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multi-objective optimization for the Energy, Economic, and Environmental Performance of High-Rise Residential Buildings in Areas of Northwestern China with Different Solar Radiation

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APPLIED SCIENCES-BASEL 2024年第15期14卷 6719页

作者： Shao, Teng Wang, Jin Wang, Ruixuan Chow, David Nan, Han Zhang, Kun Fang, Yanna Northwestern Polytech Univ Sch Mech Civil Engn & Architecture Xian 710129 Peoples R China Univ Liverpool Sch Architecture Liverpool L69 3BX England

Currently, the construction and operation of buildings are responsible for 36% of global final energy usage and nearly 40% of energy-related carbon dioxide (CO2) emissions. From the perspective of sustainable development, and taking into account economy and thermal comfort, it is crucial to consider the influence of multi-objective realization on design parameters. In this paper, high-rise residential buildings in the cities of Xi'an and Yulin, which have differences in solar radiation, in the western solar enrichment area of China are taken as the research objects. The four objectives of building energy consumption, thermal comfort, life-cycle cost, and life-cycle carbon emissions are weighed using the SPEA-2 algorithm by adjusting eleven design variables, thereby obtaining the Pareto non-dominated solutions. The TOPSIS method is applied to obtain the suitable parameter combinations under different scenarios. The results show that the differences in climate and solar radiation influence the solution distribution, the range of objective function values, and the values of the design variables in Pareto non-dominated solutions. The obtained optimal scheme for the Xi'an area has an energy-saving rate of 61.7%, a TDHP improvement rate of 20.3%, an LCC of 254.8 CNY/m2, and an LCCO2 of 72.3 kgCO2/m2. The corresponding values in the Yulin area are 69.7%, 19.4%, 230.2 CNY/m2, and 0 kgCO2/m2. This reflects the potential of solar energy utilization to reduce buildings' energy consumption and carbon emissions. The methodology and findings can provide references for high-rise residential building design in Northwestern China.

关键词： high-rise residential building multi-objective optimization energy consumption life-cycle cost carbon emissions solar energy utilization

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multi-objective optimization of wire-wrapped reactor fuel assemblies using CFD and genetic algorithms

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INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER 2024年 159卷

作者： Qiu, Hanrui Liu, Zhenglong Wang, Mingjun Tian, Wenxi Su, Guanghui Xi An Jiao Tong Univ Dept Nucl Sci & Technol Xian Peoples R China

The lead bismuth eutectic cooled fast reactor (LFR), as a novel energy conversion system, offer significant advantages in terms of both economy and safety compared to other sustainable energy sources. However, the optimization of reactor design using traditional control variable methods faces certain limitations when dealing with multiple design variables. Therefore, this study proposes a multi-objective optimization analysis method based on computational fluid dynamics (CFD) and genetic algorithm. This approach aims to find geometric designs of wire that provide the highest heat transfer capability and the lowest flow resistance. This study conducted numerical simulations on a 7-rod fuel assembly of the LFR, analyzing the impact of different wire pitches and diameters on thermal-hydraulic parameters of the coolant. The results indicated that the wire is the primary factor promoting coolant mixing. Variations in wire pitch predominantly affect the heat transfer coefficient. When the pitch is greater than 342.7 mm and the diameter is greater than 4.2 mm, variations in diameter primarily influence the friction factor;below these values, wire pitch is more significant in determining the friction factor. The errors in the multi-objective optimization analysis are within the specified range (10 %), with maximum relative errors of 6.67 % for the heat transfer coefficient and 5.29 % for the friction factor. The maximum heat transfer coefficient of 18,933 W & sdot;m- 2 & sdot;K- 1 is occurred when the wire pitch is 179.2 mm and the diameter is 4.4 mm. The minimum friction factor of 0.161 is observed when the wire pitch is 397.2 mm and the diameter is 3.5 mm.

关键词： multi-objective optimization CFD Advanced energy technology Heat transfer enhancement Thermodynamic analysis

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multi-objective optimization and multi-attribute decision-making support for optimal operation of multi stakeholder integrated energy systems

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APPLIED SOFT COMPUTING 2024年 167卷

作者： Zheng, J. H. Zhai, L. X. Li, Fang Wang, Dandan Li, Yalou Li, Zhigang Wu, Q. H. South China Univ Technol Sch Elect Power Engn Guangzhou 510641 Peoples R China China Elect Power Res Inst State Key Lab Power Grid Safety & Energy Conservat Beijing 100192 Peoples R China Chinese Univ Hong Kong Shenzhen Sch Sci & Engn Shenzhen Peoples R China

