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Energy, exergy, economy analysis and multi-objective optimization of SOFC/GT/SCO2 hybrid power system for ships based on zero-carbon emissions

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2025年 98卷 1430-1449页

作者： Xia, Minjie Li, Chen Yao, Shouguang Yan, Xuan Wang, Chuang Jiangsu Univ Sci & Technol Sch Energy & Power Zhenjiang 212000 Peoples R China Southeast Univ Sch Energy & Environm Nanjing 210096 Peoples R China Wuhan Univ Technol Sch Traff & Transportat Engn Wuhan 430063 Peoples R China

LNG-powered ships have gradually become the mainstream of green shipping because of the high efficiency and emissions reduction, but cannot realize zero-carbon emissions operation. Therefore, a SOFC/GT/SCO2 hybrid power system for ships based on oxygen-enriched combustion carbon capture technology is proposed to achieve efficient and zero-carbon emissions operation for ships. The system is evaluated based on the thermoeconomic method, the effects of system operating parameters on system performance are investigated, and the system is optimized based on parametric analysis utilizing a multi-objective genetic algorithm. The results demonstrate that the higher operating temperature of the reformer increases the system net output power and reduces the system electricity production cost. The system net output power is maximized and the electricity production cost is minimized when the operating temperature of SOFC is 800 degrees C. For the SCO2 cycle, the selection of appropriate operating parameters is essential for the efficient operation of the system. The system achieves 7276.182 kW net power generation, 63.00% exergy efficiency, and 0.0774$/kWh electricity production cost after multi-objective optimization.

关键词： multi-objective genetic algorithm Thermoeconomic method Oxygen-enriched combustion carbon capture technology LNG-Powered ship SOFC/GT/SCO2 hybrid power system

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A Fault-Tolerant Scheduling algorithm Based on a multi-objective genetic algorithm for Precedence-Constrained Tasks in Real-Time Heterogeneous Distributed Systems

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JOURNAL OF COMPUTATIONAL AND THEORETICAL NANOSCIENCE 2013年第5期10卷 1091-1096页

作者： Chen Yuanlong Ma Peijun Li Dong Harbin Inst Technol Sch Comp Sci & Technol Harbin 150001 Peoples R China

In this paper, we investigate an efficient off-line fault-tolerant scheduling algorithm that generates schedules in which real-time tasks with precedence constraints can tolerate more than one fault at a time in a heterogeneous system with a fully connected network. We assume tasks to be non-preemptable, and each task has two copies scheduled on different processors and mutually excluded. Our fault-tolerant scheduling algorithm based on non-domination sort genetic algorithm (NSGA) employs performance improving techniques, including (1) a dynamic distance concept that decides the relative position of two task copies in the task graph, (2) a multi objectives genetic algorithm to optimise multiple targets. We performed experiments using synthetic workloads and a real-world application. Simulation results show that compared with existing scheduling algorithms, our scheduling algorithm improves guarantee ratio, reliability and overhead.

关键词： Heterogeneous Distributed System Dynamical Distance Fault Tolerant Scheduling multi-objective genetic algorithm

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SENNA: Unified Hardware/Software Space Exploration for Parametrizable Neural Network Accelerators

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ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS 2025年第2期24卷 1-26页

作者： Kwon, Jungyoon Min, Yemi Egger, Bernhard Seoul Natl Univ Dept Comp Sci & Engn Seoul South Korea

Parametrizable neural network accelerators enable the deployment of targeted hardware for specialized environments. Finding the best architecture configuration for a given specification, however, is challenging. A large number of hardware configurations have to be considered, and for each hardware instance, an efficient software execution plan needs to be found, leading to a vast search space. Prior work has tackled this problem by dividing the search into subproblems for individual layers of a network. There is no guarantee, however, that the overall best hardware configuration that delivers the desired end-to-end performance across the entire network is among the best individual layer configurations. This work presents SENNA, a unified hardware/software space exploration framework for parametrizable neural network accelerators. To guide the exploration toward the overall best configuration, SENNA employs a multi-objective genetic algorithm with a novel design space representation that encodes the configuration of hardware and software parameters in a single chromosome. Using the Parallel Island Model (PIM), each layer is represented by one or more individual islands each containing a separate population to simultaneously search for the best configuration across the entire network. A tailored gene migration technique enables the exchange of genes between the populations of different islands. SENNA is evaluated with three parametrizable architectures and four neural networks. The evaluation result demonstrates that SENNA achieves upto 1.92x EDP improvement compared to the State-of-the-Art. With equivalent evaluation budgets, SENNA shows 2.5x-9.3x speedup compared to an Oracle scheme and the State-of-the-Art.

