METHODS 2025年第4期22卷

作者： Hu, Da Hu, Yongjia Hu, Rong Tan, Ze Ni, Pengpeng Chen, Yu Xiang, Xuejuan Li, Yongsuo Liu, Jing Hunan City Univ Hunan Engn Res Ctr Struct Safety & Disaster Preven Yiyang 413000 Peoples R China Hunan City Univ Sch Civil Engn Yiyang 413000 Peoples R China Sun Yat Sen Univ Sch Civil Engn Guangzhou 510275 Peoples R China State Key Lab Tunnel Engn Guangzhou 510275 Peoples R China Southern Marine Sci & Engn Guangdong Lab Zhuhai Zhuhai 519082 Peoples R China

Ground settlement prediction for shield construction is highly important and challenging. This study introduces a machine learning algorithm combined with finite element numerical simulation, i.e., machine learning-finite element mesh optimization. For surface subsidence prediction, 16 combination models of ANN, KNN, RF and SVR were optimized by PSO, GA, BT and BO, involving raw data preprocessing, principal component analysis, hyperparameter selection and prediction accuracy evaluation. A subway shield tunneling project was analyzed, in which the meshes of finite element numerical models were discretized into different sizes from 1.0m to 2.0m. In total, 360 sets of data points were extracted from the simulation results, including stress, strain, shield jacking force, internal friction angle, cohesion force, and settlement, of which 252 data points were used as the input parameters of machine learning model. Analysis of average error rate of finite element-machine learning coupling models showed that the finite element model had the highest accuracy of settlement prediction when the mesh size of the finite element model was 1.4m, and the GA-SVR model had the highest accuracy and generalization ability in ground settlement prediction. This study highlights the uniqueness of machine learning-finite element mesh optimization model in application.

关键词： Settlement prediction finite element simulation intelligent optimization algorithm mesh optimization finite element-machine learning coupling model

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An Active Control Method for a Lower Limb Rehabilitation Robot with Human Motion Intention Recognition

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SENSORS 2025年第3期25卷 713-713页

作者： Song, Zhuangqun Zhao, Peng Wu, Xueji Yang, Rong Gao, Xueshan Beibu Gulf Univ Coll Mech & Marine Engn Qinzhou 535011 Peoples R China Beijing Inst Technol Sch Mechatron Engn Beijing 100081 Peoples R China Minist Civil Affairs Key Lab Intelligent Control & Rehabil Technol Beijing 100176 Peoples R China

This study presents a method for the active control of a follow-up lower extremity exoskeleton rehabilitation robot (LEERR) based on human motion intention recognition. Initially, to effectively support body weight and compensate for the vertical movement of the human center of mass, a vision-driven follow-and-track control strategy is proposed. Subsequently, an algorithm for recognizing human motion intentions based on machine learning is proposed for human-robot collaboration tasks. A muscle-machine interface is constructed using a bi-directional long short-term memory (BiLSTM) network, which decodes multichannel surface electromyography (sEMG) signals into flexion and extension angles of the hip and knee joints in the sagittal plane. The hyperparameters of the BiLSTM network are optimized using the quantum-behaved particle swarm optimization (QPSO) algorithm, resulting in a QPSO-BiLSTM hybrid model that enables continuous real-time estimation of human motion intentions. Further, to address the uncertain nonlinear dynamics of the wearer-exoskeleton robot system, a dual radial basis function neural network adaptive sliding mode Controller (DRBFNNASMC) is designed to generate control torques, thereby enabling the precise tracking of motion trajectories generated by the muscle-machine interface. Experimental results indicate that the follow-up-assisted frame can accurately track human motion trajectories. The QPSO-BiLSTM network outperforms traditional BiLSTM and PSO-BiLSTM networks in predicting continuous lower limb motion, while the DRBFNNASMC controller demonstrates superior gait tracking performance compared to the fuzzy compensated adaptive sliding mode control (FCASMC) algorithm and the traditional proportional-integral-derivative (PID) control algorithm.

关键词： human motion intention recognition follow-up lower extremity exoskeleton rehabilitation robot machine learning algorithm intelligent optimization algorithm dual radial basis function neural network adaptive sliding mode controller

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A novel nonlinear prestack inversion method based on nutcracker optimization algorithm with high convergence speed

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JOURNAL OF APPLIED GEOPHYSICS 2025年 233卷

作者： Zhou, Lin Ming, Ouyang Liao, Jianping Li, Jingye Xia, Hanlin Ding, Haiyang Hunan Univ Sci & Technol Sch Earth Sci & Spatial Informat Engn Xiangtan 411201 Peoples R China Chongqing Univ Sci & Technol Chongqing Inst Unconvent Oil & Gas Chongqing 401331 Peoples R China China Univ Petr Coll Geophys Beijing 102249 Peoples R China Petrochina Qinghai Oilfield Co Explorat & Dev Res Inst Dunhuang 736202 Peoples R China