To efficiently tackle the optimal operation problem of multi-stakeholder integrated energy systems (IESs), this paper develops a multi-objective optimization and multi-attribute decision-making support method. Mathematically, The optimal operation of IESs interconnected with distributed district heating and cooling units (DHCs) via the power grid and gas network, can be formulated as a multi-objective optimization problem considering both economic, reliability and environment-friendly objectives with numerous constraints of each energy stakeholder. Firstly, a multi-objective group search optimizer with probabilistic operator and chaotic local search (MPGSO) is proposed to balance global and local optimality during the random search iteration. The MPGSO utilizes a crowding probabilistic operator to select producers to explore areas with higher potential but less crowding and reduce the number of fitness function calculations. Moreover, a new parameter selection strategy based on chaotic sequences with limited computational complexity is adopted to escape the local optimal solutions. Consequently, a set of superior Pareto-optimal fronts could be obtained by the MPGSO. Subsequently, a multi-attribute decision-making support method based on the interval evidential reasoning (IER) approach is used to determine a final optimal solution from the Pareto-optimal solutions, taking multiple attributes of each stakeholder into consideration. To verify the effectiveness of the MPGSO, the DTLZ suite of benchmark problems are tested compared with the original GSOMP, NSGA-II and SPEA2. Additionally, simulation studies are conducted on a modified IEEE 30-bus system connected with distributed DHCs and a 15-node gas network to verify the proposed approach. The quality of the obtained Pareto-optimal solutions is assessed using a set of criteria, including hypervolume (HV), generational distance (GD), and Spacing index, among others. Simulation results show that the number of Pareto-opt

关键词： multi-objective optimization Group search optimizer Decision-making support Integrated energy systems Optimal operation

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multi-objective optimization for Quantum Rectangular Cycle with Power, Efficiency and Efficient Power

ACTA PHYSICA POLONICA A

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ACTA PHYSICA POLONICA A 2024年第1期145卷 16-27页

作者： Xie, X. Chen, L. Yin, Y. Shi, S. Wuhan Inst Technol Sch Opt Informat & Energy Engn Wuhan 430205 Peoples R China Wuhan Inst Technol Inst Thermal Sci & Power Engn Wuhan 430205 Peoples R China Hubei Prov Engn Technol Res Ctr Green Chem Equipme Wuhan 430205 Peoples R China Wuhan Inst Technol Sch Mech & Elect Engn Wuhan 430205 Peoples R China

This paper establishes a quantum rectangular heat engine model by applying finite -time thermodynamics. The working medium is countless particles trapped in a one-dimensional infinite potential well. Taking into account heat leakage between the system and the outside, expressions for thermal efficiency (eta), dimensionless power ( P over bar ), and dimensionless efficient power ( over bar Wep) of quantum rectangular heat engine are derived, and its optimal performance is studied. System performance P over bar , eta, and over bar are optimized firstly by taking the width ratio of the potential well as the optimization variable. The outcomes show that the relationship curve between P over bar and eta is a loop -shaped curve. With an increase in heat leakage coefficient, the optimal design range of the quantum rectangular heat engine becomes smaller. The relationship curve between the efficient power and width ratio of the potential well is parabolic -like. The relationship curve between over bar Wep and eta is a loop -shaped curve. The efficiency of the quantum rectangular heat engine at over bar Wep max operating point is greater than that at P over bar max operating point. Secondly, multi -objective optimization is applied with eta, P over bar , and over bar Wep as optimization objectives by applying NSGA-II, and the optimal design scheme is achieved by applying TOPSIS, LINMAP, and Shannon entropy decision -making approaches. The smallest deviation index inferred by the Shannon entropy approach is 0.0269. The design scheme is closest to the ideal scheme.

关键词： finite-time thermodynamics quantum rectangular heat engine (QRHE) power multi-objective optimization thermal efficiency and efficient power

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multi-objective optimization of microcellular injection molding process parameters to reduce energy consumption and improve product quality

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2024年第11-12期134卷 5159-5173页

作者： Guo, Wei Lu, Tongyuan Zeng, Fankun Zhou, Xiaoyu Li, Wei Yuan, Huan Meng, Zhenghua Wuhan Univ Technol Sch Automot Engn Wuhan 430070 Peoples R China Wuhan Univ Technol Hubei Key Lab Adv Technol Automot Components Wuhan 430070 Peoples R China Wuhan Univ Technol Hubei Collaborat Innovat Ctr Automot Components Te Wuhan 430070 Peoples R China Wuhan Univ Technol Hubei Res Ctr New Energy & Intelligent Connected V Wuhan 430070 Peoples R China Wuhan Univ Technol Inst Adv Mat & Mfg Technol Wuhan 430070 Peoples R China SAIC GM Wuling Automobile Co Ltd Liuzhou 545007 Peoples R China

