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A multi-objective optimization of methylcyclohexane dehydrogenation via microwave-enhanced process: 3D multiphysics simulation and response surface methodology

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2025年 112卷 322-332页

作者： Sun, Jianchen Shang, Hui Fan, Xiayu Li, Keying Li, Jun China Univ Petr State Key Lab Heavy Oil Proc Beijing 102249 Peoples R China Petrochina Planning & Engn Inst Zhixin West Rd 3 Beijing 100083 Peoples R China

Microwave enhancement technology has been explored to improve methylcyclohexane (MCH) dehydrogenation. However, comprehensive studies on multiphysics coupling and parameter optimization of these systems remain limited. In this paper, a 3D multiphysics coupling model, integrating chemical reactions, heat transfer, electromagnetic waves, and porous media flow, was developed using COMSOL multiphysics to study MCH dehydrogenation. Response Surface Methodology (RSM) and Box-Behnken Design (BBD) were used to analyze the influence of parameters and optimize the operating conditions. The results showed that the conversion rate of microwave heating was nearly 70% higher than that of conventional heating, and the feed rate and temperature had the greatest influence on the conversion rate. Under the optimal conditions (623 K, 0.1 bar, carrier-gas ratio 1, feed rate 0.173 g/min), the conversion rate was 86.3%, and the hydrogen production rate was 1820 mmol/gPt/ min. This study provides a theoretical basis for the development of efficient microwave enhanced MCH dehydrogenation reactor.

关键词： Microwave-enhanced Methylcyclohexane dehydrogenation Response surface methodology multiphysics simulation model multi-objective optimization

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Industrial transformation for synergistic carbon and pollutant reduction in China: Using environmentally extended multi-regional input-output model and multi-objective optimization

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ENERGY 2025年 318卷

作者： Zhang, Shuo Yu, Kun Yu, Yadong East China Univ Sci & Technol Sch Business Shanghai Peoples R China Natl Univ Singapore Dept Civil & Environm Engn Singapore Singapore

China faces significant environmental challenges, including reducing pollutants, improving environmental quality, and peaking carbon emissions. Industrial restructuring is key to achieving both emission reductions and economic transformation. This study uses the Environmentally Extended multi-Regional Input-Output model and multi-objective optimization to analyze pathways for China's industrial transformation to synergistically reduce emissions. Our findings indicate that under a compromise scenario, China's carbon emissions could stabilize at around 10.9 billion tonnes by 2030, with energy consumption controlled at approximately 5 billion tonnes. The Papermaking sector in Guangdong and the Chemicals sector in Shandong are expected to flourish, while the Coal Mining sector in Shanxi and the Communication Equipment sector in Jiangsu will see reductions. The synergy strength between carbon emission reduction and energy conservation is highest at 11 %, followed by a 7 % synergy between carbon emission and nitrogen oxide reduction. However, significant trade-offs are observed between carbon emission reduction and chemical oxygen demand, and ammonia nitrogen reduction targets at -9%. This comprehensive analysis at regional and sectoral levels provides valuable insights for advancing China's carbon reduction and pollution control goals.

关键词： Carbon reduction Pollutant reduction Industrial transformation China multi-regional input-output model multi-objective optimization

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Integrating Grey-Green Infrastructure in Urban Stormwater Management: A multi-objective optimization Framework for Enhanced Resilience and Cost Efficiency

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APPLIED SCIENCES-BASEL 2025年第7期15卷

作者： Wang, Lie Zhao, Jiayu Xiong, Ziheng Zhuang, Ji'an Wang, Mo Hunan Univ Informat Technol Art Sch Changsha 410151 Peoples R China Guangzhou Univ Coll Architecture & Urban Planning Guangzhou 510006 Peoples R China

Urban stormwater management systems are increasingly strained by rapid urbanization and climate change, yet existing planning approaches often lack holistic optimization frameworks that account for both green and grey infrastructure (GREI) under uncertain future conditions. This study introduces a multi-objective optimization framework for Grey-Green Infrastructure (GGI), which integrates green infrastructure (GI) with GREI to enhance urban flood resilience, cost efficiency, and adaptability. The framework addresses life cycle cost (LCC), technological resilience (Tech-R), and operational resilience (Oper-R), offering a comprehensive approach to navigating the complexities of urban stormwater management. Key findings reveal that: (1) GGI systems optimized for resilience achieve a 33% improvement in Oper-R, with only a marginal increase in LCC of less than 9%, highlighting their robustness under GREI failure scenarios;(2) the integration of bioretention cells (BCs) and porous pavements (PPs) into GGI increases Tech-R by 7.1%, enhancing soil water retention and permeability, particularly in densely urbanized contexts;and (3) decentralized GGI systems exhibit superior adaptability to extreme weather events, with Design D reducing LCC to USD 53.9 M while maintaining no overflow under a 5-year rainfall event. The framework was validated in Zhujiang New Town, Guangzhou, where optimized GGI designs reduced average pipe diameters and manhole depths by 0.2-0.3 m compared to GREI-only systems, demonstrating both cost and resilience advantages. These findings provide decision-makers with a robust tool for evaluating trade-offs in stormwater infrastructure planning, advancing sustainable urban water management.

