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multi-objective optimization of horizontal louver systems with flat, single-curvature, and double-curvature profiles to enhance daylighting, glare control, and energy consumption in office buildings

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SOLAR ENERGY 2025年 285卷

作者： Kangazian, Ali Art Univ Isfahan Fac Architecture & Urbanism Esfahan Iran Islamic Azad Univ Dept Architecture Isfahan Khorasgan Branch Esfahan Iran

Identifying the most optimal slat shape significantly influences the performance of louver systems in terms of daylighting, glare control, and energy consumption. This is particularly crucial in climates with high levels of solar irradiance, where thermal gain and daylight illumination highly affect buildings and occupants. This study aims to identify the optimal slat shapes for various profile types-flat, single-curvature, and double-curvature-that simultaneously reduce annual energy consumption and enhance annual daylighting and visual comfort performance in fully glazed office buildings. This goal is achieved by employing a multi-objective optimization (MOO) algorithm, NSGA-II, applied to a control-point-based algorithm designed to generate diverse slat shapes for each profile type. The main objective functions include the maximization of spatial useful daylight illuminance (sUDI) and spatial glare autonomy (sGA), as well as the minimization of energy use intensity (EUI). The MOO process results in a diverse set of Pareto optimal slat shapes for each profile type, which are subsequently ranked by a fitness function. Findings suggest that the Pareto optimal solutions within each type significantly improve the overall performance of the space compared to the base case. Specifically, among these solutions, flat profiles with the highest fitness scores enhance daylighting levels of the space to a greater extent (9.028% to 14.583%) compared to single (-2.778% to 12.5%) and double-curvature profiles (-5.556% to 9.722%) with the highest scores. Regarding glare, double-curvature profiles with the highest fitness scores provide a more visually comfortable environment for users by improving the sGA value by 19.879% to 33.247% compared to the base case. However, those with a concave-convex shape produce excessive illumination in the perimeter zone, whereas those with a convex-concave shape present challenges in providing sufficient daylight in the rear zone of the space. Ad

关键词： Slat shape Daylight Energy Consumption Glare multi-objective optimization

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multi-objective optimization of PC-SAFT parameters for ionic liquids from density and viscosity data using entropy scaling

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FLUID PHASE EQUILIBRIA 2025年 595卷

作者： Melfi, Diego T. Scurto, Aaron M. Univ Kansas Dept Chem & Petrol Engn Lawrence KS 66045 USA Univ Kansas Ctr Environm Beneficial Catalysis Lawrence KS 66046 USA

Equations of state using the Statistical Associating Fluid Theory (SAFT EoS) have found tremendous success in the thermodynamic modeling of ionic liquids (ILs) and mixtures. Traditionally, SAFT EoS parameters are fit to pure component pressure-volume-temperature (PVT) (density) data and vapor pressure data. We have recently combined the PC-SAFT EoS with entropy scaling theory to correlate and predict the viscosity of ILs and IL mixtures. We found that the PC-SAFT EoS parameters for ionic liquids regressed to PVT data can sometimes lead to relatively large deviations in the viscosity correlations, especially at high pressure. Here, we investigate the effect of including viscosity data along with PVT data for the PC-SAFT parameter regression of two series of 1-nalkyl-3-methyl imidazolium ionic liquids ([CnMIm][Tf2N] and [CnMIm][BF4]). From analyzing the Pareto fronts, the inclusion of viscosity data to PVT data for PC-SAFT parameters resulted in only a small loss in accuracy for the density, but with much improved viscosity correlations through entropy scaling. We found that the parameter sets obtained from density and viscosity data regression are less prone to numerical pitfalls, i.e. fictitious SAFT critical points, than the parameter sets obtained from PVT data alone. In addition, the predicted (kij=0) phase equilibrium (VLE) of ionic liquids and mixtures with CO2, CH4, and water were equal to, if not better than the predictions using PVT data alone. Overall, the use of pure PVT and viscosity data in the parameterization of PCSAFT yields a more widely applicable prediction method for both thermodynamic and transport properties.

