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Multi-objective optimization of supersonic separator for gas removal and carbon capture using three-field two-phase flow model and non-dominated sorting genetic algorithm-ii (NSGA-ii)

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SEPARATION AND PURIFICATION TECHNOLOGY 2025年 358卷

作者： Ding, Hongbing Zhang, Guangchen Wang, Shiwei Zhang, Yu Yang, Yan Wen, Chuang Tianjin Univ Sch Elect & Informat Engn Tianjin Key Lab Proc Measurement & Control Tianjin 300072 Peoples R China Huainan Normal Univ Sch Mech & Elect Engn Huainan 232038 Peoples R China Univ Exeter Fac Environm Sci & Econ Exeter EX4 4QF England Univ Reading Sch Built Environm Reading RG6 6AH England

Supersonic separator is an efficient technology for gas removal and carbon capture. To enhance its performance, many researchers have studied its structure;however, existing studies have primarily used traditional computational fluid dynamics (CFD) models for single-objective structural optimization of the separator's separation performance. However, in the supersonic separators, separation efficiency and pressure-loss ratio are the most important and conflicting performance parameters, and evaluating separation performance in isolation from either one is incomplete. In the present study, we develop a gas-liquid two-phase three-field CFD model considering liquid films. This mathematical model is combined with the non-dominated sorting genetic algorithm-ii (NSGA-ii) for multi-objective optimization of the coupled multiple structural parameters with the objective of the separation efficiency and pressure-loss ratio. The results indicate that the maximum relative errors between simulated and predicted values for the four Pareto optimal solutions in computing pressure loss ratio and separation efficiency are 5.4% and 5.3%, respectively. The optimized solutions achieve the maximum reduction in pressure loss ratio of 28.3% at the same 90% separation efficiency compared to the original structure.

关键词： Separation Efficiency Supersonic separator Multi-objective Optimization Method Gas-liquid three-field two-phase CFD model non-dominated sorting genetic algorithm-ii Cost analysis

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Ergonomic conscious scheduling of maintenance activities in marine vehicles using an optimized non-dominated sorting genetic algorithm-ii - An application of job-shop scheduling

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 149卷

作者： Usman, Shaban Lu, Cong Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu Peoples R China Yibin Pk Univ Elect Sci & Technol China Yibin Peoples R China

Incorporating ergonomics in marine activities is critical due to the extreme working conditions and limited crew in marine vehicles, aiming to enhance productivity and job performance by reducing the risks of fatigue, stress, and work-related musculoskeletal disorders. This paper introduces an innovative analogy of the flexible job-shop scheduling problem with ergonomic considerations (AFJSP-ER) to schedule maintenance activities in marine systems, addressing the dual objectives of optimizing productivity and promoting ergonomic relief. A novel metric, 'ergonomic impact load' is introduced to assess the actual workload of the crew by combining the processing time and the rapid entire body assessment (REBA) score of an operation. To solve the AFJSP-ER, an optimized non-dominated sorting genetic algorithm-ii (ONSGA) is proposed, incorporating an optimized random crossover (ORX) operator. The ORX operator is fine-tuned using the Taguchi method to determine the optimal number of elements for crossover, while non-dominated sorting ensures the selection of superior individuals after crossover and mutation. The effectiveness of the proposed ONSGA has been validated through extensive experiments on newly developed test instances and using an industrial case study from the ship engine compartment. The results also indicate that the AFJSP-ER approach effectively optimizes productivity and promotes ergonomic relief, offering a practical solution for scheduling in ergonomically challenging marine environments.

关键词： Maintenance scheduling Extreme working conditions Ergonomics Rapid entire body assessment Job-shop scheduling non-dominated sorting genetic algorithm-ii

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Optimization of large-area photovoltaic module frames for lightweight design using structural analysis and non-dominated sorting genetic algorithm ii

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SOLAR ENERGY MATERIALS AND SOLAR CELLS 2025年 285卷

作者： Noh, Yohan Kim, Junghoon Jeong, Chaehwan Lee, Jaehyeong Sungkyunkwan Univ Dept Elect & Comp Engn Suwon 16419 South Korea TOPSUN Co Ltd Jangseong 57241 South Korea Korea Inst Ind Technol Green Energy& Nano Technol R&D Grp Gwangju 61012 South Korea