关键词： Grey-Green Infrastructure multi-objective optimization resilience urban stormwater management life cycle cost

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Genetic algorithm-optimized artificial neural network for multi-objective optimization of biomass and exopolysaccharide production by Haloferax mediterranei

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BIOPROCESS AND BIOSYSTEMS ENGINEERING 2025年第5期48卷 785-798页

作者： Al Rawahi, Alaa M. Zafar, Mohd. Khan, Taqi Ahmed Al Araimi, Sara Mahanty, Biswanath Behera, Shishir Kumar Univ Technol & Appl Sci Sur Coll Appl Sci & Pharm Dept Appl Biotechnol Sur Oman Karunya Inst Technol & Sci Div Biotechnol Coimbatore 641114 Tamil Nadu India Vellore Inst Technol Sch Chem Engn Ind Ecol Res Grp Vellore 632014 Tamil Nadu India

Microbial production of industrially important exopolysaccharide (EPS) from extremophiles has several advantages. In this study, key media components (i.e., sucrose, yeast extract, and urea) were optimized for biomass growth and extracellular EPS production in Haloferax mediterranei DSM 1411 using Box-Behnken design. In a multi-objective optimization framework, response surface methodology (RSM) and genetic algorithm (GA)-optimized artificial neural network (ANN) were used to minimize biomass growth while increasing EPS production. The performance of the selected ANN model for the prediction of biomass and EPS (R-2: 0.964 and 0.975, respectively) was found to be better than that of the multiple regression model (R-2: 0.818, 0.963, respectively). The main effect of sucrose and its interaction with urea appears to have a significant effect on both responses. The ANN model projects an increase in EPS production from 4.49 to 18.2 g l(-1) while shifting the priority from biomass to biopolymer. The optimized condition predicted a maximum biomass and EPS production of 17.27 g l(-1) and 17.80 g l(-1), respectively, at concentrations of sucrose (19.98 g l(-1)), yeast extract (1.97 g l(-1)), and urea (1.99 g l(-1)). Based on multi-objective optimization, the GA-ANN model predicted an increase in the EPS to biomass ratio for increasing the EPS and associated biomass production. The extracted EPS, identified as Gellan gum through NMR spectroscopy, was further characterized for surface and elemental composition using SEM-EDX analysis.

关键词： Box-Behnken design Composite desirability Gellan gum Genetic algorithm multi-objective optimization

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A multi-objective Evolutionary Algorithm Based on Bilayered Decomposition for Constrained multi-objective optimization

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2025年第2期20卷 244-262页

作者： Yasuda, Yusuke Kumagai, Wataru Tamura, Kenichi Yasuda, Keiichiro Tokyo Metropolitan Univ Dept Elect Engn & Comp Sci 6-6 Asahigaoka Hino Tokyo 1910065 Japan Yokogawa Elect Corp Innovat Ctr 2-9-32 Naka Cho Musashino Tokyo 1800006 Japan

This paper proposes a multi-objective evolutionary algorithm based on bilayered decomposition (MOEA/BLD) for solving constrained multi-objective optimization problems. MOEA/D is an effective method for solving unconstrained multi-objective optimization problems. It decomposes the objective space using weight vectors and simultaneously searches for solutions for the subproblems. However, real-world applications impose many constraints, and these constraints must be handled appropriately when searching for good feasible solutions. The proposed MOEA/BLD treats such constraints as an additional objective function. Furthermore, in addition to the conventional weight vector, an augmented weight vector is introduced that decomposes the objective space and constraint violation space hierarchically. In the first stage, the objective space is decomposed by conventional weight vectors. In the next stage, the bi-objective space consisting of the scalarizing function and constraint violation is decomposed by augmented weight vectors. The augmented weights are adjusted so that they decrease linearly in the search process as the search gradually moves from infeasible regions to feasible regions. The proposed algorithm is compared to several state-of-the-art constrained MOEA/Ds using multi- and many-objective problems. The results show that the proposed method outperforms existing methods, in terms of search performance, under various conditions. (c) 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

关键词： multi-objective optimization metaheuristics evolutionary computation MOEA/D constraint handling decomposition

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Analytical Characterization and multi-objective optimization of Scramjet Intake Performance

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TRANSACTIONS OF THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES 2025年第1期68卷 57-67页

