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dual-objective optimization of solar-driven energy system for rural households in solar-rich areas

ENERGY 2025年 318卷

作者： Xu, Xinyin Yang, Liu Liu, Yan Cao, Qimeng Feng, Hengli Xian Univ Architecture & Technol Sch Architecture Xian 710055 Shaanxi Peoples R China Suzhou Univ Sci & Technol Sch Elect & Informat Engn Suzhou 215009 Jiangsu Peoples R China State Key Lab Green Bldg Xian 710055 Shaanxi Peoples R China

Poverty in terms of conventional energy but abundant renewable energy resources coincide in solar-rich areas, so the on-site supply of solar energy is essential for alleviating energy poverty and decarbonizing buildings. We propose a novel energy structure based on a solar-driven energy system combining battery and thermal storage under the power-to-heat concept. dual-objective optimization models were developed for PV-PT hybrid system and PV-dominant system to identify the optimal technological portfolio and design parameters with the aim of minimizing both the annual total cost (ATC) and carbon emission intensity (CEI), and the models were solved using the biologically-inspired algorithms. Considering a rural household in Lhasa as an example, the optimum design consists of 1.7 kW of photovoltaic (PV), 28.2 m2 of photothermal (PT), 4.8 kWh of battery, 1.7 m3 of thermal storage, and 3.2 kW of auxiliary heat source. The ATC is 44.5 % lower than that using conventional energy and CEI can be reduced by 94.8 %. Furthermore, the proposed system could achieve a CO2 reduction cost of 0.3 CNY & sdot;kg-1, compared with the two PV-dominant systems, i.e., 2.8 and 0.5 CNY & sdot;kg- 1 CO2, respectively. The proposed methodology can identify the optimal technology portfolio and balance environmental with economic.

关键词： Solar-driven energy system dual-objective optimization Biologically-inspired algorithms Carbon reduction cost Economy

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dual-objective optimization method for aeroengine multistage rotor stacking: concentricity and perpendicularity via deep Q-network

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MEASUREMENT SCIENCE AND TECHNOLOGY 2025年第6期36卷 065005-065005页

作者： Kang, Jia He, Jun Yang, Shixi Zhou, Chen Zhejiang Univ Sch Mech Engn State Key Lab Fluid Power & Mechatron Syst Hangzhou 310058 Peoples R China Zhejiang Univ Sch Mech Engn Key Lab Adv Mfg Technol Zhejiang Prov Hangzhou 310058 Peoples R China Zhejiang Univ Zhongyuan Inst Zhengzhou Peoples R China

The assembly precision of multistage rotors is crucial for the high-performance operation of aeroengines. Concentricity and perpendicularity are among the key indicators for evaluating assembly precision. Traditional trial-based assembly methods require multiple disassembly operations to meet precision requirements, which is cumbersome and may lead to component damage. This paper proposes a dual-objective optimization method to achieve concentricity and perpendicularity in aeroengine multistage rotor stacking based on a deep Q-network (DQN). First, a prediction model for concentricity and perpendicularity in the multistage rotor stacking process is established. This stacking process is subsequently formulated as a Markov decision process, and a dual-objective optimization method for multistage rotor stacking based on a DQN is proposed. Second, a six-stage rotor stacking process is simulated and an optimized stacking solution is obtained using the proposed method. Finally, a rotor stacking measurement platform is established to validate the optimization performance of the proposed method. Both simulated and experimental results show that the proposed method outperforms conventional methods (including the genetic algorithm method, the trial method, and the exhaustive method) in obtaining optimal assembly solutions that balance computation time with concentricity and perpendicularity, offering theoretical guidance and practical solutions for onetime high-precision assembly of aeroengine multistage rotors.