The microcellular injection molding (MIM) process is widely used in the manufacture of automotive components to achieve vehicle lightweighting. Due to the complex conditions of the MIM process, it can lead to significant energy consumption and product warpage deformation. To achieve low energy consumption, lightweight, and high-quality production of MIM products, this study selected the automotive door interior panel as the research object, and the parameters of the MIM process were optimized by comprehensively taking into account the production energy consumption, weight, and warpage. In particular, the training sample database was created using Latin hypercube sampling, and the optimal neural network prediction model was established considering the nonlinear relationship between process parameters and energy consumption, quality, and warpage. Then, the NSGA-II algorithm and the fuzzy decision based on the critic method were used to identify the optimal process parameters. Finally, the finite element simulation of automobile door interior panels verifies the exactitude of the optimization process. The optimized energy consumption, weight, and warpage are 89.54 kJ, 169.5 g, and 2.807 mm, respectively, and have decreased by 16.78%, 2.88%, and 8.48% when compared with the best results under the combination of traditional process parameters.

关键词： Injection molding process multi-objective optimization Automotive door interior panel Neural network Energy consumption

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multi-objective optimization for Turning Process of 304 Stainless Steel Based on Dung Beetle Optimizer-Back Propagation Neural Network and Improved Particle Swarm optimization

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JOURNAL OF MATERIALS ENGINEERING AND PERFORMANCE 2024年第8期33卷 3787-3800页

作者： Xue, Huan Li, Tao Li, Jie Zhang, Yansong Huang, Shiyao Li, Yongchun Yang, Chongwen Zhang, Wenqian Hubei Univ Technol Sch Mech Engn Hubei Key Lab Modern Mfg Qual Engn Wuhan 430068 Peoples R China Hubei Guoan Special Steel Inspect & Testing Co Huangshi 435000 Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430076 Peoples R China

Austenite stainless steel of type 304 is one of the most difficult materials to process. During the machining process, parts easily generate higher surface residual stress in cutting direction (CD) and production costs. To solve the problem, a multi-objective optimization method that combined Dung Beetle Optimizer-Back Propagation Neural Network (DBO-BPNN) and Improved Particle Swarm optimization (IPSO) algorithm was adopted. Firstly, BPNN, Genetic Algorithm-Back Propagation Neural Network (GA-BPNN) and DBO-BPNN were established to map the nonlinear relationship between surface residual stress in CD and turning parameters. Secondly, a dataset of surface residual stress in CD and material removal rate under different turning parameters was obtained through finite element method (FEM) turning simulation. The dataset was applied into neural network to establish a nonlinear mapping relationship between turning parameters and surface residual stress in CD. The turning parameters are used as variables, the surface residual stress in CD and material removal rate are applied as objective functions. The optimal Pareto solution set of the surface residual stress in CD and material removal rate was acquired by combining DBO-BPNN and IPSO. Finally, turning experiments were designed to verify the accuracy of the turning simulation. The study shows that the surface residual stress in CD decreased by 38.47%, and the material removal rate increased by 91.69%.

关键词： 304 stainless steel dung beetle optimizer algorithm improved particle swarm optimization algorithm multi-objective optimization residual stress turning process

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multi-objective optimization and comprehensive Thermodynamic, economic, and environmental analysis of an innovative solar-geothermal integrated system for synergistic power generation and water desalination

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2024年第1期46卷 13065-13087页

作者： Babaelahi, Mojtaba Lotfalipour, Mohammad Univ Qom Dept Mech Engn Alghadir Blvd Qom *** Iran

This paper presents a novel multi-objective optimization and sustainability analysis framework for an innovative solar-geothermal integrated system for power generation and water desalination. Harnessing renewable solar and geothermal energy, the system offers a sustainable solution for energy, water, and environmental challenges. Advanced thermodynamic modeling techniques, validated by industry simulations, accurately characterize system performance. Departing from conventional analyses, the research incorporates exergy-based economic and environmental assessments and the emergy concept to quantify the total available energy required, enabling a comprehensive sustainability evaluation. Through multi-objective optimization using genetic algorithms, the system's design and operational parameters are optimized to enhance exergy efficiency, economic emergy performance, and environmental emergy performance simultaneously. The optimization framework considers turbine inlet conditions, pressures, and desalination configurations to identify optimal operating points balancing energy efficiency, economic feasibility, and environmental impact. Results demonstrate significant improvements of up to 23.49% in exergy efficiency, 25.97% in economic emergy performance, and 16.04% in environmental emergy performance compared to the baseline system. This study focuses on the multi-objective optimization of a novel solar-geothermal integrated system for synergistic power generation and water desalination. The methodology combines thermodynamic, economic, and environmental analyses to evaluate system performance. Key findings include a significant improvement in overall efficiency and sustainability compared to conventional systems, highlighting the potential for large-scale implementation.

