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Integrating Analytical Simulations With Regression Learning: Advancing Efficiency in Energy and Water Use in Sugar Production

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JOURNAL OF FOOD PROCESS ENGINEERING 2025年第2期48卷

作者： Azizi, Mohammad Mosharaf-Dehkordi, Mehdi Fouladi, Nourbaksh Kazanci, Caner Univ Isfahan Fac Engn Dept Mech Engn Esfahan Iran Univ Georgia Coll Engn Athens GA USA

Energy and water consumption are critically important in the sugar industry. In this context, the heat exchanger network of a target sugar factory has been modeled and optimized, as this sector is the primary consumer of energy and water. A key innovation of this work lies in the coupling of interacting components within the model, leading to a more comprehensive framework compared to previous models in the literature. Some sections of the system are modeled using analytical interpretations, while others are developed through a regression learning process utilizing statistical data. This integration of analytical formulation and data-driven modeling represents another significant advancement in this research. The resulting model demonstrates acceptable accuracy for most measurable parameters, with an average deviation of approximately 4%. The optimization results indicate that certain parameters, such as the cooling pool evaporation rate, exhibit considerable flexibility, allowing optimization algorithms to converge more easily. Conversely, other parameters, such as the vapor fed to the exchangers, are more rigid, which restricts the freedom of the optimization process. Moreover, the effectiveness of the elements within the optimization target function is crucial for identifying the optimal point. Overall, minimizing energy consumption and water usage simultaneously presents a significant challenge, necessitating careful consideration in determining which optimal point is most practical.

关键词： heat exchanger network multiple evaporation optimization algorithm regression learner

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Air quality index prediction through TimeGAN data recovery and PSO-optimized VMD-deep learning framework

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APPLIED SOFT COMPUTING 2025年 170卷

作者： Wang, Kenan Yang, Tianning Kong, Shanshan Li, Mingduo North China Univ Sci & Technol Coll Sci Tangshan 063210 Peoples R China North China Univ Sci & Technol Coll Elect Engn Tangshan 063210 Peoples R China

With the rapid development of the economy, air pollution has become increasingly severe. Accurate prediction of the Air Quality Index (AQI) is crucial for safeguarding public health and the environment. However, AQI time series exhibit strong randomness and volatility, posing challenges for traditional forecasting methods to achieve precise AQI predictions. Therefore, we propose a new AQI hybrid prediction model, TG-Hybrid model, which integrates generative artificial intelligence, signal decomposition techniques, artificial intelligence methods, and optimization algorithms. In the proposed model, missing values in the data are handled using generative adversarial networks, effectively addressing the issue of a large number of missing values in time series data. Autoregressive integrated moving average is employed to forecast the linear components of the data, while variational mode decomposition decomposes AQI into multiple modes. Particle swarm optimization is used to combine the prediction results of convolutional neural network combined with bidirectional long short-term memory and extreme gradient boosting. Additionally, AQI prediction experiments were conducted using air pollution data from Tangshan and Beijing, and compared with fifteen other models. The results indicate that the root mean square error for Tangshan and Beijing are 6.407 and 7.485, respectively, significantly outperforming other baseline models.

关键词： Time Generative Adversarial Network Air Quality Index Prediction Deep learning model optimization algorithm Variational Mode Decomposition

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Nonlinear Trim and Stability Analysis for a Wingtip-Chained Aircraft

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2025年第5期48卷 1187-1195页

作者： He, Jiamin Liu, Haojie Huang, Rui Zhao, Yonghui Nanjing Univ Aeronaut & Astronaut Sch Aeronaut & Astronaut State Key Lab Mech & Control Aerosp Struct Coll Aerosp Engn Nanjing 210016 Peoples R China

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Performance investigation and power prediction of a trifunctional heat pump system

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APPLIED THERMAL ENGINEERING 2025年 266卷

作者： Han, Youhua Zhang, Jili Ma, Liangdong Lu, Shixiang Lv, Hui Gong, Jian Nanchang Hangkong Univ Sch Civil Engn & Transportat Nanchang 330063 Jiangxi Peoples R China Dalian Univ Technol Dept Civil Engn Dalian 116024 Liaoning Peoples R China Zhongyuan Univ Technol Sch Energy & Environm Zhengzhou 450007 Peoples R China

In this work, a trifunctional direct-expansion photovoltaic thermal heat pump system was constructed to provide domestic hot water, photovoltaic power, and chilled water. Experiments and power prediction models of the trifunctional direct-expansion photovoltaic thermal heat pump system were conducted. First, the photovoltaic, heating, and cooling performances were investigated to evaluate the comprehensive performance of the system with a vapor injection cycle. The experimental results revealed that the average electrical power and photoelectric conversion efficiency of the photovoltaic thermal array were 1.01 kW and 14.71 %, respectively. The average heating power, coefficient of performance, equivalent coefficient of performance, cooling power, and energy efficiency ratio of the system were 7.46 kW, 3.64, 6.87, 4.07 kW, and 1.94, respectively. The electrical and heating performance of the system was sensitive to solar irradiation, and the cooling performance was sensitive to the ambient temperature and wind speed. Afterwards, based on the experimental data, a back- propagation neural network model combined with particle swarm optimization, a genetic algorithm, and a time correlation series were proposed to forecast the tri-generation of the direct-expansion photovoltaic thermal heat pump system. The prediction results show that the proposed neural network prediction model has high prediction accuracy and robustness. The normalized root mean square error and mean absolute percentage error of the model were 2.00 % and 2.23 %, respectively, for electrical power prediction;1.03 % and 1.28 %, respectively, for heating power prediction;and 3.34 % and 4.29 %, respectively, for cooling power prediction.