关键词： HW/SW co-design reconfigurable accelerators design space exploration multi-objective genetic algorithm parallel island model

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Solar Spectrum Simulation algorithms Considering AM0G and AM1.5G

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SENSORS 2025年第5期25卷 1406-1406页

作者： Yang, Junjie Zhang, Guoyu Zhao, Bin Yang, Dongpeng Zhang, Ke Zhang, Yu Zhang, Jian Ren, Zhengwei Sun, Jingrui Wang, Lu Mo, Xiaoxu Ren, Taiyang Ren, Dianwu Peng, Zeng Yang, Songzhou Lv, Jiabo Changchun Univ Sci & Technol Sch Optoelect Engn Changchun 130022 Peoples R China Jilin Optoelect Measurement & Control Instrument E Changchun 130022 Peoples R China Minist Educ Key Lab Optoelect Measurement & Control & Opt Info Changchun 130022 Peoples R China Changchun Univ Sci & Technol State Key Lab High Power Semicond Laser Changchun 130022 Peoples R China Changchun Univ Sci & Technol Sch Artificial Intelligence Changchun 130022 Peoples R China Jilin Inst Metrol Jilin Prov Key Lab Measuring Instrument & Technol Changchun 130022 Peoples R China

LED solar simulators currently face limitations in their spectral simulation capabilities, especially in terms of accurately incorporating AM0G and AM1.5G solar spectra. To this end, this study introduced a framework for an LED solar spectrum simulation algorithm that considers both AM0G and AM1.5G. This study examined the principle of solar spectrum discretization and reconstruction, established a foundation for analyzing the quality of solar spectrum reconstruction, and developed a non-dominated sorting genetic algorithm II (NSGA-II)-assisted long short-term memory (LSTM)-based solar spectrum simulation strategy. This strategy integrates a multi-objective genetic algorithm to generate training datasets and a neural network for solar spectrum simulation. A dataset generation method using the NSGA-II algorithm was implemented, which leveraged the 6500 K standard blackbody spectral curve, the spectral curve offset coefficients, and the spectral distributions of various narrowband LEDs. An LSTM-based neural network for solar spectrum simulation was developed, with the RMSE serving as the evaluation function. The analysis and selection of 29 narrowband LEDs produced 5000 solar spectrum simulation training datasets. The trained LSTM model achieved spectral matching accuracies within +/- 10.5% and +/- 9.3% for AM0G and AM1.5G, respectively, meeting the A+ level simulation standard for solar spectrum reconstruction considering AM0G and AM1.5G. These findings provide a theoretical foundation and technical advancements for high-precision solar spectrum reconstruction, which has practical implications for improving the efficiency and accuracy of solar energy systems, as well as supporting further research on solar spectrum utilization, and is expected to influence the development of more efficient solar simulators.

关键词： LED solar simulator multi-objective genetic algorithm neural network solar spectrum spectral simulation

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Dynamic modeling method and parameter calibration of subarea thin-layer elements in bolted connection area of diaphragm coupling

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PHYSICA SCRIPTA 2025年第3期100卷 036008-036008页

作者： Wen, Huabing Gao, Shuaishuai Jiangsu Univ Sci & Technol Sch Energy & Power Zhenjiang 212100 Peoples R China

Aiming at the finite element modeling problem on the dynamic characteristics of multi-layer elastic diaphragm bolted joints, and adopting the equivalent modeling method of subarea thin-layer elements, a parameter identification method that can accurately and efficiently identify the material parameters of each area of subarea thin-layer elements is proposed. Taking the diaphragm coupling in the marine propulsion shaft system as an example, the accuracy of the simplified model is verified by comparing the dynamics characteristics of the vibrational mode, driving point mechanical impedance, and vibration level drop between test and simulation. The variation of material parameters in each region under different bolt preloads was studied, and the effects of different material parameters on the dynamic properties were analyzed, in which the elastic modulus had a greater effect on the model dynamic characteristics, while Poisson's ratio and density had a smaller effect. The research results show that the subarea thin-layer element modeling method can effectively simplify the dynamic modeling method of the diaphragm coupling under the consideration of bolt preload, and the parameter identification based on the multi-objective genetic algorithm can accurately identify the material parameters of each area of the subarea thin-layer element, which can be used to analyze the dynamic characteristics of the multilayered elastic diaphragm bolted couplings.