关键词： PC-SAFT Viscosity Entropy scaling Equation of state multi-objective optimization

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multi-objective optimization of cryogenic propellant zero boil-off storage: Modeling, optimization method and performance enhancement

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ENERGY 2025年 320卷

作者： Zhang, Wujie Xu, Liangze Zhang, Jiaxu Zheng, Zhaoqi Miao, Ruijiao Huang, Yonghua Shanghai Jiao Tong Univ Inst Refrigerat & Cryogen Shanghai 200240 Peoples R China

Cryocooler-based zero boil-off schemes are promising for long-term storage of cryogenic propellants. To date, no studies have addressed the fluid-thermal coupling between key components of the zero boil-off system, as well as the trade-offs among the cooling power, insulation performance and energy utilization efficiency. To predict and optimize the performance of a cryocooler-based zero-boil-off system, a systemic model integrating theoretical calculations and computational fluid dynamics was developed. Additionally, a multi-objective optimization method was designed based on a modified hyperplane generation approach and a self-adaptive crossover operator. The accuracy of the model was validated using the results of liquid nitrogen experiments from the literature. On the same computation platform, the proposed optimization method demonstrated superior capabilities in avoiding local optima and accelerating convergence compared with the original Non-dominated Sorting Genetic Algorithm-III: the temperature uniformity of the cold shield improved by at least 10.44 %, and the time cost was reduced by 32.25 %. In addition, conflicts were identified among the cooling power, temperature uniformity of the cold shield, and parasitic heat leakage, whereas the temperature uniformity showed a positive correlation with heat leakage through multilayer insulation. These findings provide guidelines for the multi-objective design of cryogenic propellant storage systems for future space missions.

关键词： Cryogenic propellants Variable density multilayer insulation Cold shield Zero boil-off storage technology multi-objective optimization

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multi-objective optimization of kitchen comfort based on arithmetic superposition index P

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CASE STUDIES IN THERMAL ENGINEERING 2025年 66卷

作者： Zhang, Shunyu Zhao, Xiangrui Cheng, Liangguo Shen, Zhihang Chen, Zhenlei Ningbo Univ Fac Maritime & Transportat Ningbo 315211 Peoples R China

For the accumulation of pollutants and high-temperature environment in the kitchen, this paper comprehensively considers kitchen pollution emissions, air supply, and thermal comfort, innovatively introduces the concepts of arithmetic superposition index P and human respiratory zone thermal sensitivity weighting, and proposes a multi-objective optimization of kitchen comfort based on the P index. This study adopts a combination of orthogonal experiments and computational fluid dynamics (CFD) simulations to study the exhaust characteristics of range hoods under different structural parameters and compares and analyzes the rationality and effectiveness of different auxiliary ventilation methods in kitchen ventilation systems to propose the optimal solution. The research findings indicate that adjusting the range hood barrier angle significantly reduces kitchen pollution, and workstation air supply markedly improves kitchen and breathing zone comfort. Optimizing range hood parameters (178 degrees angle, 1.544m height, 20.57 Pa pressure) increased PM10 capture efficiency by 25.68 % and reduced PM10 intake fraction by 72.55 %. This also decreased the breathing zone's weighted PMV by 37.17 % and increased kitchen ADPI by 27.7 %. Compared to no auxiliary air supply, workstation air supply decreased kitchen PMV by 53.08 % and breathing zone thermal weighted PMV by 96.99 %. Additionally, it lowered the thermal weighted air age in the breathing zone by 11.98 % and improved ventilation efficiency by 12.55 %, greatly enhancing kitchen comfort.

关键词： Arithmetic superposition index P Thermal sensitivity weighting multi-objective optimization Kitchen comfort Orthogonal experiments Auxiliary ventilation

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multi-objective optimization of a combined cooling, heating and power system integrated with reformed methanol high-temperature proton exchange membrane fuel cell

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2025年第1期47卷 3674-3691页

作者： Zhong, Zhaoda Gao, Xin Zhu, Jimin Zhao, Wenyu Li, Na Araya, Samuel Simon Liso, Vincenzo Aalborg Univ DOE Pontoppidanstraede 111 DK-9220 Aalborg Denmark Tech Univ Carolo Wilhelmina Braunschweig Inst Internal Combust Engines & Fuel Cells IVB Braunschweig Germany Tech Univ Denmark DTU Construct Kongens Lyngby Denmark Luxembourg Inst Sci & Technol LIST Mat Res & Technol Dev Esch Belval Belvaux Sanem Luxembourg

Reformed methanol high-temperature proton exchange membrane fuel cell (RM HT-PEMFC) systems demonstrate potential for both mobile and stationary applications. However, optimizing key variables is challenging due to the complex coupling of heat flows across various temperature levels. This study develops a combined cooling, heating and power system by integrating the RM HT-PEMFC with a double-effect LiBr-H2O absorption refrigeration cycle. The proposed system is optimized using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), targeting system exergy efficiency, specific CO2 emissions, and exergy cost per unit product. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is employed to determine the optimal values of objectives: an exergy efficiency of 43.12%, specific CO2 emissions of 0.510 kg/kWh, and exergy cost per unit product of 167.59 USD/GJ, representing improvements of 20.73%, reduction of 17.10%, and 1.07% compared to baseline. The optimized ranges for key parameters are identified as follows: stack temperature (173.94-179.91 degrees C), steam to carbon ratio (1.78-1.80), current density (0.20-0.40 A/cm2), and cathode stoichiometry (2.29-2.52).

