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Waste heat management of gas turbine-tailored power plants with reverse osmosis (RO) technology and hydrogen fuel production;sensitivity analysis and Techno-economic optimization using genetic algorithm methods

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APPLIED THERMAL ENGINEERING 2025年 266卷

作者： Chen, Rui Qian, Haifeng Alhomayani, Fahad M. Almadhor, Ahmad Elmasry, Yasser Abdullaeva, Barno Sayfutdinovna Mahariq, Ibrahim Balla, Hyder H. Abed Abood, Ahmed Sabeeh Abed Kareem, Zaid S. Huanghe Jiaotong Univ Sch Mech & Elect Engn Jiaozuo 454000 Henan Peoples R China Guangxi Sci & Technol Normal Univ Laibin 546100 Guangxi Peoples R China Taif Univ Coll Comp & Informat Technol Taif Saudi Arabia Taif Univ Appl Coll Taif Saudi Arabia Jouf Univ Coll Comp & Informat Sci Dept Comp Engn & Networks Jouf Saudi Arabia King Khalid Univ Coll Sci Dept Math POB 9004 Abha 61413 Saudi Arabia Tashkent State Pedag Univ Vice Rector Sci Affairs Tashkent Uzbekistan Gulf Univ Sci & Technol GUST Engn & Appl Innovat Res Ctr GEAR Mishref Kuwait Saveetha Univ Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Math Chennai 602105 Tamil Nadu India China Med Univ China Med Univ Hosp Dept Med Res Taichung Taiwan Al Furat Al Awsat Tech Univ Najaf Tech Inst Aeronaut Tech Dept Kufa Kufa Iraq Univ Al Ameed Karbala Iraq Univ Kufa Fac Engn Mech Engn Dept Kufa Iraq Korea Univ Coll Engn Seoul 02481 South Korea

This study presents an innovative heat recovery methodology to enhance traditional combined power plants' technical and economic performance. The proposed system uses reverse osmosis (RO) for water treatment, lowtemperature gases for industrial cooling through an absorption refrigeration cycle (ARC), and proton exchange membrane (PEM) electrolysis for hydrogen generation. A comprehensive parametric investigation and a multicriteria optimization process employing artificial neural networks (ANNs) and genetic algorithms (GAs) are conducted. Under optimal conditions, this system can generate 67.5 MW of electrical power, 197.6 kg/h of hydrogen, 682 kg/s of fresh water, and 20.02 MW of cooling capacity. The operational cost is estimated at 6043 $/h, resulting in a levelized cost of electricity (LCOE) of 6.77 cents/kWh. The breakdown of costs shows that the RO unit accounts for 39.9 % of the total, with power generation units at 32.7 % and by-product modules at 27.4 %. Enhancing airflow leads to improved work output and cooling capacity without affecting exergy and cost metrics. Besides, increasing the compression ratio boosts fuel usage and overall system performance. Conversely, a higher middle temperature reduces outputs and exergy efficiency but lowers the total cost rate. The optimization results indicate a total cost rate reduction of 5171.5 $/h with an exergy efficiency of 36.94 %, and the use of ANNs has significantly cut optimization time from 94 h to just 9 min. Future research should focus on further optimizing system parameters and exploring integration with other renewable energy sources to boost sustainability and efficiency.

关键词： Thermal energy engineering Gas turbine Thermo-economic assessment Fresh water production genetic algorithm

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A genetic algorithm for vehicle routing problems with time windows based on cluster of geographic positions and time windows

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APPLIED SOFT COMPUTING 2025年 169卷

作者： Liu, Jiani Tong, Lei Xia, Xuewen Wuhan Business Univ Sch Mech & Elect Engn Wuhan 430056 Peoples R China Wuhan Business Univ Hubei SME Math Intellectualizat Innovat Dev Res Ct Wuhan 430056 Peoples R China Minnan Normal Univ Coll Phys & Informat Engn Zhangzhou 363000 Peoples R China Minnan Normal Univ Key Lab Intelligent Optimizat & Informat Proc Zhangzhou 363000 Peoples R China