作者： Fujio, Chihiro Ogawa, Hideaki Kyushu Univ Fukuoka Fukuoka 8190395 Japan

Scramjet engines are a promising airbreathing propulsion technology for high-speed transportation as well as accessto-space. The air intake plays a major role in determining the overall performance of scramjets. Designing high-performance intakes by taking both aerodynamic and thermodynamic characteristics into account represents a significant task. This paper presents new knowledge on scramjet intake performance along with underlying physical ground that has been obtained by comprehensive analytical formulations of global intake characteristics derived on a theoretical basis. Heat transfer across the intake surface has been found to be responsible for the trade-off between compression efficiency and drag under assumptions of a fixed contraction ratio or mean exit temperature, while the mean exit temperature and the contraction ratio are the decisive factors for the compression ratio and efficiency. multi-objective design optimization has then been performed to verify these trade-offs and examine their influence on intake design. The resultant optimal solutions have been found to underpin the design strategy based on these relations. The new insights gained from this study conduce to rapid and reliable design of high-performance intakes.

关键词： Scramjet Intake multi-objective optimization Compressible Flow

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Kinetic modeling and multi-objective optimization of an industrial hydrocracking process with an improved SPEA2-PE algorithm

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CHINESE JOURNAL OF CHEMICAL ENGINEERING 2025年 80卷 130-146页

作者： Fan, Chen Wang, Xindong Li, Gaochao Long, Jian East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China

Hydrocracking is one of the most important petroleum refining processes that converts heavy oils into gases, naphtha, diesel, and other products through cracking reactions. multi-objective optimization algorithms can help refining enterprises determine the optimal operating parameters to maximize product quality while ensuring product yield, or to increase product yield while reducing energy consumption. This paper presents a multi-objective optimization scheme for hydrocracking based on an improved SPEA2-PE algorithm, which combines path evolution operator and adaptive step strategy to accelerate the convergence speed and improve the computational accuracy of the algorithm. The reactor model used in this article is simulated based on a twenty-five lumped kinetic model. Through model and test function verification, the proposed optimization scheme exhibits significant advantages in the multi-objective optimization process of hydrocracking. (c) 2025 Chemical Industry and Engineering Society of China (CIESC) and Chemical Industry Press Co., Ltd. (CIP). Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

关键词： Hydrocracking multi-objective optimization Improved SPEA2 Kinetic modeling

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Experimental analysis and multi-objective optimization of heavy-duty hydrogen SI engine performance and emissions based on GA-BP-MOGWO

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ENERGY CONVERSION AND MANAGEMENT 2025年 329卷

作者： Ning, Dezhong Dong, Jiawei Guan, Wei Wang, Zhi Wang, Hui Lin, Tiejian Qin, Yufeng Zhang, Song Pan, Mingzhang Guangxi Yuchai Machinery Co Ltd Yulin 537000 Peoples R China Tsinghua Univ Sch Vehicle & Mobil Beijing 100084 Peoples R China Tsinghua Univ State Key Lab Intelligent Green Vehicle & Mobil Beijing 100084 Peoples R China Guangxi Univ Coll Mech Engn Nanning 530004 Peoples R China

The heavy-duty hydrogen engine, as a key technology for achieving zero-carbon emissions, shows great development potential. The main problem of the hydrogen engine is high fuel consumption, high NOx emissions and hydrogen leakage. This study combined experimental and computational techniques to systematically study the effects of the excess air coefficient (lambda) and spark timing (SPK) on the combustion, performance and emission characteristics of large-displacement multi-cylinder commercial hydrogen engines at a speed of 1200 r/min and throttle opening of 40 %. Further, the study explored the effects of key operating parameters on the hydrogen engine's power characteristics (power), economic characteristics (Brake Specific Fuel Consumption, BSFC), and emission characteristics (NOx, Hydrogen Leakage). The experiment results reveal that the leaner the mixture, the better the economy, but it had adverse effect on power. The lowest BSFC of around 75 g/kWh is achieved when controlling lambda at 2.8 and the SPK timing at approximately -30 degrees CA ATDC. Additionally, the lean mixture is conducive to reducing NOx emissions, and the minimum hydrogen leakage is located in a narrow area around lambda at 2.2 and the SPK timing from -17 degrees CA ATDC to -33 degrees CA ATDC. In this paper, the complex relationship between independent variables (lambda, SPK timing, intake pressure, CA50) and dependent variables (power, BSFC, NOx, Hydrogen Leakage) was established based on Genetic Algorithm-Back Propagation Neural Network (GA-BP) method. Finally, the study combined the multi-objective Grey Wolf Optimizer (MOGWO) algorithm and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to find the optimal trade-off between the performance and emissions of the hydrogen engine and obtain the optimal operating conditions. The multiobjective optimization results show that the NOx emissions and hydrogen leakage can be effectively reduced. Specifically, compared with t