关键词： subarea thin-layer element diaphragm coupling dynamic characteristics parameter identification multi-objective genetic algorithm

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Joint optimization of energy consumption and latency for task offloading in cloud-edge collaboration system

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PHYSICAL COMMUNICATION 2025年 70卷

作者： Jiang, Xue Dou, Haie Wang, Lei Xia, Zhijie Nanjing Univ Posts & Telecommun Sch Internet Things Nanjing Peoples R China Nanjing Univ Posts & Telecommun Jiangsu Engn Res Ctr Commun & Network Technol Nanjing Peoples R China Nanjing Univ Posts & Telecommun Key Lab Broadband Wireless Commun & Sensor Network Nanjing Peoples R China

Ina cloud-edge collaboration system, ensuring consistent wireless connectivity that meets the quality of service (QoS) for users with varying requirements is challenging. Therefore, the approach to task offloading for all mobile devices (MDs) should be carefully designed to enhance performance based on different requirements. This paper introduces a novel multi-objective genetic algorithm (MOGA) to address the task offloading problem, considering minimal energy consumption and latency simultaneously. Experimental results demonstrate that the proposed multi-objective genetic algorithm achieves rapid convergence and optimizes delay and energy consumption.

关键词： Cloud-edge collaborative Task offloading multi-objective genetic algorithm

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Efficient risk-based inspection framework: Balancing safety and budgetary constraints

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2025年 253卷

作者： Javid, Y. Kharazmi Univ Engn Fac Ind Engn Dept Tehran Iran

Efficient equipment maintenance is paramount across various industries to mitigate energy wastage and avert potential disasters such as hazardous emissions, fires, and explosions. Within this context, the adoption of risk- based inspection strategies has emerged as a crucial method for assessing equipment integrity. This study integrates the principles of Risk-Based Inspection (RBI) with a novel two-objective mathematical model, resulting in a comprehensive framework for equipment inspection programs. The primary aim of this framework is to reduce overall risk exposure while optimizing inspection expenditures. Unlike conventional approaches, this methodology eliminates the necessity to define threshold risk levels. By integrating inspection costs into the model, the assessment of Failure Consequences, and thereby, the decision-making process has been streamlined. This innovative algorithm effectively balances the reduction of failure likelihood with the minimization of inspection costs, enhancing decision-making capabilities. Importantly, this approach offers significant protection against energy wastage and the occurrence of leaks through robust risk management strategies. The algorithm employs specialized operators to expedite the discovery of optimal solutions. Empirical validation through a case study conducted at a Petrochemical Plant highlights the practicality and effectiveness of the proposed framework.

关键词： Inspection program Risk-based inspection multi-objective genetic algorithm Risk reduction

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A resource pre-allocation method for cognitive analytics-based social media service in edge computing

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WIRELESS NETWORKS 2024年第6期30卷 6135-6150页

作者： Wang, Ruizhi Zhang, Difei Shanxi Agr Univ Jinzhong 030801 Peoples R China Qufu Normal Univ Sch Math Sci Qufu 273165 Peoples R China

With the rapid development of 5G mobile communication technology, the growing demand for media services in society is becoming a characteristic of smart cities. Technical study has exhaustively investigated the low-latency resource supply of edge computing systems for web hosting services. However, external variables (such as transmission and network delays) impact the transmission between edge servers and service requests, resulting in service request delays. In addition, resource service requests that are constantly updated and in dynamic distribution may overload some servers while others are idle, resulting in poor load balancing. Consequently, this research presents a Resource Pre-Allocation method (RPA) for cognitive analytics-based social media platforms, which aims to improve the load balancing of edge servers while serving requests with strict latency requirements, so as to obtain the optimal resource allocation strategy. First, the resource requirement prediction algorithm is developed based on temporal-spatial demand history. Then, we propose a multi-objective algorithm combined with the optimal solution selection techniques to obtain the ideal resource allocation decisions. Finally, the performance of RPA is tested and evaluated. The experimental results show that RPA can allocate resources better than other methods.