Although solar power is commonly considered a green energy source that can reduce carbon emissions, it emits a significant amount of carbon in its entirety, from the production of components and equipment to their use and disposal. In particular, frames that protect the edges of photovoltaic (PV) modules and provide mechanical strength to facilitate installation use aluminum as the primary material, which is produced by electrolyzing alumina derived from bauxite. This process produces a large amount of carbon dioxide: 11.3 kg per kilogram of aluminum. As research on large-area PV modules to achieve high power and efficiency progresses, the use of components such as cover glass and aluminum frames and the weight of PV modules increase;thus, it is difficult to construct and maintain mechanical properties that can withstand external forces such as wind and snow loads. Therefore, complex research is required, including the design of lightweight frames that can secure the mechanical properties of large-area PV modules while reducing the amount of aluminum used. In this study, we used the non-dominated sorting genetic algorithm-ii (NSGA-ii), a meta-heuristic optimization technique, and structural analysis simulation to design a lightweight frame model with mechanical strength similar to that of existing commercial frames while using less aluminum to reduce carbon emissions. We applied the proposed frame to a large-area PV module and compared its mechanical properties with those of a PV module with a commercial frame through mechanical load tests. Consequently, we present a frame model with mechanical properties similar to those of an existing PV module under mechanical loads in the range of 2400-7200 Pa while reducing the cross-sectional area by 10.13 %.

关键词： Photovoltaic module frame structure design Mechanical reliability Structural analysis Lightweight structure design non-dominated sorting genetic algorithm-ii

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non-dominated sorting genetic algorithm-ii for possibilistic mean-semiabsolute deviation-Yager entropy portfolio model with complex real-world constraints

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MATHEMATICS AND COMPUTERS IN SIMULATION 2022年 202卷 59-78页

作者： Deng, Xue Chen, Jiaxing Wang, Xu Geng, Fengting South China Univ Technol Sch Math Guangzhou Peoples R China South China Univ Technol Sch Math Guangzhou 510610 Peoples R China

The purpose of our paper is to address the multi-objective portfolio model with complex real-world constraints under the assumption that the returns of risky assets are fuzzy variables. Firstly, a new possibilistic mean-semiabsolute deviationYager entropy portfolio model is proposed with transaction costs, cardinality and quantity constraints Secondly, to solve the proposed model efficiently, a non-dominated sorting genetic algorithm-ii (NSGA-ii) is presented, which can not only reduce the computational complexity but also enhance the solution accuracy. Then, a numerical example is provided to verify the feasibility and effectiveness of our proposed model and algorithm. Based on these results, we analyze the efficient frontiers with different quantity constraints and transaction costs, and illustrate the portfolio distributions with different transaction costs by using the boxplot figures. Finally, these solutions solved by NSGA-ii and four traditional computation methods are compared. Our proposed algorithm outperforms the minimax method (Polakabb, 2010), two-stage method (Masson, 2016), extended two-stage method (Li, 2012) and compromise approach-based genetic algorithm (Li, 2013) in the efficient frontier, accuracy and number of solutions. (c) 2022 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

关键词： Portfolio Entropy Multi-objective programming non-dominated sorting genetic algorithm-ii Cardinality constraint

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A new integrated remanufacturing process planning and scheduling model under uncertainties using extended non-dominated sorting genetic algorithm-ii

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2024年第1期46卷 2123-2145页

作者： Xu, Huifen Fang, Cheng Zhang, Shuai Zhejiang Univ Finance & Econ Sch Informat Management & Artificial Intelligence Hangzhou Peoples R China Zhejiang Univ Finance & Econ Sch Data Sci Hangzhou 310018 Peoples R China

Remanufacturing, with its environmental and economic implications, is gaining significant traction in the contemporary industry. Owing to the complementarity between remanufacturing process planning and scheduling in actual remanufacturing systems, the integrated remanufacturing process planning and scheduling (IRPPS) model provides researchers and practitioners with a favorable direction to improve the performance of remanufacturing systems. However, a comprehensive exploration of the IRPPS model under uncertainties has remained scant, largely attributable to the high complexity stemming from the intrinsic uncertainties of the remanufacturing environment. To address the above challenge, this study proposes a new IRPPS model that operates under such uncertainties. Specifically, the proposed model utilizes interval numbers to represent the uncertainty of processing time and develops a process planning approach that integrates various failure modes to effectively address the uncertain quality of defective parts during the remanufacturing process. To facilitate the resolution of the proposed model, this study proposes an extended non-dominated sorting genetic algorithm-ii with a new multi-dimensional representation scheme, in which, a new self-adaptive strategy, multiple genetic operators, and a new local search strategy are integrated to improve the algorithmic performance. The simulation experiments results demonstrate the superiority of the proposed algorithm over three other baseline multi-objective evolutionary algorithms.