The vehicle routing problem with time windows (VRPTW) has attracted many scholars' attention because it plays an important role in distribution and logistics. Many studies show that (meta-)heuristics are practical approaches for VRPTW. However, how to efficiently utilizing characteristics of customers' time windows and geographic distributions is neglected in last few years. Thus, this paper proposes an improved genetic algorithm (GA) for VRPTW based on a c lustering method and the l ongest c ommon s ubstring (LCS) of elite and inferior individuals. In the proposed algorithm, called CLCS-GA, cluster information of customers' geographic distributions and time windows is utilized to initialize three subpopulations with distinct properties. Moreover, during the evolutionary process, the LCS of elite and inferior individuals is utilized in the crossover and mutation operators to speedup the convergence and help individuals jump out of local optima. When performing the local search, which is a crucial operator for optimizing VRPTW, relatedness measured by customers' geographic distribution and time windows are considered, aiming to overcome the blindness of the common local search. Comprehensive properties of CLCS-GA are extensively testified by 56 VRPTW instances, in which seven state-of-art algorithms are adopted as peer algorithms. Moreover, distinct characteristics of the proposed strategies are also analyzed based on a set of experiments.

关键词： VRPTW genetic algorithm Cluster LCS Local search

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Modelling and analysis of a fractional-order epidemic model incorporating genetic algorithm-based optimization

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JOURNAL OF APPLIED MATHEMATICS AND COMPUTING 2025年第1期71卷 901-925页

作者： Adak, Sayani Barman, Snehasis Jana, Soovoojeet Majee, Suvankar Kar, T. K. Indian Inst Engn Sci & Technol Dept Math Howrah 711103 W Bengal India Ramsaday Coll Dept Math Howrah 711401 W Bengal India

Infectious diseases have always been a threat to the smooth running of our daily activities. To regulate the disease's devastating outcome, we have performed a qualitative study of infectious disease using an SIR model. While formulating the model, we have taken into account the saturated incidence function with three controls, namely, treatment control, vaccination control, and media awareness. To make the model more robust, we have updated the model using Caputo fractional-order differential equation. We have determined the existence and uniqueness of the solution along with all possible equilibrium points. We have also obtained the basic reproduction number and the criteria of asymptotic local and global stability, taking the basic reproduction number as the threshold parameter. Finally, to control the disease, we have performed the optimization using a metaheuristic search and optimization technique, genetic algorithm (GA).

关键词： Saturated incidence function Global stability Sensitivity analysis genetic algorithm

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Triple-Layer genetic algorithm (3LGA) for Project Scheduling and Material Ordering Problem with Limited Storage Space

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BUILDINGS 2025年第7期15卷 1040-1040页

作者： Malaikrisanachalee, Suphawut Wongwai, Narongrit Promchun, Pongpan Kasetsart Univ Fac Engn Dept Civil Engn Bangkok 10900 Thailand Kasetsart Univ Fac Engn Sriracha Dept Civil Engn Sriracha Campus Bangkok 20230 Thailand Kasetsart Univ Fac Engn Dept Civil Engn Infrastructure Engn & Management Bangkok 10900 Thailand