关键词： Exergy analysis emergy analysis multi-objective optimization solar-geothermal hybrid system simultaneous power and desalination

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multi-objective optimization and 4E analyses of the solar energy assisted direct-heated sCO2 power cycle for low-temperature applications

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2024年第1期46卷 13499-13520页

作者： Toker, Serpil Celik Kizilkan, Onder Isparta Univ Appl Sci Fac Technol Dept Mech Engn TR-32200 Isparta Turkiye

The aim of this study is to investigate the performance of the intercooling and reheating supercritical CO2 Brayton cycle (SCBC) for low-temperature solar applications. Thermodynamic, exergo-economic, and exergo-environmental analyses of SCBC are examined. Furthermore, parametric research is performed to assess the effects of operating parameters on plant performance. Finally, multi-objective optimization (MOO) of the solar energy-based integrated plant is conducted for optimum operating conditions, taking into account the main parameters affecting the system performance. Considering the analyses, the generated power is calculated as 0.380 kW, while the system's energetic and exergetic efficiencies are found as 3.91% and 4.20%, respectively. According to the exergo-economic analysis results, exergo-economic analysis, the total cycle's cost rate is 0.889 $/h, the total cost rate of exergy destruction is 0.274 $/h, the total exergo-economic factor value is 69.14%, and the total purchase cost of the plant is 31,422$. Moreover, the total collector number is 23, the compressor inlet pressure is 8067.57 kPa, and the system's entire purchase cost is 41,031 $, according to the MOO results. The exergy efficiency of the system improved by 20.53%, as an outcome of the optimization analysis.

关键词： Energy evacuated solar collector exergy multi-objective optimization supercritical Brayton cycle

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multi-objective optimization design of a circular core paper sandwich panel

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NORDIC PULP & PAPER RESEARCH JOURNAL 2024年第4期39卷 587-600页

作者： Jiang, Xiawang Zhang, Shihao Yu, Minggong Sun, Delin Cent South Univ Forestry & Technol Coll Mat Sci & Engn Changsha 410004 Peoples R China Hunan Univ Technol Sch Packaging Design & Art Zhuzhou 412007 Peoples R China Hunan Inst Engn Coll Sci Xiangtan 411104 Peoples R China

Ensuring sufficient mechanical performance while enabling lightweight design is critical for utilizing paper sandwich panels in the furniture industry. To design lightweight sandwich panels that balance mechanical properties and cost, this study developed a circular core paper sandwich panel (CCPSP) and investigated its structural efficiency using multi-objective optimization. The response surface method (RSM) based on Box-Behnken design was utilized to establish mathematical models relating the paper tube spacing, inner diameter, and height to the out-of-plane compressive strength, density, and cost. The resulting models effectively revealed the coupled effects of the parameters on the responses. Subsequently, the models were optimized using the non-dominated sorting genetic algorithm II (NSGA-II) to find the Pareto optimal trade-offs between maximizing compressive strength while minimizing its density and cost. The optimization solution resulted in an optimal set of paper tube geometries that maximized the structural efficiency of CCPSP. Overall, lower tube height conferred superior structural efficiency, while tube spacing and diameter were constrained. The results highlight the potential of CCPSP as an efficient and sustainable material for furniture manufacturing, enabled by multi-objective optimization of its structure.

关键词： sandwich panel paper tube multi-objective optimization structural efficiency furniture

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multi-objective optimization of safety, comfort and economy of hybrid electric vehicle in car-following scenario

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INTERNATIONAL JOURNAL OF GREEN ENERGY 2024年第5期21卷 1060-1071页

作者： Du, Aimin Han, Yeyang Zhu, Zhongpan Chu, Chuanchuan Tongji Univ Sch Automot Studies Shanghai Peoples R China Tongji Univ Sch Elect & Informat Engn Shanghai Peoples R China

The energy management strategy (EMS) of hybrid electric vehicle (HEV) is the key to optimize the energy conversion performance of the whole vehicle power system. The performance of EMS could be affected by vehicle parameters such as vehicle speed, battery state of charge (SOC) and the environmental parameters including the current following state jointly. Combining the following state of the vehicle with EMS can be beneficial to improve the road performance of HEV. In this paper, a multi-objective optimization framework for safe following, comfort and fuel economy of HEV is established based on model predictive control (MPC) under both urban and high-speed conditions, and the kinematic parameter of the preceding vehicle was predicted on the basis of third-order Markov model. Simulation tests including urban and high-speed working condition are carried out respectively and results show that the following vehicle maintains expected following safety and comfort. Meanwhile, compared to the typical rule-based strategy and equivalent consumption minimum strategy (ECMS), the fuel economy has also been increased by 29.8% and 9.2% in UDDS working condition, 25.2% and 6.1% in WLTC working condition, respectively.

关键词： Hybrid electric vehicle multi-objective optimization third-order markov model model predictive control car-following >

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