关键词： Photovoltaic thermal heat pump system Tri-generation performance Experimental study Artificial neural network optimization algorithm Prediction accuracy

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Multi-Agent Grouping and Routing with Cooperative Tasks

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2025年第3期22卷 163-176页

作者： Choi, Euihyeon Ahn, Jaemyung Korea Adv Inst Sci & Technol KAIST Dept Aerosp Engn 291 Daehak Ro Daejeon 34141 South Korea

This paper introduces a grouping and routing problem of multiple agents for cooperative missions. The introduced problem, referred to as the Vehicle Grouping and Routing Problem with Profits, aims to maximize the total reward obtained by conducting a multi-agent mission (e.g., cooperative reconnaissance) while reducing its makespan by appropriately grouping the agents and determining their routes under operational constraints (e.g., fuel, endurance). A mixed-integer linear programming formulation and a conservative column generation-based solution procedure for the problem are proposed. A case study with homogeneous and heterogeneous agents and numerical experiments involving a cooperative reconnaissance mission with multiple unmanned aerial vehicles demonstrate the validity of the proposed formulation and solution procedure.

关键词： Unmanned Aerial Vehicle Mixed Integer Linear Programming optimization algorithm Flight Dynamics Autonomous Technology Planetary Science and Exploration Aerospace Engineering

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Hierarchical Spatiotemporal Tree Search Method for Planning Multi-Asteroid Successive Rendezvous

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2025年第5期48卷 1138-1147页

作者： Zhang, Jia-cheng Zhu, Yue-he Luo, Ya-zhong Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha 410073 Peoples R China Natl Univ Def Technol Hunan Key Lab Intelligent Planning & Simulat Aeros Changsha 410073 Peoples R China

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Techno-economic optimization of standalone hybrid power systems in context of intelligent computational multi-objective algorithms

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ENERGY REPORTS 2022年 8卷 11661-11674页

作者： Mohammed, Yekini Suberu Adetokun, Bukola Babatunde Oghorada, Oghenewvogaga Oshiga, Omotayo Nile Univ Nigeria Fac Engn Dept Elect & Elect Engn Abuja Nigeria Fed Polytech Nasarawa Sch Engn Technol Dept Elect Elect Engn Technol Nasarawa Nasarawa State Nigeria

The use of renewable energy (RE) for meeting some load power demand in the present global developmental dealings is realistically unavoidable. However, many challenges conspicuously stand the way of RE penetration into the global power sector. Some of the perceived problems require aggressive research attentions. The utilization of single RE energy structure for the supply of electricity in off-grid isolated communities is usually not a technically dependable system with to regards reliability, security and stability. The core challenge is usually connected to some spontaneous variable weather conditions. It is based on this perspective that the implementation of integrated hybrid RE becomes a promising solution for mitigation of RE intermittent behaviors. In this study, an autonomous hybrid energy system was examined based on simulations for optimal sizing configurations of solar photovoltaic (PV), wind turbine (WT), diesel generator (DG) and battery storage (BS) system. Modern intelligent optimization algorithms of Ant Colony optimization (ACO), Flower Pollination algorithm (FPA), Genetic algorithm (GA) and Particle Swarm optimization (PSO) were applied for providing solutions to the set of selected focal technoeconomic objectives in the framework of this study. Compare with others, FPA provided better results in terms of the net present cost (NPC), cost of energy (COE) and deficit power supply probability (DPSP). The proposed hybrid power systems are configured in four different scenarios: PV/BS, PV/DG/BS, PV/WT/BS and PV/WT/DG/BS. It was consequently established that the configuration of PV/DG/BS with NPC of $85112.08, COE of 0.145 $/kWh and zero DPSP gave the best overall technoeconomic results through the FPA optimization technique.(c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND

关键词： Battery storage Cost of energy Electricity Hybrid system optimization algorithm Renewable energy

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Interpolant Method in Analyzing the Laser-Induced Plasma Spectroscopy

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JOURNAL OF THERMOPHYSICS AND HEAT TRANSFER 2025年第1期39卷 150-158页