Efficient project management in the construction industry depends on optimizing project scheduling and material ordering, two interdependent processes that significantly impact both costs and durations. Traditional approaches often address these processes separately, neglecting the trade-offs between scheduling costs and material procurement costs. Furthermore, existing studies frequently overlook real-world constraints, such as limited storage space at construction sites. To bridge these gaps, this paper investigates the project scheduling and material ordering problem with limited storage space (PSMOP-LSS) and introduces an integrated model that simultaneously optimizes storage space allocation, activity scheduling, and material ordering. A novel ordering strategy with time period (OSTP) is employed to enhance material procurement under storage constraints. To solve this NP-hard problem, a triple-layer genetic algorithm (3LGA) is proposed, comprising three layers: space allocation, project scheduling, and material ordering. Computational experiments conducted on a case study demonstrate the effectiveness of the 3LGA, achieving significant reductions in project costs and durations compared to conventional ordering strategies. The results highlight trade-offs between cost and duration, offering actionable insights for project managers. This research provides a robust decision-making framework for balancing inventory costs, ordering costs, and project durations in space-constrained environments. Managerial implications include optimizing ordering strategies based on storage capacity and cost parameters.

关键词： space allocation project scheduling material ordering storage space genetic algorithm

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A new simulation - optimization framework based on multi-layer Green-Ampt infiltration model and genetic algorithm for developing planning guidelines of low impact development measures at river basin scales

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JOURNAL OF HYDROLOGY 2025年 658卷

作者： Subrahmanian, Sreethu Sankarbalaji, Arun Rajasekaran Bhallamudi, S. Murty Narasimhan, Balaji Indian Inst Technol Madras Chennai 600036 Tamil Nadu India

The effectiveness of Low Impact Development (LID) measures depends on the geology, topography, climate, and land use patterns in a watershed. Therefore, the planning, design, and implementation of LIDs must be performed optimally by accounting for all the relevant characteristics of the watershed through adequate physical processbased modeling. This study integrates the genetic algorithm (GA) with a physics-based process modeling of LIDs using the Multi-Layer Green-Ampt infiltration (MLGA) method as a new attempt for determining optimal LID measures at river basin scales. The study also proposes a methodology for LID planning at large spatial scales that involves clustering sub-basins into hydrologically similar groups based on their physical characteristics. The results from the simulation-optimization framework indicated that the LID implementation percentages within hydrologically similar sub-basin groups fall in identical ranges. Thus, the optimal combination of LID measures obtained for a sub-basin could be applied over other hydrologically similar sub-basins in the same group, reducing the computational expenses required for river basin scale analyses and implementation planning. The impact of optimal LID combinations was demonstrated in the study for the river basins of Chennai in India for a 10-year design storm, and a maximum flood volume reduction of 38-82% and a peak reduction of 37-75% were obtained in the impervious areas of the basin. This study provides insight into the efficiency of LID measures at the river basin scale, which is essential for planning and implementing a sustainable urban drainage system.

关键词： Multi-layer Green-Ampt infiltration method genetic algorithm K -means clustering Optimization of Low Impact Development measures

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Acoustic emission source location in complex structures based on artificial potential field-guided rapidly-exploring random tree* and genetic algorithm

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2025年 224卷

作者： Nie, Jia-Hao Li, Dan Wang, Hao Xiang, Shu-Lin Yu, Tao Mao, Jian-Xiao Southeast Univ Key Lab C&PC Struct Minist Educ Nanjing 211189 Peoples R China Southeast Univ Sch Civil Engn Nanjing 211189 Peoples R China Hefei Gen Machinery Res Inst Hefei 230031 Peoples R China

Towards more accurate and easy-to-implement damage detection in large-scale complex structures, a novel acoustic emission (AE) source location method is developed based on artificial potential field-guided rapidly-exploring random tree* (APF-RRT*) and genetic algorithm (GA). APF-RRT*, which combines the excellent obstacle avoidance ability of RRT* with the path planning efficiency of APF, is introduced to adaptively estimate the shortest distances from the damage source to AE sensors. The shortest distances are obtained as the actual propagation distances of waves and then embedded into the modified error function, where GA is employed as an optimization scheme to evaluate the source location via iterations. Through the experiment on a full-scale high-strength bolt joint plate with a series of bolt holes, the effectiveness and superiority of the proposed method were validated. It achieved a better source location performance with lower mean absolute error and standard deviation than the time-of-arrival (TOA) method, delta-T mapping method, and machine learning-improved methods based on Gaussian process (GP) and artificial neural network (ANN), respectively. The primary contributions of the proposed method lay in abandoning the straight-wave-propagation assumption of the traditional TOA method by adaptively taking into account the geometric obstacles in complex structures, and removing the need for a large amount of training data and burdensome pencil lead break (PLB) tests required by data-driven location methods.