关键词： Integrated remanufacturing process planning and scheduling remanufacturing systems uncertainty environment interval processing time non-dominated sorting genetic algorithm-ii

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Hydraulic optimization simulation for reducing confluence and controlling the overflow pollution of storage ponds based on the Storm Water Management Model and non-dominated sorting genetic algorithm-ii

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AQUA-WATER INFRASTRUCTURE ECOSYSTEMS AND SOCIETY 2023年第7期72卷 1130-1144页

作者： Jin, Cuntian CCCC HIGHWAY CONSULTANTS CO LTD Beijing 100010 Peoples R China

The Storm Water Management Model (SWMM) was established to simulate rainfall-runoff dynamically, and the internal runoff component of the SWMM was used to simulate rainfall operation in each watershed, including rainfall-runoff and scour pollution load. Then, using the routing component in the SWMM, the properties of runoff into the tank system are calculated through pipelines and other facilities to obtain the optimal tank volume. The coupling optimization model was established, and the algebraic function of the storage capacity, total runoff, and total cost was established by using the multiple linear regression method, which was transformed into the optimization model aiming at the minimum total runoff and total cost. The NSGA-ii is improved by using a reverse learning mechanism. By solving the optimization model, the non-dominant solution of the proxy model is obtained. The non-dominant solution was substituted into the SWMM, and the rationality of the optimization results was analyzed. The experimental results show that the reservoir volume determined by this method can effectively accept the pollutants brought by the initial rain, so as to reduce the hydraulic pollution caused by the confluence overflow and the overflow pollution of the urban integrated pipe network.

关键词： cut down non-dominated sorting genetic algorithm-ii overflow pollution storage tank Storm Water Management Model

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Multi-objective optimization of combustion, performance and emission parameters in a jatropha biodiesel engine using non-dominated sorting genetic algorithm-ii

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Frontiers of Mechanical Engineering 2014年第1期9卷 81-94页

作者： Sunil Dhingra Gian Bhushan Kashyap Kumar Dubey Department of Mechanical Engineering University Institute of Engi-neering and Technology Kurukshetra University Kttnakshetra Department of Mechanical Engineering National Institute of Technolo-gy Kurukshetra Department of Biotechnology University Institute of Engineering andTechnology Maharshi Dayanand University Rohtak

The present work studies and identifies the different variables that affect the output parameters involved in a single cylinder direct injection compression ignition （CI） engine using jatropha biodiesel. Response surface methodology based on Central composite design （CCD） is used to design the experiments. Mathematical models are developed for combustion parameters （Brake specific fuel consumption （BSFC） and peak cylinder pressure （Pmax））, performance parameter brake thermal efficiency （BTE） and emission parameters （CO, NOx, unburnt HC and smoke） using regression techniques. These regression equations are further utilized for simultaneous optimization of combustion （BSFC, Pmax）, performance （BTE） and emission （CO, NOx, HC, smoke） parameters. As the objective is to maximize BTE and minimize BSFC, Pmax, CO, NOx, HC, smoke, a multi- objective optimization problem is formulated. non- dominated sorting genetic algorithm-ii is used in predict- ing the Pareto optimal sets of solution. Experiments are performed at suitable optimal solutions for predicting the combustion, performance and emission parameters to check the adequacy of the proposed model. The Pareto optimal sets of solution can be used as guidelines for the end users to select optimal combination of engine outputand emission parameters depending upon their own requirements.

关键词： jatropha biodiesel fuel properties responsesurface methodology multi-objective optimization non-dominated sorting genetic algorithm-ii

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non-dominated sorting genetic algorithm-ii-Induced Neural-Supported Prediction of Water Quality with Stability Analysis

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JOURNAL OF INFORMATION & KNOWLEDGE MANAGEMENT 2018年第2期17卷 20-20页

作者： Chatterjee, Sankhadeep Sarkar, Sarbartha Dey, Nilanjan Sen, Soumya Univ Calcutta Dept Comp Sci & Engn Kolkata India Indian Sch Mines Indian Inst Technol Dept Min Engn Dhanbad Bihar India Techno India Coll Technol Dept Informat Technol Kolkata India Univ Calcutta AK Choudhury Sch Informat Technol Kolkata India

