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Multiobjective Optimization of CO2 Emission and Net Profit for a Naphtha Cracking Furnace Using a Deep Neural Network with a nondominated sorting genetic algorithm

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ACS SUSTAINABLE CHEMISTRY & ENGINEERING 2024年第7期12卷 2841-2851页

作者： Joo, Chonghyo Kwon, Hyukwon Lim, Jonghun Lee, Jaewon Kim, Junghwan Korea Inst Ind Technol Green Mat & Proc R&D Grp Ulsan 44413 South Korea Yonsei Univ Dept Chem & Biomol Engn Seoul 03722 South Korea

A naphtha-cracking furnace converts naphtha to ethylene (EL) and propylene (PL);the yields depend on the coil outlet temperature (COT) and naphtha composition. However, determining the optimal COT for maximizing net profit is difficult because the product price and its composition fluctuate frequently. Moreover, CO2 emissions increase inevitably with increasing net profit, which requires taking environmental aspects into account. Hence, this study proposes a multiobjective optimization model for the naphtha cracking furnace by considering the incompatible goals: maximization of net profit and minimization of CO2 emissions. First, a deep neural network (DNN)-based model is developed to predict the EL yield, PL yield, and CO2 emissions for a given COT and naphtha composition using 783 industrial data points. Second, the developed model is combined with a nondominated sorting genetic algorithm (NSGA-II) for multiobjective optimization to obtain a Pareto front with various solutions. Finally, case studies are conducted for different product prices: EL was more expensive than PL in 2018;PL was more expensive than EL in 2019;and EL and PL had similar prices in 2020. For these three cases, the actual industrial data are applied to the model, and various solutions are proposed. The representative solutions in each case exhibit 5.35-6.14% higher net profits and 12.81-15.34% lower CO2 emissions than those of the industrial data. The proposed model can help decision-makers by providing flexible options for the modification of various production parameters, including environmental regulations.

关键词： naphtha cracking furnace CO2 emission multiobjective optimization deep neural network nondominated sorting genetic algorithm

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Shape optimization of egg-shaped sewer pipes based on the nondominated sorting genetic algorithm (NSGA-II)

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ENVIRONMENTAL RESEARCH 2022年第PartA期204卷 111999-111999页

作者： Wu, Hanting Huang, Yangrui Chen, Lei Zhu, Yingjie Li, Huaizheng Tongji Univ Coll Environm Sci & Engn Shanghai 200092 Peoples R China Tongji Univ Key Lab Yangtze River Water Environm Minist Educ Shanghai 200092 Peoples R China Shanghai Inst Pollut Control & Ecol Secur Shanghai 200092 Peoples R China

Overflow pollution is an undesired issue that commonly occurs in combined sewers under wet weather conditions. There is a lack of existing studies on the structural optimization of sewers to prevent siltation, and no previous study on egg-shaped sewers with this purpose has confirmed satisfactory anti-sedimentation performance. To achieve reduced sedimentation and lower energy loss under low- and high-flow conditions, respectively, the nondominated sorting genetic algorithm (NSGA-II) was adopted in this study based on a constant full filling discharge capacity equal to that of a 300 mm (diameter) circular sewer. The results showed that eggshaped sewers with bottom and top arc radii of 58.3 and 116.6 mm, respectively, and a height of 408.1 mm performed significantly better than circular sewers (d = 300 mm). Notably, at a low flow ratio below 0.2, the shear stress of the optimized egg-shaped sewer was 5.2%-20.6% higher than that of the circular sewer. At a flow ratio of 0.2-0.6, both the egg-shaped and circular sewers were capable of maintaining a balanced amount of sediment between deposition and erosion. As the flow ratio increased to 0.6-1, both types of sewers completely scoured sediments: in this situation, the shear stress of the egg-shaped sewer was 5.5%-10.1% lower than that of the circular sewer, thus exhibiting reduced energy loss. This study indicates that egg-shaped sewers have an attractive future in replacing circular sewers for sedimentation prevention and cost control.

关键词： Egg-shaped sewer Structural design optimization Shear stress Anti-sedimentation nondominated sorting genetic algorithm

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Identification of Factors Influencing Crash Severity for Electric Bicycle Using nondominated sorting genetic algorithm 1

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6th KES Annual International Conference on Smart Education and e-Learning (KES SEEL) / 2nd KES International Symposium on Smart Transportation Systems (KES-STS)

作者： Xu, Cheng Zhejiang Police Coll Dept Traff Management Engn Hangzhou 310053 Peoples R China Zhejiang Univ Inst Intelligent Transportat Syst Hangzhou 310058 Peoples R China

ISBN: (数字)9789811386831

ISBN: (纸本)9789811386831;9789811386824

Electric bicycles (E-bike) are one of the most important travel modes in China. In recent years, traffic accidents involving electric bicycles have increased year by year, and research on traffic safety risks of electric bicycles is particularly important. The key factor in obtaining traffic accidents involving electric bicycles is an important basis for the development of electric bicycle traffic management and the relevant policies. Therefore, based on the electric bicycle traffic accident in Hangzhou, this paper uses the nondominated sorting genetic algorithm II (NSGA-II) to study the key factors affecting the severity of electric bicycle accidents. The results show that the type of accident and the type of illegality are the two most important factors affecting the severity of electric bicycle accidents.