关键词： Acoustic emission Damage location Complex structures Artificial potential field Rapidly-exploring random tree* genetic algorithm

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Fast Braking of Segmented Electro-Pneumatic Braking System by Using Variable-Universe Fuzzy-PID Controller Optimized by genetic algorithm

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第2期74卷 2610-2619页

作者： Chen, Lan Zhou, Zhi Peng Wan, Zhi Chong Wan, Guo Chun Tong, Mei Song Shanghai Inst Technol Sch Elect & Elect Engn Shanghai 201418 Peoples R China Tongji Univ Dept Elect Sci & Technol Shanghai 201804 Peoples R China

In order to solve the safety issues such as broken hooks and derailment caused by the longitudinal impact of long trains, this paper proposes a functional variable universe fuzzy (FVUF) PID (FVUF-PID) controller incorporated with a genetic algorithm (GA) for the segmented electro-pneumatic (SEP) braking system of heavy-haul trains. To study the performance of the SEP braking system with the controller, the simulation model of the SEP braking system with a high fidelity to the static standard bench of 150-marshalling freight train is established based on the principle of Model-120 distribution valve, and extensive simulations for the SEP braking performance with the controller are carried out. The controller can realize an online adaptive tuning for the PID parameters by applying the GA to optimize the scaling factors and control rules of variable universe fuzzy inference, resulting in a great enhancement of control performance. The simulation and experiment results show that, compared with traditional pneumatic braking and SEP braking with FVUF-PID controller optimized by Particle Swarm Optimization (PSO), the time difference of starting rise of the braking cylinder between the first and last car of a 150-marshalling freight train can be reduced by 78% and 15.3%, respectively. Also, the braking time and distance of the SEP braking system with the proposed controller are shorter by 20.9% and 23.03%, and 3.13% and 5.63%, respectively, compared with those of the traditional pneumatic braking system and SEP braking with FVUF-PID controller optimized by PSO at a pressure reduction of 140kPa. Furthermore, the maximum compressional forces of the coupler during traditional pneumatic braking are reduced by 18.11-62.83% under different decompression pressures, demonstrating a significant improvement on the SEP braking performance by using the controller.

关键词： genetic algorithms Mathematical models Automobiles Finite element analysis Atmospheric modeling Transportation Standards Electric-pneumatic brake functional variable universe fuzzy PID controller genetic algorithm heavy-haul train pneumatic brake

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An Optimal Radiation Shield Design of a Small Modular Nuclear Power Reactor by genetic algorithm

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NUCLEAR TECHNOLOGY 2025年第5期211卷 940-952页

作者： Bagheri, S. Khalafi, H. Bahrami, A. A. Nucl Sci & Technol Res Inst NSTRI Radiat Applicat Res Sch Tehran Iran

One of the critical concerns in developing small modular nuclear reactors (SMRs) is employing efficient radiation shielding consistent with the design and implementation requirements. Besides observing the radiation shielding requirements, the design process entails developing compact, lightweight shielding as well as ensuring the safety of the staff and radiation-sensitive equipment around the reactor under different operating conditions. Conventional methods of radiation shielding design are based on experiments conducted. Therefore, the resultant design involves trial and error, rarely achieving an optimal, effective, and efficient *** this study, a multi-objective method based on the genetic algorithm coupled with the MNCP calculation code has been used to enhance the radiation shielding system of a SMR. Using this method, the thickness of different shielding layers has been optimized to minimize the total dose (neutrons and gamma) at the output, the weight, and the overall volume of the shielding. To assess and compare the method used to an implemented conventional method, a sample design of the MRX nuclear power reactor is considered. Using the optimization technique, the radiation shielding of the reactor has been redesigned and compared with the original *** paper discusses the existing deficiencies in the initial design of the primary shield of the reactor as a design implemented based on conventional methods. Similar to the original design, the main materials considered for radiation shielding in consecutive layers were water and steel, and the final layer contained lead. The thickness of the different layers and their arrangements have been optimized to assess the upgraded method. The calculations indicate that the overall thickness of the proposed shielding is suggested as 93.8 cm compared to 140 cm to obtain the total dose of neutrons and gammas, which is suggested as less than 10 mu Sv/h. The results imply a 38.56% reduction in volume and