关键词： Absorption refrigeration cycle environmental analysis HT-PEMFC multi-objective optimization NSGA-II

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multi-objective optimization in the construction of steel-concrete composite columns with carbon emission considerations: Pareto front development and decision-making

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AIN SHAMS ENGINEERING JOURNAL 2025年第2期16卷

作者： Zhan, Zhengjie Xia, Pan Xia, Dongtao Hu, Yan Hubei Univ Technol Sch Civil Engn Architecture & Environm Wuhan 430068 Peoples R China Hubei Univ Technol Engn & Technol Coll Dept Civil Engn Wuhan 430068 Peoples R China Hubei Univ Technol Key Lab Intelligent Hlth Percept & Ecol Restorat R Minist Educ Wuhan 430068 Peoples R China

This study tackles the multi-objective optimization (MOP) challenges in constructing steel-concrete columns by introducing a novel TCQE model that considers time, cost, quality, and carbon emissions. Employing relative deviation theory with dynamic weighting, the model normalizes MOP and applies an improved ant colony algorithm (IACA) to generate optimized solutions. Empirical research validates the model's efficiency and applicability. Furthermore, a decision-making framework based on AHP-TOPSIS is proposed, demonstrating superior weight distribution and fairness in the decision process. Compared to the ideal point method and VIKOR, the proposed approach consistently identifies optimal solutions, affirming its scientific validity and effectiveness. The findings suggest broad application prospects in practical construction projects and provide valuable insights for construction management research, highlighting the theoretical and practical significance of the model and framework.

关键词： Steel-Concrete Composite Column Ant colony algorithm Carbon emissions multi-objective optimization Decision-making

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multi-objective optimization algorithms for freight allocation in a food grain supply chain

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APPLIED SOFT COMPUTING 2025年 171卷

作者： Anoop, K. P. Panicker, Vinay V. Siby, Jerin Aryadutt, C. S. Natl Inst Technol Calicut Dept Mech Engn Supply Chain & Syst Simulat Lab Kozhikode 673601 India

This article addresses multi-objective optimization of a freight allocation problem and presents the case of a food grain organization in India (FOI). The inventory and warehouse parameters that are relevant in the regional level allocation of food grains (using freight trains) are represented using three penalty factors, namely rake penalty factor, capacity utilization penalty factor, and weekly penalty factor. The article formulates a tri-objective optimization model to minimize each of the three penalty factors. Two customized multi-objective optimization algorithms are developed based on multi-objective Simulated Annealing (MOSA) and Elitist Non-dominated Sorting Genetic Algorithm II (NSGA II) to solve the formulated model. The algorithms are tested and validated via computational experiments designed using historical data collected from the FOI. The algorithms help the transportation managers at the FOI to generate improved and balanced transportation plans (with respect to the three objectives) in a quick time. Further, the performance of the algorithms is compared based on seven different performance metrics reported in literature. The MOSA-based algorithm performs equally or better than the NSGA II-based algorithm with respect to four performance metrics.

关键词： Food grain supply chain multi-objective optimization Freight allocation multi-objective simulated annealing Non-dominated sorting genetic algorithm II

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multi-objective optimization control for shield cutter wear and cutting performance using LightGBM and enhanced NSGA-II

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AUTOMATION IN CONSTRUCTION 2025年 171卷

作者： Yin, Ziwei Jiao, Jianwei Xie, Ping Luo, Hanbin Wei, Linchun Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan Peoples R China Huazhong Univ Sci & Technol Inst Artificial Intelligence Wuhan Hubei Peoples R China Huazhong Univ Sci & Technol Natl Ctr Technol Innovat Digital Construct Wuhan Peoples R China Shanghai Tunnel Engn Co Ltd Shanghai Peoples R China

Varying results in cutter wear and cutting performance can be observed based on different selections of shield operational parameters, particularly in hard rock or soil with a high quartz content. Improperly selecting operational parameters may result in excessive wear and reduced cutting performance, leading to longer project duration and increased costs. Furthermore, it is still challenging to balance cutter wear and cutting performance. To address these issues, a multi-objective optimization (MOO) framework based on the Light Gradient Boosting Machine (LightGBM) algorithm and the enhanced non-dominated sorting genetic-II (NSGA-II) algorithm is proposed to predict and optimize the cutter wear and cutting performance. To validate this framework, a shield tunneling project in China is presented. The results show that the efficiency and accuracy of predicting and optimizing the two objectives have been improved compared with other common methods. This MOO framework is valuable for operators to formulate rational operational control strategies.