关键词： Heavy-duty commercial engine Hydrogen fuel Excess air coefficient Combustion and Emission GA-BP-MOGWO multi-objective optimization

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Comprehensive photovoltaic system in roofs, opaque walls, and windows toward zero-energy buildings utilizing multi-objective optimization

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JOURNAL OF BUILDING ENGINEERING 2025年 104卷

作者： Kang, Yiting Cui, Yu Zhang, Dongjie Xu, Wei Pang, Feng Lu, Shilei Wu, Jianlin Zhao, Yue Mao, Rui Tianjin Univ Sch Environm Sci & Engn Tianjin 300072 Peoples R China China Acad Bldg Res Beijing 100013 Peoples R China China Green Dev Investment Grp Co Ltd Beijing 100020 Peoples R China Urban Dev Design Grp Co Ltd Zero Carbon Bldg Res Inst Dalian 116021 Peoples R China

Photovoltaic (PV) technologies are pivotal in achieving zero-energy building (ZEB) targets yet face empirical design challenges, overemphasis on power generation (PG), and limited synergy with passive building parameters. Traditional approaches, often using simulation tools like EnergyPlus with local search optimization methods, struggle to handle complex parameter interactions and multi-objective trade-offs. In this study, a comprehensive data-driven multiobjective optimization (MOO) method was developed for PV-integrated ZEB design that considers EC, cost-effectiveness, thermal comfort, PVPG, and lighting comfort. First, energy, daylighting, thermal comfort, and PV systems were simulated for a nearly-zero energy office building in the cold climate zone in China. Second, a non-linear relationship between design parameters and optimization objectives was modeled using particle swarm optimization (PSO) combined with support vector machine (SVM) technique. Finally, Non-dominated Sorting Genetic Algorithm III (NSGA-III) was applied to optimize five objectives simultaneously. Compared to traditional methods, the proposed approach demonstrated superior capacity in managing complex parameters simultaneously optimization. The results showed that the PSO-SVM model could predict the five optimization objectives effectively, with R2 for all objectives above 0.9. The optimal PV configuration location prioritizes windows and rooftops, whereas opaque walls integrated with PV systems were excluded. The results also highlighted the effective combination of PV systems with windows at a transmittance of 0.29 and a solar heat gain coefficient of 0.5. This study provides a novel PSO-SVM-NSGA-III framework for PV-integrated ZEB design, enhancing the synergy between building performance and PV design parameters, thus contributing to improving energy efficiency strategies for ZEB design.

关键词： Zero-energy building PV system multi-objective optimization Data-driven

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A novel hybrid method for predicting maglev train-induced environmental vibrations combining field measurement, 2.5D FEM modelling and multi-objective optimization

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ENGINEERING STRUCTURES 2025年 325卷

作者： Gu, Xiaoqiang Wu, Deshun Liu, Xin Wang, Pinshan Yue, Jianyong Tongji Univ Dept Geotech Engn Shanghai 200092 Peoples R China Zhejiang Univ Sch Civil Engn & Architecture Hangzhou 310018 Peoples R China East China Architectural Design & Res Inst Co Ltd Shanghai 200041 Peoples R China East China Architectural Design & Res Inst Co Ltd Shanghai Underground Space Engn Design & Res Inst Shanghai 200011 Peoples R China

The maglev system has been developed rapidly in recently years since its advantage in mass transportation, ride comfort and energy efficiency, which also caused an increasing concern on the maglev train-induced environmental vibrations. This paper presents a novel hybrid framework for predicting high-speed maglev train-induced environmental vibrations combining field measurement, 2.5D FEM simulation and multi-objective optimization. First, a maglev train-guideway-soil coupled model considering guideway irregularities was proposed to calculate the ground vibration using 2.5D FEM. Then, the guideway irregularity spectrum and soil damping are inverted by the optimization process using the NSGA3 genetic algorithm, in which the differences of measured and calculated vertical vibrations of measuring points are chosen as objective functions. With the inverted guideway irregularity spectrum and soil damping, the ground vibrations were calculated using the 2.5D FEM model and compared with the field measurements. It was found that the inverted PSD spectrum of guideway irregularity generally decreases as the spatial frequency increases, exhibiting a similar trend as the measured ones. The results obtained by the proposed hybrid prediction method agree well with the field measurement in both time domain and frequency domain. The prediction error for the vertical vibration level of all measuring points induced by five maglev train passing are mainly less than 10 %, indicating that the proposed hybrid method provided a reliable and effective method for vibration prediction combining field measurement and numerical simulation.

关键词： Maglev train Environmental vibration Field measurement 2.5D FEM Hybrid method multi-objective optimization

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