关键词： multistage rotor stacking deep Q-network concentricity perpendicularity dual-objective optimization aeroengine

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dual-objective optimization of a green closed-loop supply chain in steel industry considering quantity discount

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ANNALS OF OPERATIONS RESEARCH 2023年 1-27页

作者： Pan, Xiongfeng Guo, Shucen Dalian Univ Technol Sch Econ & Management Dalian 116024 Peoples R China

The steel industry is one of the main sources of CO2 emissions in China. China aims to incorporate carbon reduction into the whole supply chain management and regulate carbon emissions in the steel industry. Current research mostly focused on optimizing economic costs and carbon emissions in forward logistics, while reverse logistics have often been overlooked in terms of their economic costs and carbon emissions. The effect of economic scale, where unit cost decreases with the increase in quantity, is frequently overlooked in the closed-loop supply chain model too. This paper constructs a green closed-loop supply chain model with a dual-objective, aiming to incorporate both the economic cost and carbon emissions. The closed-loop model also considers the quantity discount affected by the scale effect in the model. This study combines the green closed-loop supply chain model with the quantity discount effect and discusses the impact of the model on decision-making. The model comprises four layers for forward logistics (suppliers, steel manufacturers, processing and distribution centers, and customers) and three layers for reverse logistics (customers, recycling and decomposition centers, and disposal center). The dual objective model calculates the total cost (including the cost of procurement, production, processing and distribution, recycling and decomposition, disposal, and transportation) and the total carbon emission (including the carbon emission of production, processing and distribution, recycling and decomposition, disposal, and transportation) of the green closed-loop supply chain network for the steel industry. In the cost model considering quantity discount, the unit production cost and unit transportation cost are designed to decrease as the quantity increases. The dual objective model is unified into a dimensionless membership single objective function using the comprehensive fuzzy analytic hierarchy process and fuzzy multi-objective linear programming.

关键词： Closed-loop supply chain Quantity discount Carbon reduction dual-objective optimization Steel industry

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Parameter analysis and dual-objective optimization of the hydraulic power take-off system of a floating wind-wave hybrid system

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OCEAN ENGINEERING 2024年 307卷

作者： Huang, Shuting Wang, Jun Yang, Mingyu Dong, Ge Wang, Yuanzhi Liu, Yanjun Shandong Univ Inst Marine Sci & Technol Qingdao 266200 Peoples R China Shandong Univ Shenzhen Res Inst Shenzhen 518057 Peoples R China China Yangtze Power Co Ltd Yichang 443133 Peoples R China Shandong Univ Sch Mech Engn Jinan 250061 Peoples R China Minist Educ Key Lab High Efficiency & Clean Mech Manufacture Jinan 250061 Peoples R China

The hydraulic power take-off (PTO) system of wave energy converter (WEC) is a key component of the floating wind-wave hybrid system. It has a notable influence not only on the overall power performance, but also on the motion response which represents wave stability and survivability. Its parameter analysis and optimization is essential, but is ignored in the previous studies. For this reason, this study established a fully-coupled mathematical model of the hybrid system with a complete hydraulic PTO system. Sensitive analysis of 6 hydraulic parameters on the power absorption and pitch motion of the hybrid system are conducted. A surrogate model based on elliptical basis functions neural network (EBFNN) is established to meet the computing needs. The optimization of hydraulic parameters is conducted by the NSGA-II multi-objective optimization algorithm with dual objectives: maximum power absorption and minimum pitch motion. The results show that 6 parameters has an interaction effect between each other. The throttle valve port flow area has the most significant effect on the power absorption, and the pre-charged volume of the accumulator has the most significant effect on the motion response. Four groups of optimal parameter values are recommended to provide reference for system design.

关键词： Wind-wave hybrid system Offshore wind Wave energy dual-objective optimization Elliptical basis functions neural network

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dual-objective optimization of solar driven alkaline electrolyzer system for on-site hydrogen production and storage: Current and future scenarios

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RENEWABLE ENERGY 2024年 237卷

作者： Tebibel, H. Ctr Dev Energies Renouvelables Route Observ Algiers 16340 Algeria

Achieving cost competitiveness of renewable hydrogen could accelerate the transition to the deeply decarbonized energy system. In this article, we develop and apply a dual-objective optimization model to explore the photovoltaic (PV) hydrogen production pathway and costs development from the present through 2050. The model is applied for optimal capacity allocation of the megawatt-scale off-grid PV-Hydrogen system to achieve maximum production at the minimal levelized cost of hydrogen (LCOH). Methodology includes a smoothing control strategy. Simulation is performed utilizing measured meteorological data for one year with hourly resolution and considering electrolyzer's load flexibility constraint. It has been found that the smoothing control strategy is indispensable for maximizing PV energy utilization, enhancing the electrolyzer's capacity factor and reducing the power curtailments. The analysis shows that the off-grid solar hydrogen in Algeria lacks economic competitiveness currently. Components CAPEX reduction turns out to be the fundamental condition towards the future LCOH decrease. LCOH could decline from 4.2 $/kg in 2025 to 2.24 $/kg in 2050 under central assumptions and to roughly 1.4 $/kg under optimistic assumptions. Alkaline electrolysis step cost could reduce by 0.28 $/kg every decade. Hydrogen storage autonomy could rise the LCOH by 7.1 c$ per day of autonomy in 2050.