关键词： 5G edge computing Resource requirement prediction Resource pre-allocation multi-objective genetic algorithm Optimal solution selection

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Optimization of Residential Demand Response Program Cost With Consideration for Occupants Thermal Comfort and Privacy

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IEEE ACCESS 2024年 12卷 15194-15207页

作者： Nematirad, Reza Ardehali, M. M. Khorsandi, Amir Mahmoudian, Arash Kansas State Univ Elect & Comp Engn Dept Manhattan KS 66506 USA Amirkabir Univ Technol Tehran Polytech Dept Elect Engn Tehran *** Iran Bellevue Univ Coll Sci & Technol Bellevue NE 68005 USA

Residential consumers can optimize their participation in demand response programs (DRPs) using home energy management systems (HEMS). By automatically adjusting air conditioning (AC) setpoints and shifting certain appliances to off-peak hours, HEMS can lead to significant cost reductions. While HEMS aims to adjust AC temperature setpoints, it is important to consider the occupants thermal comfort. This study aims to develop a multi-objective model for the application of DRPs in a smart residential house. The objectives of the model are to achieve (a) reduction in electrical load demand, (b) adjustment in thermal comfort temperature setpoints, and (c) minimization of consumer costs subject to the related constraints. Determining occupancy status through HEMS provides more economic benefits and thermal comfort for consumers. However, traditional methods such as direct occupancy monitoring are often costly, inaccurate, and can intrusively collect data on residents' activities, locations, and routines, compromising their privacy. To tackle these challenges, this study introduces advanced forecasting algorithms such as random forest, light gradient boosting machine, and multilayer perception artificial neural networks to predict occupancy by utilizing indirect data sources, such as energy consumption patterns. This approach enables the prediction of residential presence without direct monitoring. However, inherent uncertainty associated with predicted parameters can compromise the effectiveness of DRPs, and potentially lead to non-optimal energy savings, jeopardizing consumer comfort, and even system instability. To address these uncertainties, this study integrates robust counterpart optimization techniques, augmented with uncertainty budgets to control uncertainty variation making them less conservative. Simulations show uncertainty increases costs by 36% and reduces AC temperature setpoints. Further, implementing DRPs reduces demand by shifting some appliances to off

关键词： Demand response program thermal comfort occupant privacy occupancy forecasting learning algorithms robust counterpart optimization multi-objective genetic algorithm

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Through hole-cutting conic posture optimization for a redundant 3D laser cutting machine

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2024年第1-2期132卷 443-461页

作者： Ding, Zhipeng Soccio, Pietro Indri, Marina Rizzo, Alessandro Politecn Torino Dept Elect & Telecommun Corso Duca Abruzzi 24 I-10129 Turin Italy Eft EUROPE Srl Corso Duca Abruzzi 2 I-10128 Turin Italy

Productively reducing the time required to cut numerous through holes in automotive workpieces is crucial for enhancing parts manufacturing in the 3D laser cutting process. However, the conventional cutting strategy, in which the laser beam maintains a stationary posture along the hole path, lacks flexibility and fails to effectively leverage processing tolerances. In this study, we conduct a thorough analysis of the kinematics of a six-axis redundant laser cutting machine and resolve through a decoupling method with singularity management. We propose an innovative conic posture cutting strategy for 3D laser hole-cutting with thin materials. This approach adopts the geometry of a cone as the posture while cutting the hole path. In order to obtain the optimal vertex of the cone while minimizing the taper error generated by the conic posture and kinetic energy consumption of the actuators during motion, we formulate a multi-objective optimization problem and solve it using a genetic algorithm. Furthermore, we enhance the optimization by adopting a time minimization approach. Through the implementation of a B-pillar workpiece cutting experiment, we have successfully validated the credibility of our proposed cutting strategy, thereby demonstrating an enhancement of time on 26 hole-cutting paths.

关键词： 3D laser cutting Through hole-cutting Redundant kinematics Posture optimization multi-objective genetic algorithm

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