Water is one of the most important necessities for human survival. In municipal corporation areas, water quality affects a large part of the population. Good quality water supply is an imperative parameter that influences individuals' health. Automated accurate water quality determination becomes an urgent necessity. Detecting the drinking water quality can prevent such scenarios prior to the critical stage. Recent research works have achieved reasonable success in predicting the water quality by deploying several machine learning-based techniques and utilising different aspects to analyse water quality. The accuracy levels of already proposed models are to be improved, keeping in mind the sensitivity of the problem domain. In the current work, non-dominated sorting genetic algorithm-ii (NN-NSGA-ii) was employed to train the artificial neural network (ANN) to improve its performance over its traditional counterparts. The proposed model gradually minimises two different objective functions, namely the root mean square error (RMSE) and Maximum Error (ME) in order to find the optimal weight vector for the ANN. The proposed model was compared with another two well-established models namely ANN trained with genetic algorithm (NN-GA) and ANN trained with Particle Swarm Optimisation (NN-PSO) in terms of accuracy, precision, recall, F-Measure, Matthews correlation coefficient (MCC) and Fowlkes-Mallows (FM) index. Furthermore, a data perturbation-based stability analysis is proposed to test the stability of the proposed method. The simulation results established superior accuracy of NN-NSGA-ii over the other models.

关键词： Artificial neural network genetic algorithm particle swarm optimisation non-dominated sorting genetic algorithm-ii water quality prediction

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Towards low-carbon cities: Patch-based multi-objective optimization of land use allocation using an improved non-dominated sorting genetic algorithm-ii

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ECOLOGICAL INDICATORS 2022年 134卷 108455-108455页

作者： Liu, Hongjiang Yan, Fengying Tian, Hua Tianjin Univ Sch Architecture Tianjin 300072 Peoples R China Tianjin Univ State Key Lab Engines Tianjin 300072 Peoples R China

The rational land use allocation is of great significance to the construction of low-carbon cities. The optimization model of land use allocation is an important tool that helps urban planners to quantitatively trade-off among the multi-objectives and achieve optimal land use schemes. For multi-objective optimization of low-carbon land use allocation, the models conducted by existing studies generally tend to be based on gridded data, lack of comprehensive consideration of quantitative and spatial objectives, and efficient algorithms to execute the optimization process. Therefore, this paper proposed a patch-based low carbon multi-objective land use allocation (LC-MLUA) optimization model involving both quantitative and spatial optimization targets. The LCMLUA optimization model was solved with an improved non-dominated sorting genetic algorithm-ii (NSGAii), and the weighted-sum method was used to make the final selection under different preferences. The LCMLUA optimization model was then applied to a case study of Changxing, a county-level city in east China, and there were three key results. (1) The LC-MLUA optimization model had a remarkable outperform of the land use allocation than the original land use plan, and the optimized values of economic benefit, emission reduction, and accessibility increased by 27.0%, 6.2% and 8.3%, respectively. (2) The LC-MLUA optimization model generated a series of optimal schemes to support suggestion-making for the low-carbon adjustment of the land use structure and spatial layout. (3) The LC-MLUA optimization model based on vector land patch data was proved more efficient as the unit number was reduced by 5 times than gridded data and better reflected the land use planning practice. (4) Compared with other algorithms, the improved NSGA-ii had better performance in the number of solutions, target optimization rate, and comprehensive performance. Based on these results, it suggests that the patch-based LC-MLUA optimization model

关键词： Low carbon city Land use allocation Multi-objective optimization Land patch non-dominated sorting genetic algorithm-ii

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A non-dominated sorting genetic algorithm-ii-based approach to optimize the spectral and spatial quality of component substitution-based pansharpened images

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2021年第5期33卷 e6030-e6030页

作者： Yilmaz, Volkan Artvin Coruh Univ Dept Geomat TR-08100 Artvin Turkey

Pansharpening aims to fuse a lower-resolution multispectral (MS) image and a higher-resolution panchromatic image, resulting in an image with the color quality of the former and spatial detail quality of the latter. Of all, the component substitution (CS)-based pansharpening methods have drawn attentions with their ability to produce sharp images. Despite their success in sharpening the images, these methods deteriorate the color features of the input MS images due of the uncertainty in the calculation of the intensity component used by them. Previous studies showed that attempts to preserve the color features tend to cause spatial detail loss to a certain extent. This, of course, reveals the necessity of a compromise between the spectral and spatial fidelity of the pansharpened images produced by the CS-based techniques. This study proposed using the multi-objective non-dominated sorting genetic algorithm-ii metaheuristic algorithm with the CS-based methods to optimize the intensity component to find the best compromise between the spectral and spatial fidelity of the pansharpened images. The proposed framework was applied on two commonly used pansharpening techniques, Gram-Schmidt and Synthetic Variable Ratio. It was found that the proposed methods managed to find the best balance between the color and spatial fidelity.

关键词： Gram-Schmidt image fusion multi-objective metaheuristic optimization non-dominated sorting genetic algorithm-ii pansharpening synthetic variable ratio

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