关键词： Off-grid photovoltaic hydrogen production dual-objective optimization Smoothing control strategy Levelized cost of hydrogen projection up to 2050

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dual-objective optimization of Maximum Rail Potential and Total Energy Consumption in Multitrain Subway Systems

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IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 2021年第4期7卷 3149-3162页

作者： Zhu, Chengqian Du, Guifu Jiang, Xingxing Huang, Weiguo Zhang, Dongliang Fan, Mingdi Zhu, Zhongkui Soochow Univ Sch Rail Transportat Suzhou 215131 Peoples R China China Univ Min & Technol Sch Elect & Power Engn Xuzhou 221116 Jiangsu Peoples R China

The dc traction power system is commonly adopted in multitrain subway systems. During the operation of multiple trains, different power distribution of the system directly affects the energy consumption and power supply safety. It is urgent for multitrain subway systems to optimize the operation parameters to ensure energy-saving and safety. In this article, nondominated sorting genetic algorithm II (NSGA-II) based on kernel density estimation (KDE) is proposed for collaborative optimization of total energy consumption and the maximum rail potential in the system. First, a simulation model for the dynamic operation of multiple trains with a parallel multiconductor traction network is established to realize the dynamic flow calculation with train-network coupling. Second, a dual-objective optimization model of the maximum rail potential and the total energy consumption is formulated to realize the energy-saving and power supply safety of the system. Finally, the NSGA-II algorithm based on KDE is proposed to handle the dual-objective optimization model. According to the dynamic simulations of Guangzhou Metro Line 2, the optimization method is verified. Results show that the total energy consumption is reduced by 22.2%, and the maximum rail potential is dropped by 40.3%, which effectively realizes the energy-saving and power supply safety.

关键词： Rails Safety Energy consumption Acceleration Rectifiers Public transportation Power system dynamics DC traction power system dual-objective optimization energy consumption rail potential

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dual-objective optimization for petroleum molecular reconstruction based on property and composition similarities

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AICHE JOURNAL 2023年第8期69卷 e18108-e18108页

作者： Guan, Dong Cai, Guangqing Zhang, Linzhou China Univ Petr Petr Mol Engn Ctr PMEC State Key Lab Heavy Oil Proc Beijing Peoples R China China Univ Petr Petr Mol Engn Ctr PMEC State Key Lab Heavy Oil Proc Beijing 102249 Peoples R China

Petroleum molecular reconstruction method can be used to calculate molecular composition from limited analytical data, which is the basis of the molecular-level process modeling of petroleum refining. However, due to the problem of multiple solutions, it is difficult to obtain accurate and stable molecular compositional models. Therefore, based on the traditional bulk property constraints, composition similarity was proposed and a new petroleum molecular reconstruction method was proposed. In this article, diesel is taken as the study object, and the method was applied to construct various diesel compositional models. The results showed that the models' bulk properties and molecular fraction distribution were consistent with the experimental data. Finally, the method was applied to construct diesel compositional models of an actual refinery. With the reference oil compositional model as the molecular reference data, the problem of accurate construction of compositional models in a long-term production process without detailed characterization data was solved.

关键词： composition similarity dual-objective optimization molecular management molecular reconstruction petroleum

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dual-objective optimization for energy-saving and fouling mitigation in MBR plants using AI-based influent prediction and an integrated biological-physical model

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JOURNAL OF MEMBRANE SCIENCE 2021年 626卷 119208-119208页

作者： Nam, KiJeon Heo, SungKu Rhee, GaHee Kim, MinJeong Yoo, ChangKyoo Kyung Hee Univ Dept Environm Sci & Engn Integrated Engn Coll Engn 1732 Deogyeong Daero Yongin 17104 Gyeonggi Do South Korea Korea Railrd Res Inst Uiwang South Korea