关键词： Cutter wear Cutting performance multi-objective optimization The light gradient boosting machine (LightGBM) Enhanced non-dominated sorting genetic-II (NSGA-II) algorithm

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multi-objective optimization of lithium-ion battery pack thermal management systems with novel bionic lotus leaf channels using NSGA-II and RSM

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ENERGY 2025年 314卷

作者： Dong, Hongyu Chen, Xuanchen Yan, Shuting Wang, Dong Han, Jiaqi Guan, Zhaoran Cheng, Zhanjun Yin, Yanhong Yang, Shuting Henan Normal Univ Sch Chem & Chem Engn Xinxiang 453007 Henan Peoples R China Natl & Local Engn Lab Mot Power & Key Mat Xinxiang 453000 Peoples R China Collaborat Innovat Ctr Henan Prov Mot Power & Key Xinxiang 453000 Peoples R China Tianjin Univ Sch Environm Sci & Engn Tianjin 300072 Peoples R China

This study employs a multi-objective optimization approach integrating the fast non-dominated sorting genetic algorithm (NSGA-II) and response surface methodology (RSM) to enhance the performance of battery thermal management systems (BTMS) through the design and optimization of a novel bionic lotus leaf (NBLL) channel. Heat generation rates, obtained from lithium-ion battery (LIB) testing experiments conducted under various discharge rates, along with design variables such as channel spacing, width, angle, and mass flow rate, are optimized. The objective functions, comprising maximum temperature difference, heat transfer coefficient, and pressure drop, are optimized while adhering to a maximum temperature constraint. Optimal Latin Hypercube Sampling (OLHS) is utilized for selecting design points, and RSM constructs objective function expressions. The optimal combination is determined through the Pareto optimal frontier generated by NSGA-II. Relative to the initial model, the optimized design demonstrates a reduction in the maximum temperature difference by 14.898 %, an increase in the heat transfer coefficient by 35.786 %, and a decrease in the pressure drop by 68.325 %. This optimized BTMS design significantly enhances heat dissipation performance, which is crucial for battery performance, longevity, and safety in the realm of battery thermal management.

关键词： Battery thermal management Liquid cooling Bionic lotus leaf structure multi-objective optimization Fast non-dominated sorting genetic algorithm Response surface methodology

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multi-objective optimization analysis of on-demand design in multi-source heating system for low carbon development

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ENERGY 2025年 324卷

作者： Wang, Weichen Sun, Jingchao Yan, Su Yuan, Yuxing Xiao, Tianshun Chen, Baoqi Du, Tao Na, Hongming Northeastern Univ SEP Key Lab Ecoind Shenyang 110819 Liaoning Peoples R China Minist Educ Engn Res Ctr Frontier Technol Low Carbon Steelmaki Shenyang 110819 Liaoning Peoples R China Inst Frontier Technol Low Carbon Steelmaking Shenyang 110819 Liaoning Peoples R China

A multi-energy complementary heating system (MEHS) is essential for the development of low-carbon building heating. However, limited research has been conducted on the optimal heat load allocation. In light of heat balancing, heat source operation, and energy resource limits, this study proposes a MEHS model based on life cycle assessment, with costs, CO2 emissions, and energy efficiency serving as the objective functions. By optimizing, the Pareto frontier solution is obtained, and the relationships between the objective and the heat load allocation ratios of heating subsystems are analyzed. Finally, the impact of the power industry's low-carbon transition and increasing carbon trading prices on the MEHS is investigated. The results show that the optimal heat load allocation ratios for the air source heat pump, natural gas-fired, biomass-fired, coal-fired, thermal storage electric boiler, and ground source heat pump heating subsystems are 44.42 %, 13.41 %, 0.71 %, 32.18 %, 0.56 %, and 8.72 %, respectively. Costs and CO2 emissions during operation account for more than 80 % of the total life cycle impact. CO2 emissions can be reduced by 16 % when the power grid CO2 emission factor is reduced to 0.2530 kg CO2/kWh, and by 4.5 % when the carbon price increases to 200 CNY/t.

关键词： multi-source heating system CO2 emission reduction Design of heat load system multi-objective optimization

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