Predicting reservoir parameters with high accuracy is still a crucial work of oil reservoir exploration and development. Due to the limitation of computational efficiency, deterministic methods are primarily used in practical production applications for predicting reservoir parameters. When the nonlinear forward equations are exceptionally complex and the initial model constructed deviates significantly from the true reservoir parameters, deterministic methods may have difficulty obtaining reasonable predictions of reservoir parameters. Compared to deterministic methods, intelligent optimization methods based on nature-inspired metaheuristic algorithms have unique advantages because they do not require derivative information, can achieve global optimization, and have less reliance on initial model. Therefore, they perform better in solving complex nonlinear optimization problems. In this paper, a new intelligent optimization algorithm called Nutcracker optimization algorithm (NOA) with a high convergence speed is introduced. By utilizing this optimization algorithm to solve the nonlinear inversion problem constructed by the highly nonlinear exact Zoeppritz equations, we analyze the potential of nonlinear reservoir parameters prediction methods based on intelligent optimization algorithms in practical production applications. The synthetic data test shows that, compared to the classical quantum particle swarm optimization (QPSO) algorithm and the highly-cited whale optimization algorithm (WOA), the prestack nonlinear inversion method based on NOA proposed in this paper ensures high convergence accuracy and exhibits high computational efficiency. It significantly reduces computation time and holds great potential for practical production applications. The field data test shows that the proposed method can rapidly and accurately estimates reservoir parameters, validating the feasibility and effectiveness of the proposed method. This has important theoretical value and

关键词： Exact Zoeppritz equations intelligent optimization algorithm Nutcracker optimization algorithm (NOA) Nonlinear inversion

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Exponential-based dynamic objective function for coordination of overcurrent relays in power networks

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 267卷

作者： Ghotbi-Maleki, Mahdi Mohammadi, Reza Mirjalili, Seyedali Shahid Beheshti Univ Dept Elect Engn Tehran Iran Torrens Univ Ctr Artificial Intelligence Res & Optimizat Adelaide Australia Obuda Univ Univ Res & Innovat Ctr Budapest Hungary

Coordination of overcurrent relays is one of the most challenging issues in the field of power systems operation. Since this issue is a complex and highly-constrained optimization problem, thus, the definition of appropriate objective function to consider the protection constraints in the form of penalty function is one of the challenges of coordination problem. Until now, the objective functions contains the sum of operation times of relays (main component) and the penalty function formed from the coordination constraints;however, it is suggested in this paper that the penalty function should be considered exponentially for increasing the pressure on it compared to the conventional one. Also, the new objective function should be based on the multiplication operator. In this case, the main component is considered as the coefficient for the penalty function, and the pressure on the penalty function will increase because these two components have opposite behavior towards each other. It is concluded that, by using the proposed dynamic objective function in different conditions regarding different power networks, the new objective function improves the performance of metaheuristic algorithms to penetrate to lower layers of search space and achieve better results than conventional static objective functions.

关键词： Coordination of overcurrent relays Distance relay Dynamic objective function intelligent optimization algorithm Overcurrent relay Power system

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Time Difference Localization algorithm Based on Improved Eel and Grouper Optimizer

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2025年第5期44卷 3223-3257页

作者： Zhang, Xinrong Zhang, Bojun Fang, Liping Huaiyin Inst Technol Fac Automat Huaian 223003 Peoples R China

As is well known, Time Difference of Arrival (TDOA) is a technique used for locating the position of a signal source based on the time difference of when the signal arrives at multiple receivers. The iterative method enhances the function through repeated iterations, which involve both deterministic iterations with initial values and stochastic optimizations with various algorithms. However, conventional optimization techniques often encounter issues such as slow convergence rates and becoming trapped in local optima. Stochastic optimization algorithms are an effective approach for solving TDOA problems. This research introduces and enhances the latest Eel and Grouper optimization (EGO) algorithms to address such issues. First, the initial population is randomized using primarily Bernoulli chaos sequences. Next, the exploration phase is enhanced by replacing the random search agents with a Logistic-Sine-Cosine Map chaos improvement, enabling more effective position updates and improving algorithm accuracy. Simulation results demonstrate that the enhanced EGO algorithm possesses strong exploration and convergence capabilities. The performance improvement is statistically significant, showcasing robust exploration abilities and efficient convergence speeds. The algorithm can quickly and reliably converge to the source location, providing high positioning accuracy.

关键词： Indoor localization Time difference of arrival intelligent optimization algorithm Eel and grouper optimization algorithm

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