关键词： Crash severity Electric bicycle Influencing factor nondominated sorting genetic algorithm

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Performance analysis and comparison of multipump Raman and hybrid erbium-doped fiber amplifier plus Raman amplifiers using nondominated sorting genetic algorithm optimization

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OPTICAL ENGINEERING 2016年第8期55卷 086103-1-086103-7页

作者： Rocha, Helder R. de O. Beninca, Matheus O. L. Castellani, Carlos E. S. Pontes, Maria J. Segatto, Marcelo E. V. Silva, Jair A. L. Univ Fed Espirito Santo North Univ Ctr Espirito Santo Rodovia BR 101 Norte BR-29932540 Sao Mateus ES Brazil Univ Fed Espirito Santo Telecommun Lab Ave Ferrari 514 BR-29075910 Vitoria ES Brazil

This paper presents a performance analysis and comparison of optimized multipump Raman and hybrid erbium-doped fiber amplifier (EDFA) + Raman amplifiers, operating simultaneously at conventional (C) and long (L) bands, using multiobjective optimization based on evolutionary elitist nondominated sorting genetic algorithm. The amplifiers performance was measured in terms of on-off gain, ripple, optical signal-to-noise ratio (OSNR) and noise figure (NF), after propagating over 90 and 180 km of single-mode fiber (SMF). Numerical simulation results of the first analysis show that only three pumps are necessary to generate optimal gains in both amplifiers. Comparing the results of the second performance analysis, we conclude that, after 90 km SMF, the two amplifiers has the same on-off gain, if the total pump power (1807.1 mW) of the Raman amplifier is approximately double (100 + 994.7 mW) of the hybrid amplifier, when the EDFA is operating at 1480 nm with 5 m of doped fiber. Furthermore, the Raman amplifier needs a single laser with at most 741.1 mW, against 343.9 mW of the distributed Raman amplifier (DRA) pump in the hybrid system. Finally, the results of the last analysis, which considers only the EDFA + Raman amplifier, shows that with on-off gain of 26.14 dB, ripple close to 1.54 dB over a bandwidth of 66 nm and using three pumps lasers in the DRA the achieved OSNR was 39.6 dB with an NF lower than 3.3 dB, after 90 km of SMF. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： Raman amplifier hybrid erbium-doped fiber amplifier plus Raman amplifier multiobjective optimization nondominated sorting genetic algorithm

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A Fast nondominated sorting Guided genetic algorithm for Multi-Objective Power Distribution System Reconfiguration Problem

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IEEE TRANSACTIONS ON POWER SYSTEMS 2015年第2期30卷 593-601页

作者： Eldurssi, Awad M. O'Connell, Robert M. Benghazi Univ Dept Elect & Comp Engn Qar Yunis Benghazi Libya Univ Missouri Dept Elect & Comp Engn Columbia MO 65211 USA

Distribution system reconfiguration (DSR) is a multi-objective, nonlinear problem. This paper introduces a new, fast, nondominated sorting genetic algorithm (FNSGA) for the purpose of solving the DSR problem in normal operation by satisfying all objectives simultaneously with a relatively small number of generations and relatively short computation time. The objectives of the problem are to minimize real power losses and improve the voltage profile and load balancing index with minimum switching operations. Instead of generating several ranks from the nondominated set of solutions, this algorithm deals with only one rank;then the most suitable solution is chosen according to the operator's wishes. If there is no preference and all objectives have the same degree of importance, the best solution is determined by simply considering the sum of the normalized objective values. Also, a guided mutation operation is applied instead of a random one to speed up convergence. Radial system topology is satisfied using graph theory by formulating the branch-bus incidence matrix (BBIM) and checking the rank of each topology. To test the algorithm, it was applied to two widely studied test systems and a real one. The results show the efficiency of this algorithm as compared to other methods in terms of achieving all the goals simultaneously with reasonable population and generation sizes and without using a mutation rate, which is usually problem-dependent.