Optimal operational strategies for wastewater treatment plants have been employed widely to improve the economic and environmental operations. However, inadequate optimization systems have been applied to membrane bioreactor (MBR) plants, resulting in high energy consumption due to uncertain influent associated with complex biological and physical interactions associated with membrane fouling. Here, we develop a dual objective optimization system based on harmony search algorithm via process simulation. A bidirectional gated recurrent unit produced the most accurate predictions of fluctuating variations in hourly influent among competing models. And the predicted influent information was used to suggest operational strategies. The optimization system searched predictive operational strategies, including aeration intensities and permeation cleaning durations, using the integrated biological and physical model. The suggested aeration intensities were then used for scouring air on membrane surfaces when the permeation-cleaning duration was optimized. A pilot MBR plant equipped with the suggested optimization system improved energy efficiency by 4% and mitigated fouling by 39% compared with a manually operated system. dual-objective optimization also demonstrated feasibility and reliability at a full-scale MBR plant by increasing energy efficiency by 12% and decreasing fouling by 26%.

关键词： Membrane bioreactor dual-objective optimization Artificial intelligence Integrated model Fouling

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Vehicle Route Planning:Integrating dual-objective optimization with Reinforcement Learning

Vehicle Route Planning:Integrating Dual-Objective Optimizati...

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2024 世界交通运输大会

作者： Yu Wang Shuang Wu Jing Chen Zihan Wang Minghui Zhang Xinjie Fang Engineering Research Center of Highway Infrastructure Digitalization Ministry of Education of PRCChang'an University

Given the challenges of road congestion,elevated traffic costs,and suboptimal utilization of road resources in contemporary society,reasonable route planning emerges as a crucial approach to address these ***,the majority of vehicle route planning schemes are crafted with the singular objective of attaining a specific goal,such as the shortest route or minimal travel *** the diverse needs of drivers and the dynamic nature of the road environment,this paper proposes a dual-objective optimization Q-learning reinforcement learning algorithm based on distance and ***,the FMM algorithm is employed to match GPS data with the road ***,a linear combination of distance and average driving time is performed to derive the overall optimization ***,vehicle routes are planned using a series of algorithms,namely the shortest distance algorithm,the dual-objective weighted Dijkstra algorithm,the dualobjective weighted Bellman-Ford algorithm,and the dual-objective weighted Q-learning reinforcement learning *** real road network nodes and GPS trajectory data within the secondary ring area of Xi'an,a set of experiments was conducted to assess the efficacy of the four aforementioned *** results of the experiments reveal that the Q-learning algorithm excels in optimizing average driving ***,the adaptability of the algorithm allows for parameter adjustments based on the specific requirements of the driver,facilitating the generation of the most suitable recommended route.

关键词： dual-objective optimization Q-learning Reinforcement learning

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A dual-objective integrated optimization problem for energy conservation in subway systems

A dual-objective integrated optimization problem for energy ...

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IEEE Intelligent Transportation Systems Conference (IEEE-ITSC)

作者： Liu, Hongjie Lv, Jidong Chai, Ming Beijing Jiaotong Univ Sch Elect & Informat Engn Beijing 100044 Peoples R China

ISBN: (纸本)9781538670248

Maximizing regenerative energy utilization is an important way to reduce energy consumption in subway systems. Regenerative energy can be immediately utilized by traction trains, or be stored in energy storage systems and reused later. This paper adopts a combination of both methods, and proposes a dual-objective integrated optimization problem to simultaneously minimize the total net traction energy consumption and the investment of energy storage systems. Both timetable and configuration of energy storage systems are controlled in the optimization problem. A mathematical model of the dual-objective optimization problem is first formulated. Then to solve it, an epsilon-constraint method is designed to transform the dual-objective optimization problem into several single-objective optimization problems, and an improved artificial bee colony algorithm is designed to solve them sequentially. Finally, numerical examples based on the actual data from a subway system in China are performed to show the effectiveness of the proposed method. Experimental results indicate that net traction energy consumption is effectively reduced by applying the proposed method, and it reduces to different degrees when using different size of energy storage systems, with an correspondingly optimized timetable. A set of Pareto optimal solutions is obtained for the experimental subway system, which is helpful for decision makers.

关键词： Regenerative energy timetable energy storage system dual-objective optimization epsilon-constraint method

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