作者： Wu, Jieer Zhou, Yu Luo, Wenchen Song, W. G. Han, Xiao Jiangsu Univ Sci & Technol Sch Sci Zhenjiang 212003 Jiangsu Peoples R China Cent South Univ Sch Phys Changsha 410083 Hunan Peoples R China Natl Univ Def Technol Div Basic Researches Changsha 210039 Jiangsu Peoples R China Queen Mary Univ Sch Math Sci London E1 4NS England

A numerical algorithm that effectively solves the Abel integral equation in laser-induced plasma spectroscopy is introduced. The proposed algorithm utilizes a barycentric interpolation and a collocation method with second-kind Chebyshev nodes. This approach reduces the Abel integral equation to a linear system of equations, resulting in efficient approximate solutions. Analytical studies provide an error estimation for the proposed method, which further validates its effectiveness. Through numerical experiments, we demonstrate the ease of implementation and showcase the efficiency of the proposed method. Our results indicate that the proposed algorithm can provide valuable insight for researchers in the field of laser-induced plasma spectroscopy and beyond.

关键词： Spectroscopy Numerical Simulation optimization algorithm Thermodynamic Properties Plasma Environment Derivative Free Method Numerical Interpolation Kinetic Energy Propellant Rocket Propulsion System

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Development of a Performance-Based Approach for Optimizing Sensor Placement in Building Fire Detection

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FIRE TECHNOLOGY 2025年 1-28页

作者： Fang, Hongqiang Xu, Mingjun Chen, Xiao Lo, S. M. Wan, Man Pun Univ Sci & Technol China State Key Lab Fire Sci Hefei 230026 Peoples R China City Univ Hong Kong Dept Architecture & Civil Engn Hong Kong Peoples R China Nanyang Technol Univ Sch Mech & Aerosp Engn 50 Nanyang Ave Singapore 639798 Singapore

Fire detection systems play a vital role in ensuring effective fire protection within buildings. At present, the placement of fire detectors is guided by established codes and standards, which specify maximum coverage areas for each detector. Building engineers typically follow these guidelines, positioning detectors strategically to achieve full coverage. While this approach provides adequate protection, it fails to consider the impact of varying environmental factors in different settings and accurately assess the actual performance of fire detection systems. This limitation is particularly evident in unique spaces like warehouses, where fire types and potential ignition locations may differ significantly from those in conventional environments, necessitating a more customized approach to sensor placement. To address this issue, a fire detection performance-based sensor placement optimization (FDPB-SPO) approach is proposed. This methodology integrates numerical datasets generated from multiple simulated fire scenarios with advanced optimization algorithms to evaluate fire sensor placement performance and identify the optimal arrangement. The optimization process balances effective fire detection with compliance to code requirements, ensuring both enhanced performance and practical applicability. A case study evaluating this proposed approach demonstrates its effectiveness in determining the more appropriate arrangement for fire detection. Additionally, integrating it with the Genetic algorithm (GA) yields an optimized solution that enhances fire detection performance and reliability. These findings highlight the potential of the FDPB-SPO approach in advancing sensor placement strategies and contributing to the development of future fire detection system standards.

关键词： Building fire safety Fire detection Sensor placement Performance-based design optimization algorithm

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The Impact of Energy Storage System Control Parameters on Frequency Regulation Strategies

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IEEE ACCESS 2025年 13卷 34010-34022页

作者： Chang, Chia-Ming Chang, Hong-Chan Hung, Chun-Chun Kuo, Cheng-Chien Natl Taiwan Univ Sci & Technol Dept Elect Engn Taipei 10607 Taiwan

The large-scale development of battery energy storage systems (BESS) has enhanced grid flexibility in power systems. From the perspective of power system planners, it is essential to consider the reliability of BESS to ensure stable grid operation amid a high reliance on renewable energy. Therefore, this paper investigates BESS models and dynamic parameters used in planning future grids from the viewpoint of power planners. By measuring output response data from BESS units of three companies, dynamic responses are converted into WECC second generic model parameters using optimization algorithms, allowing their use in power system analysis software. Subsequently, using Taiwan's actual power system as the simulation background, N-1 simulations are conducted to explore the impact and benefits of BESS parameters when implementing frequency regulation strategies under two different BESS capacity specifications: 2 MW and 10 MW. In the 2 MW scenario, a comparison of the parameters from the three BESS units under frequency regulation strategies shows slight differences in the rise times of their output responses. However, for a 2 MW capacity, the frequency nadir in the power system remains consistently at 58.692 Hz. The simulation results based on the parameters defined in this research demonstrate the importance of using accurately measured and solved parameters in simulations. In the 10 MW scenario, the BESS capacity can offset the generator trip capacity, thereby revealing differences in response time between different parameters, with the frequency nadirs for the solved parameters being 59.755 Hz, 59.773 Hz, and 59.762 Hz, respectively.

关键词： Power system dynamics Frequency control Renewable energy sources Reactive power control Power control Voltage control Reactive power Power systems Integrated circuit modeling Software Battery energy storage systems (BESS) dynamic parameters frequency nadir frequency regulation optimization algorithm power conversion system (PCS) rate of change of frequency (RoCoF)

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