关键词： Distribution system reconfiguration graph theory guided mutation multi-objective optimization nondominated sorting genetic algorithm

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Multiobjective optimization of slotted electrical discharge abrasive grinding of metal matrix composite using artificial neural network and nondominated sorting genetic algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2013年第10期227卷 1442-1452页

作者： Yadav, Ravindra Nath Yadava, Vinod Motilal Nehru Natl Inst Technol Dept Mech Engn Allahabad 211004 Uttar Pradesh India

The alternative use of electrical discharge grinding and abrasive grinding, which is applied with the application of slotted wheel named as slotted electrodischarge abrasive grinding, is much suitable for machining of metal matrix composites. But the selection of process parameters is a difficult task due to the complexity of the process. The aim of this study is to optimize the process parameters of slotted electrodischarge abrasive grinding process using a combined approach of artificial neural network and nondominated sorting genetic algorithm II. The artificial neural network architecture has been trained and tested with experimental data, and then the developed model is coupled with nondominated sorting genetic algorithm II to develop a hybrid approach of artificial neural network-nondominated sorting genetic algorithm II, which is used for optimization of process parameters. During experimentation, the effect of current, pulse on-time, pulse off-time, wheel speed and grit number has been studied on material removal rate and average surface roughness (Ra). The results have shown that prediction capability of artificial neural network model is within the range of acceptable limits. The developed hybrid approach of artificial neural network-nondominated sorting genetic algorithm II gives optimal solution with correlation coefficient of material removal rate and Ra as 0.9979 and 0.9982, respectively.

关键词： Slotted electrical discharge abrasive grinding hybrid metal matrix composites artificial neural network nondominated sorting genetic algorithm optimization

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A parallel mechanism-based virtual biomechanical shoulder robot model: Mechanism design optimization and motion planning

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MECHANICS BASED DESIGN OF STRUCTURES AND MACHINES 2025年第4期53卷 2744-2764页

作者： Shah, Muhammad Faizan Jamwal, Prashant K. Goecke, Roland Niyetkaliyev, Aibek S. Hussain, Shahid Univ Canberra Human Ctr Technol Res Ctr Sch Informat Technol & Syst Canberra ACT Australia Nazarbayev Univ Dept Elect & Comp Engn Astana Kazakhstan Univ New South Wales Sch Syst & Comp Canberra ACT Australia Nazarbayev Univ Sch Engn & Digital Sci Astana Kazakhstan

This paper presents the design and optimization process of a Virtual Biomechanical Shoulder Robot Model (VBSRM) based on a 6-4 parallel mechanism. To address the challenges posed by parallel manipulators and the specific biomechanical constraints of the shoulder joint, a comprehensive design analysis is conducted. First, a kinematic model of the VBSRM is developed, followed by an investigation of its geometric model. To obtain an optimal VBSRM design, performance objectives such as condition number, norm of actuator force, and stiffness are identified and optimized. Initially, only condition number of the robot mechanism is optimized using genetic algorithm and performance objectives from the optimal design are analyzed. Later, the three objectives are grouped to form a single function and a single objective-based optimization is also conducted. However, further investigation revealed the conflicting nature of the objectives and hence these were simultaneously optimized using the Non-dominated sorting genetic algorithm (NSGA II). The results obtained from various optimization routines are compared and it is found that the results from the NSGA II provide a better tradeoff between the performance objectives. The motion trajectories from the optimal design of the VBSRM are later analyzed vis-& agrave;-vis human shoulder motions for its intended use as a robotic model of the human shoulder joint in various applications.

关键词： Parallel robots shoulder condition number design optimization Multi-Objective optimization nondominated sorting genetic algorithm

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Multi-Objective Optimization for Robotaxi Dispatch With Safety-Carpooling Mode in Pandemic Era

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第1期26卷 878-891页

作者： Qi, Liang Li, Mengqi Guo, Xiwang Luan, Wenjing Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China Liaoning Petrochem Univ Coll Comp & Commun Engn Fushun 113001 Peoples R China

Autonomous driving has been successfully implemented in such particular areas as logistics distribution centers, container terminals, and university campuses. Robotaxi represents one of its important applications. This work studies a robotaxi dispatch problem during the pandemic era. It aims to design a robotaxi dispatch approach according to a defined severity degree of the pandemic, which can decrease a virus infection rate by reducing contact among passengers. It develops a multi-objective optimization model to minimize travel cost of robotaxis, waiting time of both robotaxis and passengers, and contact among passengers. A two-stage nondominated sorting genetic algorithm (NSGA-TS) is proposed to solve the problem. Three operations are designed to generate new solutions, which can ensure its solution diversity and speed up its convergence. Its effectiveness is verified via its comparison with two popular multi-objective optimization algorithms, i.e., multi-objective evolutionary algorithm based on decomposition (MOEA/D) and nondominated sorting genetic algorithm II (NSGA-II). Experimental results show that the proposed model can effectively reduce travel cost and waiting time. Besides, it can reduce the virus infection rate by decreasing contact among passengers at different severity degrees of the pandemic. This work is conducive for our society to building intelligent transportation systems in the post-pandemic era.