关键词： Redesigning radiation shielding genetic algorithm small modular nuclear power reactor multi-objective optimization compact radiation shielding

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Eliminating Network Depth: genetic algorithm for Parameter Optimization in CNNs

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IEEE ACCESS 2025年 13卷 22473-22488页

作者： Askari, M. Soleimani, S. Shakoor, M. H. Momeni, M. Arak Univ Fac Engn Dept Comp Engn Arak *** Iran

Recent advances in Convolutional Neural Networks (CNNs) have significantly enhanced image classification performance. However, CNNs often require large numbers of parameters, leading to increased computational complexity, prolonged training times, and substantial resource demands. Achieving higher classification accuracy typically involves deepening network architectures, which further exacerbates these challenges. This paper proposes a novel method based on a genetic algorithm to optimize parameter selection, enabling the construction of CNNs that achieve superior accuracy with fewer parameters. By focusing on parameters with the most significant impact on performance, the method reduces the need for deeper networks, thereby minimizing computational costs. Experimental results demonstrate that the proposed algorithm outperforms its counterparts. For instance, the generated CNN achieves an accuracy improvement of 0.75 percentage points over ResNet-110 while using 84% fewer parameters. These findings highlight the method's potential to balance efficiency and accuracy, making it a promising solution for resource-constrained applications.

关键词： Convolutional neural networks Computer architecture Feature extraction Accuracy Training Filters genetic algorithms Optimization Transfer learning Neurons Convolutional neural network genetic algorithm effective weights image classification

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Integrated Modeling and Optimal Operation Strategy of Building Cooling System Combining the Standardized Thermal Resistance and genetic algorithm

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ENERGY SCIENCE & ENGINEERING 2025年第1期13卷 392-404页

作者： Tian, Liang Lai, Bohong Yang, Tianzhen Wang, Xingce Hao, Junhong Liu, Kaicheng North China Elect Power Univ Minist Educ Sch Energy Power & Mech Engn Key Lab Power Stn Energy Transfer Convers Beijing Peoples R China State Grid Zhejiang Elect Power Res Inst Hangzhou Zhejiang Peoples R China China Elect Power Res Inst Beijing Peoples R China

Integrated modeling and operation optimization of building energy systems is significant for improving the energy utilization efficiency and reducing carbon emission. This paper introduces the standardized thermal resistance to construct an overall heat current model of the building cooling system with coupled heat transfer, mass transfer, and energy conversion processes. Based on the heat current model, we derive the holistic thermal energy transfer and conversion constraints on the system level and reduce the intermediate parameters of the system model. Moreover, the genetic algorithm is introduced to optimize the system operation conditions under the given system structure parameters. The optimization results provide the optimal mass flow distribution of cooling water, return water, and ambient air and meanwhile show that the compressor power consumption can reach 76.5% of the total system power consumption. The change of user behavior by raising the room temperature to 4 degrees C can reduce the total system power consumption by 20%. The results are in line with the theoretical reality and prove the feasibility and effectiveness of the method proposed in this paper, which provides a practical reference for the energy-saving operation of the building cooling system.

关键词： building cooling system genetic algorithm optimal operation standardized thermal resistance system integrated modeling

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