关键词： Robots Costs Public transportation Pandemics Optimization Optimization models Vehicles Shared transport genetic algorithms COVID-19 Autonomous driving robotaxi dispatch problem multi-objective optimization nondominated sorting genetic algorithm virus infection rate

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Optimization of RNN-LSTM Model Using NSGA-II algorithm for IOT-based Fire Detection Framework

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IETE JOURNAL OF RESEARCH 2024年第7期70卷 6239-6254页

作者： Roy, Tanushree Shome, Saikat kumar Govt India Cent Mech Engn Res Inst Minist Sci & Technol CSIR Durgapur 713209 India

The occurrence of fire leads to unparalleled loss of resources as well as human life and hence, fire detection systems must be trustworthy and less erroneous. Real-time assessment of fire conditions through predictive learning models could lead to easier decision-making and timely rescue operations. Reported works are often restricted to the use of singular rule-based algorithms which can hardly offer a comprehensive solution by adapting to changing dynamics of fire conditions due to their static features, mostly leading to inaccurate classification. In this research, an efficient fire prediction framework has been proposed by efficiently combining the outputs of recurrent neural networks which bear the advantages of short-term predictions with long short-term memory that takes care of long-term predictions. Weights to combine multi-objective optimization functions to minimize mean absolute error and root mean square error have been designed using non-dominated sorting genetic algorithm II (NSGA-II) which offers a lower time complexity. The benchmark dataset from the NIST website has been chosen for analysis and performance validation of the proposed classifier on experimental data pertaining to different fire scenarios. A Pareto optimal front has been obtained from the proposed algorithm which represents the optimum solutions. The performance of the proposed model has been exhaustively evaluated through different factors such as accuracy, RMSE, MAE, F-Measure, binary classification rate, negative predictive value, recall and precision which justifies its contribution. The proposed model reduced RMSE by 14.95-19.88% compared to baseline machine learning models along with an enhanced accuracy of 95.05% and reduced false positive rate which is better compared to reported works along with improvement in F-Measure. Results show that the proposed NSGA-II-based RNN LSTM model accurately predicts the occurrence of fire events with reduced false alarms while maintaining a l

关键词： Fire alarm Long short-term memory Multi-objective evolutionary algorithm nondominated sorting genetic algorithm Recurrent neural network Time series

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Selective disassembly sequence planning under uncertainty using trapezoidal fuzzy numbers: A novel hybrid metaheuristic algorithm

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年 128卷

作者： Zhang, Xuesong Fu, Anping Zhan, Changshu Pham, Duc Truong Zhao, Qiang Qiang, Tiangang Aljuaid, Mohammed Fu, Chenxi Northeast Forestry Univ Coll Mech & Elect Engn Harbin 150040 Peoples R China Harbin Inst Technol Sch Econ & Management Weihai 264200 Peoples R China Univ Birmingham Dept Mech Engn Birmingham B15 2TT England Northeast Forestry Univ Sch Civil Engn & Transportat Harbin 150040 Peoples R China King Saud Univ Coll Business Adm Dept Hlth Adm Riyadh Saudi Arabia Dalian Polytech Univ Sch Foreign Languages Dalian 116000 Peoples R China

Nowadays, recycling end-of-life (EoL) products has emerged as a vital approach to address resource scarcity. Within the recycling process, disassembly plays a pivotal role and has garnered substantial attention from researchers. Disassembly sequence planning (DSP) is a crucial method to enhance disassembly efficiency. Among the various DSP models, selective disassembly sequence planning (SDSP) has gained prominence as a means to save time and reduce costs. It empowers operators to locate specific components or materials in real time, thereby boosting efficiency and minimising resource wastage. However, a notable research gap exists in the domain of SDSP, particularly in uncertain environments. To bridge this gap and render SDSP solutions more practical for real-world disassembly operations, this study adopts trapezoidal fuzzy numbers to represent uncertain information within the disassembly process and formulates a comprehensive SDSP model. In response to the intricate challenges posed by this problem, we propose a hybrid approach termed nondominated sorting genetic algorithm-II with simulated large neighborhood search (NSGA-II-SLNS). This innovative algorithm leverages the strengths of the nondominated sorting genetic algorithm-II (NSGA-II), simulated annealing algorithm (SA), and large neighborhood search (LNS). Additionally, we introduce several novel search operators into NSGA-II-SLNS, including a crossover and mutation strategy based on chaotic mapping, as well as a local search operator founded on the SA criterion and LNS. To assess the effectiveness of the proposed algorithm and model, extensive numerical case studies are conducted in this research. The outcomes contribute to the advancement of rapid, nearly optimal SDSP strategies in the face of uncertainty and ambiguity in problem settings.

关键词： Selective disassembly sequence planning Trapezoidal fuzzy numbers Uncertain nondominated sorting genetic algorithm

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